ChatGPT-4 vs. ChatGPT-3.5 at a Glance

Both models share foundational traits, but some features set them apart—the chart below shows how they compare at a high level.

What is ChatGPT-4?

ChatGPT-4, also known as ChatGPT 4 and GPT-4, is a large multimodal model capable of understanding and working with both text and images. It was developed by OpenAI, a leading generative AI company that focuses on scalable content generation for wide-ranging personal and business use cases.

ChatGPT-4 capabilities are available in free and paid ChatGPT plans. Users can also fine-tune and set up various OpenAI APIs with the same functionalities. Compared to ChatGPT-3.5 and other earlier iterations of the tool, ChatGPT-4 has greater content accuracy and creative capabilities. It also has a larger context window (32K or 128K, depending on user choice), multimodal capabilities, and several plugins and collaborative features to make the solution more useful for generative AI enterprise use cases.

Key Features of ChatGPT-4

GPT-4 boasts a remarkable set of capabilities powered by artificial intelligence and natural language processing (NLP) to answer any questions, write code, translate texts, and more. Core ChatGPT-4 features include:

• Multimodal Image Generation: GPT-4 allows users to submit visual inputs and generate relevant visual outputs, especially with ChatGPT paid plans that include a DALL-E tool integration for text-to-image content generation.
• Larger Context Windows: Depending on which ChatGPT-4 plan you select, users can benefit from 32K or 128K context windows, as compared to GPT-3.5’s 8K context window.
• Internet Connectivity: Unlike previous OpenAI GPT models and ChatGPT versions, ChatGPT-4 users (Plus plan and higher) can access the internet for limited browsing capabilities, primarily through plugins.

Pros

• Handles complex and nuanced tasks 
• Stronger commitment to safety and alignment in training and research
• Multimodal, scalable, and more creative outputs 

Cons 

• More expensive than many similar models, especially with API and direct model access
• APIs and fine-tuning may have a steep learning curve

What is ChatGPT-3.5?

ChatGPT-3.5 is an earlier generation of ChatGPT that has since been superseded by ChatGPT-4, a more advanced and powerful model. GPT-4o, OpenAI’s newest flagship model that provides GPT-4-level intelligence and free access, also outperforms ChatGPT-3.5. Although newer ChatGPT models surpass ChatGPT-3.5 capabilities, casual users looking for a simple and free platform still access ChatGPT-3.5. With ChatGPT-3.5, users can benefit from mobile and web access, unlimited AI conversational features, an 8K context window, multifactor authentication, and regular updates as OpenAI advances its generative AI technology.

Key Features of ChatGPT-3.5

Newer models have outperformed ChatGPT-3.5, but casual users still prefer this platform due to its free plan, accessibility, and speed designed for simple queries. Core ChatGPT-3.5 includes:

• Unlimited User Interactions: Though the quality, resolution, and other advanced features may be limited, all ChatGPT-3.5 users can send unlimited messages and receive unlimited interactions; they can also keep track of unlimited conversation history.
• Mobile and Web Access: Users can access ChatGPT-3.5 directly from the OpenAI website or through dedicated iOS and Android applications.
• Mobile Voice Capabilities: With the mobile app version of ChatGPT-3.5, users can take advantage of built-in smartphone microphones and AI voices to have true conversations that requires no typing or reading.

Pros

• Free to use
• Easy access across online and mobile app versions

Cons

• Less power and accuracy than the latest model
• No multimodal capabilities

Best for Pricing: ChatGPT-3.5

ChatGPT-3.5 has an advantage over ChatGPT-4 when it comes to pricing. ChatGPT-3.5 is free to access, while ChatGPT-4 is only available in paid plans and APIs.

GPT-3.5 offers a free plan for individual users, including unlimited access to unlimited messages, chat history, interactions, and availability on mobile and web platforms at no cost. While the free plan has several limitations compared to plans running on GPT-4, users can still benefit from fairly quick response times, an 8K context window, multifactor authentication, regular model quality and speed improvements, and the ability to opt out of personal content being used as part of model training sets.

Outside of the GPT-3.5 access that is available directly through ChatGPT, users can also pay for the turbo, fine-tuning, and older 3.5 models:

• gpt-3.5-turbo-0125: $0.50 per one million input tokens; $1.50 per one million output tokens
• gpt-3.5-turbo-instruct: $1.50 per one million input tokens; $2 per one million output tokens
• gpt-3.5-turbo (fine-tuning model): $8 per one million training tokens; $3 per one million output tokens
• gpt-3.5-turbo-1106: $1 per one million input tokens; $2 per one million output tokens
• gpt-3.5-turbo-0613: $1.50 per one million input tokens; $2 per one million output tokens
• gpt-3.5-turbo-16k-0613: $3 per one million input tokens; $4 per one million output tokens
• gpt-3.5-turbo-0301: $1.50 per one million tokens; $2 per one million output tokens

ChatGPT currently offers a free version that provides access to ChatGPT-4o mini and limited use of ChatGPT-4o. While these new models are built on ChatGPT-4’s architecture, free users have limited access to file uploads, advanced data analysis, web browsing, and image generation. Users, teams, and enterprises can upgrade to paid plans to unlock the full capabilities of ChatGPT-4:

• Plus: $20 per month 
• Pro: $200 per month
• Team: $25 per user, per month, billed annually; $30 per user, per month
• Enterprise: Contact sales for quote

Best for Core Features: ChatGPT-4

ChatGPT-4 is a more advanced tool than ChatGPT-3.5, offering a wider range of features as well as greater accuracy, creativity, nuanced understanding, and safety training.

GPT-3.5 and GPT-4 both use a transformer-based architecture as part of a neural network that handles sequential data. ChatGPT-3.5 is less advanced, has a smaller number of potential parameters included, and only has access to information up to September 2021. Meanwhile, ChatGPT-4’s knowledge cutoff date was until April 2023.

It’s also important to know that ChatGPT plans powered by GPT-4 can access the internet for browsing tasks, handle advanced data analysis, and operate a significantly larger context window. With these features and more, GPT-4 and paid ChatGPT plans are better at handling complex problems and multimodal challenges compared to GPT-3.5.

GPT-3.5 is still a robust model that powers a free version of ChatGPT. This free, more limited version of ChatGPT is quite capable of various user-requested tasks, including language translation, simple coding and troubleshooting, creative problem-solving and storytelling, and straightforward Q&As.

But GPT-4 is “smarter,” can understand and generate AI images, and can process between four and 16 times as many words as its predecessor with greater accuracy. As a newer model with more R&D to back it up, OpenAI is also committed to stronger safety, privacy, and ethical AI measures in its ChatGPT-4 model. In nearly all ways, ChatGPT-4 offers more and higher-quality core features than ChatGPT-3.5.

Best for Ease of Implementation: ChatGPT-3.5

ChatGPT-3.5 barely edges out ChatGPT-4 for ease of use and implementation, due to the fact that it is free and requires no payment plans or installations to get started.

With ChatGPT-3.5, all users need to do is log in or create a free OpenAI account. Users have the option to log in with their email address and their preferred password, or they can log in through existing Google, Microsoft, or Apple accounts.

Once an account is created and opened, users can immediately begin having conversations with ChatGPT and save their conversation history for later access. The login/signup process is similar for the mobile app, which can easily be downloaded from your mobile device’s app store, just as you would with any other mobile app.

Making an account for ChatGPT-4 is also fairly simple, but it comes with the added layer of setting up subscriptions and payment plans. While this shouldn’t be all that difficult, especially for business users who manage other cloud and app subscriptions, it does add a layer of complexity, especially for accounts with multiple users and/or user counts that are frequently changing.

In summary, both of these generative AI apps are fairly easy to implement and use, but ChatGPT-3.5 is slightly easier. Remember, this comparison focuses primarily on the ChatGPT interfaces for each of these tools rather than APIs and fine-tuning model versions. If you choose to install or subscribe to either of these models outside of the ChatGPT subscription framework, bear in mind that both pricing and ease of use will grow more complex, as models are more customizable and pricing is usage-based.

Best for Content Quality: ChatGPT-4

ChatGPT-4 outperforms ChatGPT-3.5 in all content quality measures because it is a more recent and advanced iteration of ChatGPT technology, supported by more robust research and development.

Compared to previous GPT generations from OpenAI, GPT-4 has been trained on more and wider-ranging datasets, has more computation power and parameters, and has a greater context window for user inputs. These features enable ChatGPT-4 to generate more nuanced, creative, accurate, and relevant responses to even the most complex and layered user queries.

Additionally, according to OpenAI’s research and admission, GPT-4 is 82 percent less likely to respond to dangerous or prohibited requests and 40 percent more likely to produce accurate, fact-based responses compared to GPT-3.5. For safety and alignment, GPT-4 takes GPT-3.5’s great strides a step further, incorporating more user feedback into GPT-4 behavioral training as well as stronger training, evaluation, and monitoring classifiers.

As far as plagiarism and QA-specific features are concerned, ChatGPT-3.5 includes an AI text classifier, which is a plagiarism checker. This feature is good at indicating potential cases of plagiarism, though it’s important to fact-check these suggestions as well: OpenAI recommends that once ChatGPT spots possible plagiarism issues and candidates, humans should look at the data and determine the truth.

ChatGPT-4 spots plagiarism examples with more certainty, though it is far from 100 percent. It also does a better job at distinguishing between AI-written and human-written text, as well as in the detection of automated misinformation campaigns that take advantage of AI tools. However, the general limitations of both versions of ChatGPT include a higher likelihood of inaccuracy with texts below 1,000 characters. Plagiarism-checking results also work better with English than other languages.

In general, ChatGPT-4 is a better tool for content quality measurements, especially as this tool has been designed to handle more advanced reasoning tasks with ease.

Best for Enterprise Use Cases: ChatGPT-4

ChatGPT-4 is a better solution for most enterprise use cases when compared to ChatGPT-3.5, especially since multi-user collaboration and security features are available.

ChatGPT-3.5 can work for many solo business use cases, whether it’s drafting digital marketing content, brainstorming a product idea or launch schedule, or outlining a better HR onboarding or interviewing process. Additionally, the free version of ChatGPT can handle some basic coding and QA programming tasks, as long as users are willing and able to supplement ChatGPT’s outputs with their research and knowledge. If your business has simple text-based content generation tasks that require little to no collaboration and no advanced security features, then ChatGPT-3.5 may have everything that you need.

However, most enterprise-level content generation tasks will require ChatGPT-4’s more advanced capabilities. The longer context window, faster response and processing times, and multimodality of this product version open up the generative AI tool to scalable, more diverse, and more complex content generation tasks. Advanced data analysis is also included in each GPT-4-based ChatGPT plan, so users can benefit from a tool that handles their data more expertly.

The Team and Enterprise versions of ChatGPT are the most powerful ChatGPT subscriptions that run on GPT-4, each including collaboration features, larger context windows, and more security and admin capabilities. While ChatGPT-3.5 only includes MFA among its security features, subscribers to these GPT-4 plans can access advanced features like a dedicated workspace, unified billing, GPTs analytics and management, admin consoles and roles, bulk member management, and compliance management features.

A growing number of businesses are developing their own fine-tuned versions of GPT-4 to meet very specific business use cases and goals. These include Duolingo, Stripe, and Morgan Stanley, which are using GPT-4 to deepen conversation quality, combat fraud, improve user experience, and organize complex knowledge base data, respectively. Many other businesses continue to use GPT-3.5 to fine-tune models for custom enterprise use cases, but as competition heats up and GPT-4 continues to prove its more robust capabilities, expect many of these enterprise users to upgrade to GPT-4 in the future.

Who Shouldn’t Use ChatGPT-4 or ChatGPT -3.5?

While both ChatGPT-4 and ChatGPT-3.5 support a wide range of personal and enterprise use cases, there are several instances when a different model or tool would be a better fit.

ChatGPT-4 might not be the ideal solution for users who:

• Require a free AI-powered generation tool
• Need a high-powered developer and coding tool; GPT-4 is best for coding assistance
• Are unwilling or unable to quality-check the model’s outputs
• Need basic AI content generation capabilities for text-only outputs
• Require deep web search integration and functionality (though GPT-4 does have basic Internet capabilities in beta now)

Similarly, despite being free to access, ChatGPT-3.5 might not be the best solution for users who:

• Want a multimodal generative AI tool, particularly for AI image generation
• Need a high-powered developer and coding tool
• Are unwilling or unable to quality-check the model’s outputs
• Want larger context windows, higher resolution, and other advanced generative AI model features
• Need real-time Internet access and up-to-date training data for their queries

3 Best Alternatives to GPT-4 and GPT-3.5

The market is filled with competing chatbots that might better meet your needs than ChatGPT-4 and ChatGPT-3.5. Three of the best alternatives available today are Google Gemini, Anthropic Claude, and Microsoft Copilot.

Google Gemini

Gemini, previously known as Bard, is one of the top ChatGPT competitors on the market today. Similar to ChatGPT, users can input multimodal prompts and receive relevant outputs on both web and mobile interfaces. While many users still prefer ChatGPT, believing it is more accurate, stable, and scalable, a growing number of users are shifting to Gemini, especially due to its greater multimodal integrations (including for free plan users) and its deep integration of Google Search and other web-based extensions.

Users can access Gemini’s 1.5 Flash model for free. Paid subscriptions start at $20 per user, per month, billed annually.

Claude

Claude is a powerful generative AI model and AI chatbot solution from Anthropic AI. It is a favorite of proponents of ethical AI, as Anthropic has committed to ethical development, transparency, high levels of built-in security and compliance, and inoffensive outputs while developing the Claude model. Users can access this model via claude.ai, through paid plans, and through API access. Significantly, Claude also has one of the largest context windows on the market today, at 200K.

Claude offers a free version for individual users, while its paid plan starts at $18 per month, billed annually. For a comprehensive analysis of Claude’s features, read our Claude AI review. You can also read our ChatGPT-4 vs. Claude comparison for an in-depth analysis of the two platforms.

Microsoft Copilot

Microsoft Copilot is a flexible generative AI tool and chatbot that users can access through multiple interfaces. The Bing-powered Copilot labels itself an “everyday AI companion” and works similarly to ChatGPT, though with more multimodality in its free plan version. Copilot assistance and content generation capabilities are also available through multiple Microsoft products, including most Microsoft 365 business applications and GitHub.

You can access a free version of Microsoft Copilot or upgrade to its paid plan for $20 per user per month.

How I Evaluated ChatGPT-4 vs. ChatGPT-3.5

To assess and compare ChatGPT-4 vs. ChatGPT -3.5, I reviewed each tool based on four key criteria: content quality, scalability, accessibility, and affordability.

Content Quality: I prioritized content output quality when reviewing these two tools, as scalable content production is only as good as the quality of content produced. I focused most specifically on output relevance to prompts, human-like tone and quality of outputs, output accuracy, and range of content output types. I also paid close attention to any information OpenAI disclosed about safety and user privacy training each tool received, which could greatly impact content accuracy and the frequency of problems like AI hallucinations.

Scalability: I reviewed each tool’s scalability and focused primarily on context windows, response times, new features available at each pricing tier, advanced data analysis capabilities, team collaboration features, and security and administrative capabilities.

Accessibility: Accessibility and ease of use are key to the success of a generative AI model, especially for less technical users. For this category, I emphasized multiple user interfaces and channel options, mobility, ease of implementation and ongoing use, user-friendly research and training guides, and chat history accessibility.

Affordability: Though affordability is unlikely to be the most important criterion for enterprise users, many small businesses and solopreneurs must prioritize generative AI tools that meet their needs without breaking the bank. For this criteria, I considered whether a free version was available, how quickly costs went up from plan to plan, how much APIs and fine-tuning models cost, and how many features users can access at each pricing tier.

Bottom Line: ChatGPT-4 vs. ChatGPT-3.5

Both GPT-4 and ChatGPT have earned formidable reputations as excellent generative AI tools. There are obvious similarities between them, as ChatGPT-4 is essentially an upgrade to ChatGPT-3.5. GPT-4 is more advanced and beats GPT -3.5 in nearly all criteria, at least as far as performance is concerned. Each tool has its place. ChatGPT-3.5 can be a powerful tool for many individual and business use cases—especially for users on a budget.

When comparing ChatGPT-4 vs. ChatGPT -3.5, the most important initial step you can take is to determine your available budget and nonnegotiable feature requirements. After that, it should be a fairly straightforward decision between the two.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide to the top AI companies.

The post ChatGPT-3.5 vs. 4: What’s the Difference in 2025? appeared first on eWEEK.

Artificial intelligence companies are riding a hyper-accelerated growth curve. Like the crack of a starting gun, the November 2022 launch of ChatGPT awakened the world to the vast potential of AI—particularly generative AI. As more companies invest in machine learning, automation, robotics, and AI-based data analytics solutions, the AI algorithm has quickly become the foundational technology of business.

This list of AI companies chronicles this growth by reflecting the dynamic shifts disrupting the tech industry. It covers the full ecosystem of AI vendors: new generative AI companies, entrenched giants, AI purveyors across verticals, and upstart visionaries. There’s no telling which will most influence AI’s future, but we believe that the players on this list as a whole will profoundly reshape technology and, as a direct result, the arts, retail, and the entirety of culture.

AI Giants

It’s no coincidence that this top AI companies list is composed mostly of cloud providers. Artificial intelligence requires massive storage and compute power at the level provided by the top cloud platforms. These cloud leaders are offering a growing menu of AI solutions to existing clients, giving them an enormous competitive advantage in the battle for AI market share. The cloud leaders represented also have deep pockets, which is key to their success, as AI development is exceptionally expensive.

Microsoft

Enterprise leader in AI

• Market Cap: $3.1 Trillion
• Headquarters: Redmond, WA
• Founded: 1975
• Annual Revenue: $198.3 Billion

As a dominant provider of enterprise solutions and a cloud leader—its Azure Cloud is second only to AWS—Microsoft has invested heavily in AI, with plenty to show for it. For example, it has significantly expanded its relationship with OpenAI, the creator of ChatGPT, leading to the development of intelligent AI copilots and other generative AI technologies that are embedded or otherwise integrated with Microsoft’s products. Leveraging its massive supercomputing platform, its goal is to enable customers to build out AI applications on a global scale. With its existing infrastructure and partnerships, current trajectory, and penchant for innovation, it’s likely that Microsoft will be the leading provider of AI solutions to the enterprise in the long run.

Amazon Web Services (AWS)

Top-tier managed services for cloud and AI

As the top dog in the all-important world of cloud computing, few companies are better positioned than AWS to provide AI services and machine learning to a massive customer base. In true AWS fashion, its profusion of new tools is endless and intensely focused on making AI accessible to enterprise buyers. AWS’s long list of AI services includes quality control, machine learning, chatbots, automated speech recognition, and online fraud detection. It is one of the best providers of innovative AI managed services.

To learn about new direction in generative AI, see the eWeek video: AWS VP Bratin Saha on the Bedrock Generative AI Tools.

Google

Leading generative AI for technical and non-technical audiences

As the most successful search giant of all time, Google’s historic strength is in algorithms, which is the very foundation of AI. Though Google Cloud is perennially a distant third in the cloud market, its platform is a natural conduit to offer AI services to customers. The Gemini ecosystem has proven especially popular and innovative, combining access to generative AI infrastructure, developer tools, and a user-friendly natural language interface. The company is also heavily focused on responsible AI and communicating how it is working toward an ethical AI approach.

IBM

Founder of Watson and watsonx AI solutions

A top hybrid and multicloud vendor, boosted by its acquisition of Red Hat in 2019, IBM’s deep-pocketed global customer base has the resources to invest heavily in AI. IBM has an extensive AI portfolio, highlighted by the Watson platform, with strengths in conversational AI, machine learning, and automation. The company invests deeply in R&D and has a treasure trove of patents; its AI alliance with MIT will also likely fuel unique advances in the future.

Nvidia

Leading provider of GPUs and other AI infrastructure

All roads lead to Nvidia as AI—especially generative AI and larger models—grows ever more important. At the center of Nvidia’s strength is the company’s wicked-fast GPUs, which provide the power and speed for compute-intensive AI applications. Additionally, Nvidia offers a full suite of software solutions, from generative AI to AI training to AI cybersecurity. It also has a network of partnerships with large businesses to develop AI and frequently funds AI startups.

For an in-depth look a how generative AI and advanced hardware are changing security, see the eWeek video: Nvidia CSO David Reber on AI and Cybersecurity.

Meta

Embedded AI assistance in social media apps

Meta—the parent company of Facebook, Instagram, and many other popular platforms—has had a slightly slower start on generative AI than some of the other tech giants, but it has nonetheless blazed through to create some of the most ubiquitous and innovative solutions on the market today. Meta’s Llama 3, for example, is one of the largest and easiest to access LLMs on the market today, as it is open source and available for research and commercial use. The company is also very transparent with its own AI research and resources. Most recently, Meta has developed Meta AI, an intelligent assistant that can operate in the background of Facebook, Messenger, Instagram, and WhatsApp.

Baidu

Chinese innovator in AI and quantum computing

Little known in the U.S., Baidu owns the majority of the internet search market in China. The company’s AI platform, Baidu Brain, processes text and images and builds user profiles. With the most recent generation, Baidu Brain 6.0, quantum computing capabilities have also expanded significantly. It has also launched its own ChatGPT-like tool, a generative AI chatbot called Ernie Bot.

Oracle

Leader in cloud-based AI support 

Oracle’s cloud platform has leapt forward over the past few years—it’s now one of the top cloud vendors—and its cloud strength will be a major conduit for AI services to come. To bulk up its AI credentials, Oracle has partnered with Nvidia to boost enterprise AI adoption. The company stresses its machine learning and automation offerings and also sells a menu of prebuilt models to enable faster AI deployment.

To find out how a cloud leader is facing the challenges of today’s IT sector, see the eWeek video: Oracle Cloud’s Leo Leung on Cloud Challenges and Solutions.

Alibaba

Cloud leader and innovator in APAC region

Alibaba, a Chinese e-commerce giant and leader in Asian cloud computing, split into six divisions, each empowered to raise capital. Of particular note is the Alibaba Cloud Intelligence group, which handles cloud and AI innovations and products. While Alibaba has been greatly hampered by government crackdowns, observers see the Cloud Intelligence group as a major support of AI development. The company is also working to optimize a ChatGPT-like tool.

For more information about today’s leading generative AI software, see our guide: Top 20 Generative AI Tools & Applications.

AI Pioneers

Think of these AI companies as the forward-looking cohort that is inventing and supporting the systems that propel AI forward. It’s a mixed bunch with diverse approaches to AI, some more directly focused on AI tools than others. Note that most of these pioneer companies were founded between 2009 and 2013, long before the ChatGPT hype cycle.

These companies are at the center of a debate about who will have the most control over the future of AI. Will it be these agile and innovative pioneers, or the giant cloud vendors that have the deep infrastructure that AI needs and can sell their AI tools to an already-captive customer base?

OpenAI

Founder of ChatGPT

The world was forever changed when OpenAI debuted ChatGPT in November 2022—a major milestone in the history of artificial intelligence. Founded in 2015 with $1 billion in seed funding, San Francisco-based OpenAI benefits from a cloud partnership with Microsoft, which has invested a rumored $13 billion in OpenAI. Not content to rest on its success, OpenAI has launched GPT-4, a larger multimodal version of its successful LLM foundation model, and continues to innovate in areas like text-to-video generation. The company also offers DALL-E, which creates artistic images from user text prompts.

C3.ai

Industry-focused AI solutions and services

Founded in 2009, C3.ai is part of a new breed of vendors that can be called an “AI vendor”: not a legacy tech company that has shifted into AI but a company created specifically to sell AI solutions to the enterprise. The company offers a long menu of turnkey AI solutions so companies can deploy AI without the complexity of building it themselves. Clients include the U.S. Air Force, which uses AI to predict system failure, and Shell, which uses C3.ai to monitor equipment across its sprawling infrastructure.

For in-depth comparison of C3.ai and a major competitor, see our guide: C3.ai vs. DataRobot: Top Cloud AI Platforms.

H2O.ai

Solutions provider for generative and predictive AI

Founded in 2011, H2O.ai is another company built from the ground up with the mission of providing AI software to the enterprise. H2O focuses on “democratizing AI.” This means that while AI has traditionally been available only to a few, H2O works to make AI practical for companies without major in-house AI expertise. With solutions for AI middleware, AI in-app stores, and AI applications, the company claims thousands of customers for its H2O Cloud.

To learn how computers can “see” the world around them, watch our eWeek video: H2O.ai’s Prashant Natarajan on AI and Computer Vision.

DataRobot

Cloud-agnostic AI and data solutions

Founded in 2012, DataRobot offers an AI Cloud that’s “cloud-agnostic,” so it works with all the cloud leaders (AWS, Azure, and Google, for example). It’s built with a multicloud architecture that offers a single platform accessible to all manner of data professionals. Its value is that it provides data pros with deep AI support to analyze data, which supercharges data analysis and processing. Among its outcomes is faster and more flexible machine learning model creation.

For in-depth comparison of DataRobot and a major competitor, read DataRobot vs. H2O.ai: Top Cloud AI Platforms.

Snowflake

Next-gen data warehouse and AI data cloud vendor

Founded in 2012, Snowflake is a next-gen data warehouse vendor. Artificial intelligence requires oceanic amounts of data, properly prepped, shaped, and processed, and supporting this level of data crunching is one of Snowflake’s strengths. Operating across AWS, Microsoft Azure, and Google Cloud, Snowflake’s AI Data Cloud aims to eliminate data silos for optimized data gathering and processing.

For an expert take on how today’s IT platforms are enabling wider data access, see the eWeek video: Snowflake’s Torsten Grabs on AI and Democratizing Data.

Dataiku

Low-code/no-code AI/ML model development platform

Founded in 2013, Dataiku is a vendor with an AI and machine learning platform that aims to democratize tech by enabling both data professionals and business professionals to create data models. Using shareable dashboards and built-in algorithms, Dataiku users can spin up machine learning or deep learning models; most helpfully, it allows users to create models without writing code.

Altair (RapidMiner)

End-to-end data analytics and AI workflows

Since RapidMiner was acquired by Altair in 2022, the vendor has continued to grow and improve its no-code AI app-building features, which allow non-technical users to create applications without writing software. The company also offers a no-code MLOps solution that uses a containerized approach. As a sign of the times, users can build models using a visual, code-based, or automated approach, depending on their preference.

Domino Data Lab

Unified AI orchestration solution provider

Founded in 2013, Domino Data Lab offers both comprehensive AIOps and MLOps (machine learning operations) solutions through its platform technology. With its enterprise AI platform, users can easily manage their data, software, apps, APIs, and other infrastructural elements in a unified ecosystem. Users have the option to work with hybrid or multicloud orchestration, and they can also choose between a SaaS or self-managed approach. Domino Data Lab has partnered with Nvidia to provide a faster development environment, so expect more innovation from them soon.

To learn how today’s software developers are finding ways to work faster, see the eWeek video: Domino Data Lab’s Jack Parmer on “Code First” Data Science.

Databricks

AI-optimized data lakehouses and infrastructure

Founded in 2013, Databricks offers an enterprise data intelligence platform that supports the flexible data processing needed to create successful AI and ML deployments; think of this data solution as the crucial building block of artificial intelligence. Through its innovative data storage and management technology, Databricks ingests and preps data from myriad sources. Its data management and data governance tools work with all major cloud players. The company is best known for its integration of the data warehouse (where the data is processed) and the data lake (where the data is stored) into a data lakehouse format.

Interested in the relationship between AI and Data? See the eWeek video: Databricks’s Chris D’Agostino on AI and Data Management.

Adobe

AI solutions for graphic designers and creatives

Adobe is a SaaS company that primarily offers marketing and creative tools to its users. The company has begun to enhance all of these products with AI solutions, including Adobe Firefly, a robust generative AI tool and assistant that helps users personalize marketing assets, edit visual assets for better quality, and generally create creative content at scale across different Adobe suite products. In late 2023, Adobe expanded its AI capabilities through its acquisition of Rephrase.ai, a text-to-video studio solution.

Alteryx

Drag-and-drop approach to data and AI modeling

A prime example of a mega theme driving AI, Alteryx’s goal is to make AI models easier to build. The goal is to abstract the complexity and coding involved with deploying artificial intelligence. The platform enables users to connect data sources to automated modeling tools through a drag-and-drop interface, allowing data professionals to create new models more efficiently. Users grab data from data warehouses, cloud applications, and spreadsheets, all in a visualized data environment. Alteryx was founded in 1997.

Learn about the major trend toward enabling wider access to data by watching the eWeek video: Alteryx’s Suresh Vittal on the Democratization of Data Analytics.

Inflection AI

A conversational approach to generative content

Inflection AI labels itself as an AI studio that is looking to create advanced applied AI that can be used for more challenging use casesWhile it has hinted at other projects in the works, its primary product right now is Pi, a conversational AI that is designed to take a personalized approach to casual conversations. Pi can be accessed through pi.ai as well as iOS and Android apps. The company was founded by many former leaders from DeepMind, Google, OpenAI, Microsoft, and Meta, though several of these leaders have since left to work in the new Microsoft AI division of Microsoft. It’s truly up in the air how this change will impact the company and Pi, though they expect to release an API in the near future.

Scale AI

Leading provider of AI for public sector use cases

Scale is an AI company that covers a lot of ground with its products and solutions, giving users the tools to build, scale, and customize AI models—including generative AI models—for various use cases. The Scale Data Engine simplifies the process of collecting, preparing, and testing data before AI model development and deployment, while the Scale Generative AI Platform and Scale custom LLMs give users the ability to fine-tune generative AI to their specifications. Scale is also a leading provider of AI solutions for federal, defense, and public sector use cases in the government.

Arista Networks

Leader in AI networking solutions

Arista Networks is a longstanding cloud computing and networking company that has quickly advanced its infrastructure and tooling to accommodate high-volume and high-frequency AI traffic. More specifically, the company has worked on its GPU and storage connections and sophisticated network operating software. Tools like the Arista Networks 7800 AI Spine and the Arista Extensible Operating System (EOS) are leading the way when it comes to giving users the self-service capabilities to manage AI traffic and network performance.

Cloudera

Hybrid, cloud-agnostic data platform

Having merged with former competitor Hortonworks, Cloudera now offers the Cloudera Data Platform and the Cloudera Machine Learning solution to help data pros collaborate in a unified platform that supports AI development. The ML solutions are specifically designed to perform data prep and predictive reporting. As an example of emerging trends, Cloudera provides “portable cloud-native data analytics.” Cloudera was founded in 2008.

For an inside view of where data leader Cloudera is headed, see the eWeek video: Cloudera’s Ram Venkatesh on the Cloudera Roadmap.

Accubits

Leader in blockchain, Web3, and metaverse technologies

Accubits is a blockchain, Web3, and metaverse tech solutions provider that has expanded its services and projects into artificial intelligence as well. The company primarily works to support other companies in their digital transformation efforts, offering everything from technology consulting to hands-on product and AI development. The company’s main AI services include support for AI product and model development, consulting for generative AI projects, solution architecting, and automation solutions.

AI Visionaries

If the AI pioneers are a mixed bag, this group of AI visionaries is heading off in an even wider array of directions. These AI startups are closer to the edge, building a new vision even as they imagine it—they’re inventing the generative AI landscape in real time, in many cases. More than any technology before, there’s no roadmap for the growth of AI, yet these generative AI startups are proceeding at full speed.

Anthropic

Generative AI leader committed to constitutional AI

Founded by two former senior members of OpenAI, Anthropic’s generative AI chatbot, Claude 3, provides detailed written answers to user questions; with this most recent generation, certain aspects of multimodality have been introduced while other components of the platform have been improved. In essence, it’s another tool that operates like ChatGPT, but with a twist: Anthropic publicly proclaims its focus on Constitutional AI, a methodology it has developed for consistent safety, transparency, and ethicality in its models.

Glean

Leader in generative enterprise search technology

Considered one of the unicorns of the emerging generative AI scene, Glean provides AI-powered search that primarily focuses on workplace and enterprise knowledge bases. With its Workplace Search, Assistant, Knowledge Management, Work Hub, and Connectors features, business leaders can set up a self-service learning and resource management tool for employees to find important documentation and information across business applications and corporate initiatives.

Adept

Commitment to general intelligence AI assistants

Currently, generative AI platforms like DALL-E and GPT-4 create images or text in response to user text prompts. Adept is building the next step: It’s creating a full-fledged digital assistant—“an AI teammate for everyone”—that will execute a series of complex commands based on text prompts, even for developer tasks. As an example of how Adept works, if you type in the prompt, “convert this client into a sales opportunity,” the Adept digital assistant performs various actions to complete the sale. Ideally, Adept’s platform will be able to use any API, software app, or website just as a human would. Though Adept is a fledgling—founded in 2022—it has attracted massive funding.

Synthesia

Generative AI video platform with AI avatars

Is the person in this video real or virtual? Synthesia uses AI to create video avatars who speak and present as if they’re human. The AI company offers more than 150 stock AI avatars to allow users to create a virtual talking head using text prompts. To add realism, the avatars can be customized with facial gestures like raised eyebrows, head nods, and local languages and dialects.

To learn about the future of video and AI, see the eWeek video: Synthesia CEO Victor Riparbelli on AI and Video Avatars.

Ironclad

AI for contract lifecycle management

Ironclad is a contract lifecycle management vendor that uses AI to manage contract data, contract creation, analytics, and more. Its contract review process is thorough and customizable, offering users AI-driven suggestions for how to improve existing contracts based on both best practices and the AI playbooks users upload themselves; the platform also includes a comprehensive AI-powered editor and a repository that makes contracts editable in a Word-Document-like format. More recently, the vendor has come out with Ironclad Contract AI, an AI assistant that supports users with chat-driven solutions for additional contract tasks and queries.

Cohere

Leading models with accessible AI playground

Founded in 2019 by an elite group of AI experts, most of whom were former researchers at Google Brain, Cohere’s goal is to enable more natural communication between humans and machines for generative AI, search, discovery, and retrieval tasks. The startup builds large language models for enterprise customers, accessible via an API, which is clearly a lucrative new niche. Funding has gushed in. Its top models are the Command, Rerank, and Embed models.

Abacus.ai

AI technology used to build AI technology

Founded in 2019, Abacus creates pipelines between data sources—such as Google Cloud, Azure, and AWS—and then allows users to custom-build and monitor machine learning models. A unique aspect of this platform is that it also enables AI to build AI agents and systems rather than requiring hands-on human intervention. Abacus’s prebuilt AI technology can be used to build AI solutions like LLMs and can provide additional information about these models to improve explainability.

Gong

AI-powered revenue, service, and sales intelligence

Gong is a fast-growing provider of customer service, sales, and marketing solutions that focus on revenue and engagement intelligence and analytics. AI is infused throughout the platform and is used to provide contextual information and recommendations for customer interactions, as well as coaching for internal team members. The vendor also offers its smart trackers tool, which gives users the ability to train Gong’s AI to more granularly detect certain types of customer interactions and red-flag behaviors.

Runway

Text-to-video and video-to-video content generation platform

The three founders of Runway met in art school, where they were immersed in digital design software. Their generative AI platform, which is browser-based and requires no plugins, creates images and videos from text prompts. Think of it as a filmmaker’s dream: If you can imagine it, the Runway platform will help you create it. Runway already has a major production credit for the film Everything Everywhere All At Once, which won Best Picture in the 2023 Academy Awards. Its most recent models and developments have earned high praise for their realism and controllability.

Openstream.ai

Highly configurable conversational AI

Openstream.ai is a player in the rapidly growing conversational AI market. Openstream.ai’s Eva platform leverages sophisticated knowledge graphs that use both structured and unstructured data, enabling it to work across multiple channels, including social media platforms. Openstream.ai uses this AI architecture to power natural language understanding (NLU), which involves impressive levels of reading comprehension. The vendor also develops copilots, help des and contact center agents, and other customer service solutions with its conversational AI approach.

Samsara

AI-powered driving assistance and analytics

Samsara is an IoT company that has brought forth several innovative technologies over the years, but more recently, it has expanded into AI for driver and road safety. The company’s built-in AI and advanced edge computing for vehicles give drivers and/or fleet managers real-time insights into road conditions and driving performance, as well as coaching workflows and in-cab driver assistance. AI dash cams are built into vehicles and designed to send footage directly to the cloud, so fleet managers and business owners can review driver and vehicle issues in a timely manner.

Moveworks

AI for IT and support workflows

Moveworks is an AI company that focuses on creating generative AI and automated solutions for business operations and employee and IT support. The platform is filled with AI-powered features, including AI workflows, analytics, knowledge management, and ticket and task automation. The company is also leading the way with copilot assistive AI technology, giving users access to tools like MoveLM, an LLM that’s dedicated to employee support queries and tasks.

Synthesis AI

AI for multiple computer vision scenarios

Synthesis AI is a generative AI and synthetic data company that focuses on creating data and models for computer vision use cases. The platform can be used for a variety of use cases spanning across industries, including AR/VR/XR, virtual try-on, teleconferencing, driver and pedestrian monitoring, and security. It can also be used in biometrics and security, specifically for ID verification and threat detection.

Insitro

Multidisciplinary, AI-powered approach to drug discovery

Founded by a former professor of machine learning at Stanford, Insitro’s goal is to improve the drug discovery process using AI to analyze patterns in human biology. Drug discovery is enormously expensive, and it’s typically met with low success rates, so AI’s assistance is greatly needed. Driving this development is the company’s mixed team of experts, including data scientists, bioengineers, and drug researchers.

Eightfold AI

AI solution for recruiters and talent management

Eightfold AI is a vendor that uses AI-powered technology to make recruitment, onboarding, retention, and other organizational talent management tasks easier to manage at scale. Users can work with the vendor’s all-encompassing Talent Intelligence Platform, which includes features not only for talent acquisition and talent management but also for resource management. Its automations and smart analytics help users to comb through larger quantities of applicants at a quicker pace while ensuring they identify top talent and new talent pipelines with minimal bias.

InVideo

Customizable AI videos for social media

InVideo is an AI video company that focuses on automating script, scene, voiceover, and overall video production. The platform is frequently used for digital marketing and content marketing projects, allowing users to transform blogs and other text prompts into YouTube, talking avatar, Instagram, and other types of engaging video content. Users can customize the content the platform generates by inputting target audience, platform, and other customization instructions.

FarmWise

AI-powered farming technology innovator

Forget using chemicals to kill weeds in agricultural fields: FarmWise’s weeding robot uses AI and computer vision to yank out weeds without the herbicide. The FarmWise machine resembles a tractor with many arms and uses what the company calls its Intelligent Plant Scanner, a tool that is capable of sub-inch weeding accuracy.

Generative AI Companies

Generative AI is a type of artificial intelligence that can generate content based on user text prompts. The benefits of generative AI are remarkable: finished essays, interesting graphics, complex software code, and more. However, its shortcomings and issues are also significant: Generative AI can lead to cybersecurity concerns or “hallucinate,” meaning it creates false or even defamatory information. Despite these challenges, businesses are flocking to the new technology and generative AI startups are launching daily.

Meanwhile, a highly charged debate is roiling within the generative AI sector. These AI platforms are trained on a massive store of existing material, including the work of artists and writers—but what are the copyright issues? Who owns the output of generative AI applications? These are thorny ethical issues with no clear answer at this point, though more may come as AI regulations continue to pass into law.

Tabnine

Generative AI coding assistant

Tabnine is an AI company that focuses on providing AI assistance for coding and product development. The tool is designed to automate and complete code wherever possible, provide coding suggestions, and do all of this work while also ensuring that all code and data remains secure and compliant. The tool emphasizes AI ethics as well, ensuring users know that it has only been trained on open-source data repositories with permission.

Midjourney

AI-powered image generation and editing solutions

A generative AI service that creates images from natural language text prompts, Midjourney is one of the most popular generative AI platforms. Founded in 2022, it has already been used to generate surprisingly high-profile art: For example, the English publication The Economist has used Midjourney to create its cover image, and a Midjourney image scored top honors in a digital art contest hosted by the Colorado State Fair.

Infinity AI

Synthetic-data-as-a-service provider

Infinity AI speeds up the process of building digital models by employing AI to create and shape synthetic data (synthetic data is computer-generated data churned out to fill in a model). In essence, Infinity AI uses AI to offer synthetic data-as-a-service, which is a niche sector that will grow exceptionally quickly in the years ahead.

Notion

Embedded AI assistance in project management platform

Notion is a project management platform that has pioneered AI assistance tools for project management professionals. Its latest collection of features, Notion AI, is available directly inside of Notion for users who want to optimize and automate their project workflows. Notion’s AI assistance can be used for task automation, note and doc summaries, action item generation, and content editing and drafting.

Podcast.ai (PlayHT)

AI for automated podcast creation

Who needs humans? Podcast.ai, powered and run by PlayHT, an AI voice and text-to-speech company, is a podcast series that is created by generative AI on an ongoing basis. Each episode is produced using realistic voice models, and the text is culled from archival material about that guest. Impressively, Podcast.ai released a Steve Jobs “appearance” by feeding the system his biography and reams of related material; the real-life Joe Rogan was able to interview “Steve Jobs” with this development.

Hugging Face

Extensive AI modeling community and resource library

Originally the developer of a chatbot aimed at the teen crowd, Hugging Face has evolved into a repository for prebuilt machine learning and artificial intelligence models across vendors and use cases. Now a significant player in the generative AI sector, thousands of companies use Hugging Face’s platform to generate AI-based applications. The company’s motto is “The AI community building the future.”

Stability AI

Multimodal open AI model developer

Stability AI is a generative AI company that supports Stable Diffusion, an AI model in multiple generations that generates images in response to user text prompts. Since its initial venture into AI image generation, the company has also expanded into video, audio, 3D, and language models. The company also offers specialized applications: Stable Assistant, a chatbot for text-to-image generation; Stable Artisan, a generative AI bot on Discord for multimodal content generation; and Stable Audio, a music and audio generation solution.

MOSTLY AI

Synthetic data generation for finance sector

Focusing on synthetic data generation, MOSTLY AI touts that the synthetic data it creates with generative AI appears as authentic as actual consumer data. The advantage is that this data doesn’t contain the original private data, so it’s compliant with privacy and data governance standards. The company works across a range of industries, including banking and insurance.

Syntho

Synthetic data generation for digital twins

Syntho’s Syntho Engine uses generative AI to create synthetic data, offering a self-service platform that also supports smart de-identification and test data management use cases. The company creates data to build digital twins that respect privacy and GDPR regulations. Its goal is to “enable the open data economy,” in which data can be shared more widely while ensuring sensitive consumer data is protected.

Jasper

Generative AI platform for marketing content creation

Similar to ChatGPT, though with a marketing focus, Jasper uses generative AI to churn out text and images to assist companies with brand-building content creation. The AI solution learns to create in the company’s “voice,” no matter how mild or spiky, for brand consistency. The company also claims to incorporate recent news and information for a current focus on any market sector.

Biomatter

Generative AI for new protein creation

Biomatter leverages generative AI to create synthetic biologic materials, specifically new proteins “for health and sustainable manufacturing applications.” This technology for creating synthetic proteins means new enzymes can be created with completely novel properties and use cases. Clearly, this is just one of many examples of how generative AI will play a crucial role in the future of medicine.

You.com

User-friendly, customizable search solution

Should Google be threatened in its internet search business? If so, the generative AI platform You.com—“the AI search engine you control”—could be part of the competition. Type a query into You.com, and the ChatGPT-style website will create content based on your request. Most recently, a mobile version of the tool was released to the general public.

Osmo

AI-powered sense of smell for computers

Computers, it seems, will soon have a sense of smell. Osmo is digitizing and analyzing scents with the goal of improving healthcare and consumer products like shampoo and insect repellent. The company is creating a vast “map” of scents, called a Principal Odor Map. There are said to be billions of molecules that carry a scent, but only about 100 million of them are known. Osmo utilizes Google Cloud’s AI platform for its generative AI work.

AI Enterprise Majors

A popular saying has emerged among IT experts: “Every company is a tech company.” Using technology is now so central to being competitive that it’s a core focus for every company, regardless of sector.

Now this saying has a companion: “Every tech company is an AI company.” This means that major enterprise tech vendors that have long sold legacy hardware and software are now shifting to artificial intelligence. These big vendors are using their deep pockets and expertise to create AI solutions or acquire AI companies.

In fact, these enterprise majors started investing in AI long before chatbots like ChatGPT burst onto the scene. So while their tools don’t get the buzz of DALL-E, they do enable staid legacy infrastructures to evolve into responsive, automated, AI-driven platforms.

Salesforce

Einstein AI for CRM users

Not long after OpenAI debuted ChatGPT, Salesforce followed up with Einstein GPT, which it calls “the world’s first generative AI platform for CRM.” Powered by OpenAI, the solution creates personalized content across every Salesforce cloud. For instance, it uses generative AI with Slack to offer conversation summaries and writing help, but it also has AI assistance and copilot-like functionalities that are specific to service, sales, marketing, and e-commerce use cases.

To learn about the future of conversational AI, see the eWeek video: Salesforce Chief Scientist Silvio Savarese on Conversational AI.

BMC Software

Automated AI-driven service management (AISM)

Among its other AI-enhanced offerings, BMC’s Helix solution uses AI and ML-based intelligent automation as part of an IT services and operation management platform. The company also provides AIOps solutions (AI for IT operations), a sector that is evolving toward AI for overall business support. The company’s larger focus—one that relies heavily on AI—is the autonomous digital enterprise. However, the Helix platform itself focuses primarily on using AI for better service management workflows.

Learn how a major industry expert sees the DataOps sector by watching the eWeek video: BMC CEO Ayman Sayed on DataOps and the Autonomous Digital Enterprise.

HPE

Rapid AI deployment and enterprise scalability provider

HPE’s Greenlake is an IT-as-a-service solution with a hybrid cloud focus. Part of this on-demand platform is a GPU offering that enables the rapid deployment of AI and machine learning tools. HPE focuses on providing AI geared for various verticals, from healthcare to financial services to manufacturing. Significantly, HPE and Nvidia recently announced a close partnership in which they will co-deliver several new enterprise-focused AI solutions. HPE has also recently released HPE Private Cloud AI.

To find out how today’s IT leaders are navigating rapid change, see the eWeek video: HPE Greenlake SVP Keith White on Change in the IT Sector.

Dell Technologies

Services and infrastructure provider for AI technology

Dell’s APEX solution, which includes multicloud management and a SaaS-based IT services panel, enables companies to build AI-based tools ranging from fraud detection to natural language processing to recommendation engines. Through APEX, customers can access generative AI solutions and AIOps solutions for multicloud management. The company also stresses the AI support provided by its hardware, like its PowerEdge servers and PowerScale Storage.

For an in-depth view of how multicloud is evolving, see the eWeek video: Dell APEX’s Chad Dunn on Handling Multicloud Challenges.

SAP

Enterprise-ready, compliant AI applications 

The ultimate legacy software player, known for its strength in ERP, SAP has clearly moved into the AI era. Its menu of enterprise AI solutions ranges from an AI chatbot to a platform that helps companies incorporate AI into enterprise applications. For its offering of pre-trained AI models, SAP stresses compliance and transparency, which is particularly important for large enterprise clients.

For insight on how companies are sharing analytics more widely, see the eWeek video: SAP’s Irfan Khan on ‘Analytics Everywhere’.

ServiceNow

AI-powered IT service management solutions

An enterprise leader in IT service management (ITSM), the ServiceNow AI offerings include a predictive analytics platform that supports AI tool delivery without data science experience. This is an example of the “democratization of tech,” in which the levers of tool creation are now open to non-tech staff. ServiceNow also provides natural language processing tools, ML models, and AI-powered search and automation.

For an expert view on how companies are using the cloud, see the eWeek video: ServiceNow’s Matt Schvimmer on Accelerating Cloud Migration.

Broadcom

Enterprise security innovator with growing AI footprint

Broadcom has a unique profile in the enterprise IT industry: The company supplies both semiconductors and enterprise infrastructure software; it serves markets from the data center to wireless; it even makes a play in the multicloud sector. In keeping with this broad approach, Broadcom fuels the AI market on multiple levels, notably in its generative AI business, which the company announced in March 2023 is poised to quadruple. More recently in 2024, Broadcom has become an investor favorite among AI stocks to watch and buy.

Multicloud is highly functional but quite challenging; for tips on making it work, see the eWeek video: Broadcom’s Ganesh Janakiraman on Multicloud Challenges.

SAS

AI with strong analytics and BI components

A leader in data analytics and business intelligence, SAS’s AI menu extends from machine learning to computer vision to NLP to forecasting. Notable tools include data mining and predictive analytics with embedded AI, which boosts analytics flexibility and scope and allows an analytics program to “learn” and become more responsive over time.

To guidance on how data can guide decision making, see the eWeek video: SAS’s Katy Salamati on Data and Intelligent Decisioning.

Rockwell Automation

Democratized AI industrial automation

Rockwell serves the rapidly expanding market for large-scale industrial automation, including factories and other major production facilities. It has a particular strength in providing automation for edge computing deployments. In keeping with a powerful trend sweeping the AI and automation sector, Rockwell’s FactoryTalk Analytics LogixAI solution enables non-technical staff to access machine learning tools.

Informatica

Unified metadata intelligence through CLAIRE

Founded in 1993 to serve the nascent ETL (extract, transform, and load) big data market for enterprise customers, Informatica’s current strategy involves using AI to improve data analytics and data mining for competitive value. The company’s CLAIRE AI Engine uses repositories of metadata to fuel its AI and ML development, making it possible to automate tasks at a massive scale.

Infosys

Leader in intelligent, AI-powered automation and RPA

Infosys touts its AI and Automation Services teams as an enterprise-ready solution to provide AI and automation consulting, create bespoke AI platforms, and offer prebuilt cognitive modeling solutions. These solutions include robotic process automation (RPA) tools and AI chatbot models. The company is considered a leader in intelligent automation.

The tech market is facing plenty of challenges; for an expert view, see th eWeek video: Infosys Consulting CEO Andrew Duncan on Tech Headwinds.

AI Robotic Process Automation Companies

The fields of robotics and AI automation existed long before AI became a viable business solution. However, early uses of robotics—notably in auto factories—were merely devices programmed to perform the same task again and again. The more recently developed field of robotic process automation (RPA) makes full use of AI.

RPA vendors develop AI-based software that learns and automatically performs routine office productivity tasks. For instance, an office manager who has to gather files for a weekly report can set up an RPA automation to do that routine task so they can focus on higher-value work.

While many large companies offer RPA as part of their overall portfolio—notably SAP, ServiceNow, and IBM—the vendors in this category specialize in creating intelligent automation and RPA solutions to boost productivity.

UiPath

Leader in the RPA market

Generally acknowledged as the leader in the RPA market, UiPath offers a broad suite of business automation tools across API integration, intelligent text processing, and low-code app development. The company’s Marketplace platform offers an extensive menu of prebuilt automations, from “extract data from a document” to automations built for Microsoft Office 365.

Nuro

AI-powered autonomous vehicles

Nuro is a robotics-focused company that uses AI, advanced algorithms, and other modern technology to power autonomous, driverless vehicles for both recreational and business use cases. The Nuro Driver technology is trained with advanced machine learning models and is frequently quality-tested and improved with rules-based checks and a backup parallel autonomy stack. The company partners with some major retailers and transport companies, including Walmart, FedEx, Kroger, and Uber Eats.

Automation Anywhere

Democratized approach to enterprise RPA

As a player in the all-important cloud native ecosystem, Automation Anywhere offers its Automation Co-Pilot for Business Users to democratize automation. It does this by enabling non-technical staffers to create workflow automations. In 2021, the company acquired process intelligence vendor FortressIQ to expand its tool sets, which should benefit Automation Anywhere as the RPA market evolves toward more sophisticated automation.

Learn about the rapid evolution in the automation sector by watching the eWeek video: Automation Anywhere CEO Mihir Shukla on Intelligent Automation.

Anduril

Autonomous vehicles for threat defense

Anduril is a leading U.S. defense technology company that creates autonomous AI solutions and other autonomous systems that are primarily powered by Lattice. The tools offered by Anduril can be used to monitor and mitigate drone and aircraft threats as well as threats at sea and on land. Its most impressive autonomous systems include underwater vehicles and air vehicles for managed threat defense.

SS&C Blue Prism

RPA for ML decisioning and process orchestration

Acquired by financial services software vendor SS&C in 2022, Blue Prism appears to have enlarged its strategy from RPA to overall business automation. This is very much in keeping with the industry shift toward more all-encompassing automation: As AI gets smarter, RPA systems are better able to keep up with these innovations and provide true value in automation. Included in the Blue Prism offering are tools that perform ML decisioning and process orchestration.

EdgeVerve

AI and RPA-powered digital transformation across industries

EdgeVerve serves its enterprise clients a growing menu of pre-fabricated automations to speed up workflows in the most important and commonly needed business areas. Products include Finacle Treasury for banking and TradeEdge for supply chain management. Like the rest of the RPA sector, EdgeVerve is evolving its automation capabilities to support digital transformation; in essence, we’re heading toward a world where the office runs itself. Infosys acquired EdgeVerve in 2014, though the company still operates mostly as an independent arm.

Shield AI

AI pilot technology innovator

Shield AI is an innovative AI startup that has quickly gained notoriety and capital for its AI pilot technology. Hivemind is an AI pilot that can fly aircraft in both commercial and battle settings, giving users greater insights into their locations and travel paths as well as what’s happening with other pilots and aircraft in their fleet. At this point, Shield AI’s technology is powering several of the vendor’s own intelligent aircraft, including jets, V-BAT teams, and Nova 2.

WorkFusion

RPA for digital staffer development

RPA software platforms frequently work to create “digital workers,” otherwise known as AI-powered software robots. WorkFusion builds on this basic truth with a platform that includes six digital staffer personas. Each category of virtual worker is geared for the most common and/or important automation scenario. WorkFusion has a strong presence in the financial sector.

NICE

RPA for intelligent call center and customer interactions

A strong contender in the call center market, NICE’s RPA solutions are geared toward an array of customer-facing support functions. Significantly, its tool set includes speech and sentiment analysis, which is critical to the retail environment because it can effectively understand the emotions of callers. This type of sentiment analysis is a particularly hot area in the AI market.

Pega

RPA that predicts customer activity proactively

As businesses seek to grow toward a more fully automated environment, Pegas’ RPA architecture has kept pace, adopting a strategy that uses real-time data to guide automated customer interactions. The company touts its ability to read customer intentions, from potential purchases to imminent cancellations, before a customer acts. Overall, the company’s strategy is geared toward greater scalability to support increasingly all-encompassing automation.

For a complete overview of the RPA landscape, see our guide: Robotic Process Automation Vendors.

Conversational AI Companies

Some people don’t want to just click on software; they want to talk with it, and they want much easier and more natural ways to control software. Software equipped with conversational AI capabilities allows just this, as it understands and mimics human speech.

Conversational AI is powered by natural language processing, a subsector of AI focused on translating the idiosyncrasies of human speech into computer commands. There are numerous advantages to this, but here’s a big one: Conversational AI enables non-technical staff to use AI. No need for programmers or experts, everyone is invited.

Gridspace

Generative AI for customer-focused virtual agents

Gridspace is a conversational AI solution that works for different businesses, giving users access to virtual AI agents, advanced analytics, and AI coaching for better conversational outcomes with customer service reps. Virtual agents can be customized to handle quality assurance, revenue management, lead generation, and self-service customer relationship management requirements. The company consists of a multidisciplinary team of engineers, designers, and experts from SRI Speech Labs, where Siri was developed.

Kore.ai

DIY AI chatbots and virtual assistants

Considered a top player in conversational AI, Kore.ai’s no-code tool set allows non-technical staff to create versatile and robust virtual assistants. This “build it yourself” ethos is a dominant theme in the AI chatbot sector. The company is also known for its extensive NLP solutions.

Cognigy

AI-powered conversation agent coaching and optimization

A core offering of conversational AI vendors is tools that improve the performance of call center agents (or other voice-based customer reps). To serve this market, Cognigy offers Cognigy Agent Assist. The company also offers analytics tools and a low-code platform to enable users to create new bot assistants as needed for their situation.

Amelia

Conversational AI and knowledge bases powered by NLU

Amelia’s intelligent agents leverage advanced NLU capabilities—essentially the leading edge of AI chatbot technology. NLU technology enables a virtual agent to use sentiment analysis, which helps reps monitor the emotions of callers. From its initial start in conversational AI, Amelia has since expanded into AIOps and Amelia Answers, an AI-powered enterprise search solution.

OneReach.ai

Creator of Intelligent Digital Workers

OneReach.ai is following a leading trend in the conversational AI market, as the company evolves its offerings from a narrow call center focus toward an enterprise-wide “AI-based virtual staff member.” The result of this trend is that the conversational AI sector is merging with the RPA sector (see above) as conversational AI companies produce full-fledged digital team members. OneReach.ai continues to see success with its comprehensive conversational agents, which it calls “Intelligent Digital Workers.”

Avaamo

Generative AI agents for patient experience and CX

With a background in healthcare-focused conversational AI, Avaamo is extending its reach across various industry sectors, working to create solutions that address customer, employee, patience, and contact center experience. Its agents have also evolved to become true copilots, which assist users through the full lifecycle of their brand conversations.

Yellow.ai

Accessible prebuilt and third-party conversational models

With an intuitive user interface, Yellow.ai’s product offering includes user-friendly prefabricated models to deploy conversational AI agents; this approach to models is quite strategic, as ease of use is a top priority in the conversational AI market. To help integrate third-party functionality, Yellow.ai has built a marketplace where customers can select third-party tools for specific tasks.

boost.ai

Conversational AI company with hybrid NLU tech

boost.ai offers a full menu of advanced chatbot orchestration tools to speed deployment. To help call center reps boost performance with customer calls, boost.ai provides agents with a large repository of support data. The company claims its Hybrid NLU technology improves the quality of its virtual agents.

Healthcare AI Companies

AI healthcare companies are incentivized by two key advantages provided by AI and generative AI. First, artificial intelligence greatly expands the capabilities of medical professionals—and better tools are literally a matter of life and death. Second, AI is adept at streamlining bureaucracy, a huge part of the healthcare sector, which saves significant time and money. Look for healthcare to be a non-flashy but very powerful driver of AI’s progress in the future.

PathAI

Pathology-focused AI company

PathAI is one of the most advanced pathology-focused AI companies today, giving patients, laboratories, and pharmaceutical companies alike access to the AI-powered insights and solutions they need. The company offers accessible AI algorithms for optimized clinical trials, particularly for oncology, as well as AI-powered companion diagnostics, pre-screening predictions, spatial analyses, and translational research. The company’s algorithms and products specifically support biomarker quantification for various cancers, disease severity assessments, quality control, tumor cellularity quantification, and molecular prediction.

Viz.ai

AI platform for patient care coordination

Viz.ai offers AI-powered platforms and applications for care coordination, ensuring patient care is handled more holistically by all of their healthcare providers. The Viz.ai One platform is specifically designed to work in different areas of healthcare, including neurology, cardiovascular, vascular, trauma, and radiology. With this platform, healthcare providers quickly receive insights, clear images, alerts, and communications from other relevant providers, making it so they can more quickly and accurately diagnose their patients. Viz.ai is available in both the U.S. and the EU.

Paige AI

AI imaging technology for cancer diagnostics

Paige AI is a generative AI company in the healthcare sector that focuses on pathology, specifically cancer diagnostics. Its detailed imaging technology, AI-driven workflows and recommendations, and other smart features assist healthcare professionals in breast and prostate cancer diagnosis as well as in optimizing hospital and lab operations.

Insilico Medicine

AI pharmaceutical product development and design

Insilico Medicine is a research and development company that uses artificial intelligence for smarter biology and chemistry research and pharmaceutical analytics. Its PHARMA.AI suite includes PandaOmics, a tool for multi-omics novel target discovery and deep biology analysis; Chemistry42, an ML-powered tool for drug design and automated novel molecule creation; and InClinico, a tool that can both design and predict the success rate of a clinical trial.

Medtronic

AI-powered polyp detection and diagnosis

A fascinating fact about Nvidia: if you dig deep into the AI landscape, you’ll see Nvidia again and again. A good example of this is with Medtronic, which is a well-known medical device maker that operates the Genius AI solution, which enhances the detection of polyps in colonoscopies. The company has partnered with Nvidia to use AI to create a range of next-gen tools for diagnosis and treatment.

Enlitic

AI-powered medical data management

Enlitic’s Curie platform uses artificial intelligence to improve data management in the service of better healthcare. The goal is to make data more accurate, useful, and uniform to enable doctors and other healthcare professionals to make better patient care decisions. The platform also supports data anonymization, which is important for patient privacy and compliance with HIPAA and other healthcare privacy regulations.

Deepcell

AI for cell examination and classification

Deepcell is a biotech startup—spun out of Stanford University in 2017—that leverages AI to examine and classify cells. By identifying viable cells based on morphology (the study of shapes and arrangement of parts), Deepcell technology can more accurately perform diagnostic testing.

Activ Surgical

AI-powered surgical insights

Activ Surgical is an AI healthcare company that uses AI to provide real-time surgical insights and recommendations during surgical operations. The ActivSight product, powered by the ActivEdge platform, is designed to not only give surgeons easy-to-view real-time data but also to make it possible for them to switch between dye-free and dyed visualizations, depending on their needs. Important for healthcare workers, the solution is MIS-system compatible.

Tempus Radiology

AI insights for radiology images

To enhance medical imaging, Arterys—now merged with Tempus Radiology—accesses cloud-based GPU processors, which it uses to support a deep learning application that examines and assesses heart ventricles. This AI-based automated measurement of ventricles allows healthcare professionals to make far more informed decisions. With its merger with Tempus, its focus has expanded to look at radiology images in different formats.

Etcembly

ML-driven immunology and immunotherapy engineering

Etcembly is an immunotherapy company that focuses on designing new TCR therapies. Its therapies are optimized through a deep ML library of immunology expertise and computer-assisted immunotherapy engineering. The platform is designed to learn directly from the interactions of T-cells so appropriate TCR treatments can be identified and developed.

Corti

AI call center solution for healthcare and telemedicine

There are numerous companies using AI to provide call center support, but Corti’s niche is the healthcare sector. To provide a virtual voice assistant geared for the healthcare sector, the company’s solution has been trained with countless hours of conversations between healthcare workers. Among other tasks, the solution can support QA on calls to telemedicine centers.

Butterfly Network

Various AI health assessment and ultrasound technologies

A medical imaging vendor, Butterfly Network uses AI in myriad ways. In 2022, Butterfly Network debuted FDA-cleared AI software to support the use of ultrasound technology. In 2023, the company received FDA approval for its AI-enabled lung tool, which uses deep learning technology to more quickly and fully assess lung health.

Owkin

AI predictive analytics for drug development

Owkin uses AI to drive predictive analytics for the development of better drug solutions for a variety of diseases. Perhaps most notably, the company’s platform facilitates collaboration between data scientists and academic researchers. To support this development, Owkin has received a major investment from Sanofi, a French multinational pharmaceutical company.

GE HealthCare

AI orchestration for healthcare IT

Spun off from conglomerate GE in January 2023, GE HealthCare has developed an AI orchestration solution that fully integrates AI-enabled clinical applications into radiology for both GE and non-GE devices. This is being done to boost the quality of medical decision-making. Additionally, the company has hired top executives to assist in its AI healthcare expansion.

Stryker

AI assistance in medical procedures

Already a large and well-established medical device maker, in 2021, Stryker acquired the AI company Gauss Surgical and is aggressively moving to deploy AI more broadly across its product offerings. Among its notable products is the AI-based Stryker Mako robot, which can assist with numerous medical procedures.

Cleerly

AI for coronary health issue detection

In service to Cleerly’s ambitious goal—“creating a world without heart attacks”—the company’s artificial intelligence platform performs an analysis of non-invasive coronary computed tomography angiography (CCTA) scans to assess plaque levels in the heart. Cleerly’s algorithms mine an extensive database full of lab images to compare a patient with historical records.

ClosedLoop

AI for healthcare administration and admission management

ClosedLoop’s data science platform leverages AI to manage and monitor the healthcare landscape, working to improve clinical documentation to lower out-of-network use and predict admission and readmission patterns. Impressively, the company won the CMS Artificial Intelligence Health Outcomes Challenge in 2021.

Oncora Medical

AI healthcare digital assistant solutions provider

Oncora Medical’s machine learning software supports healthcare professionals with numerous administrative tasks in the manner of a digital assistant. It streamlines doctors’ time by assisting in documentation, stores all notes and reports, requests additional relevant notes from healthcare providers, and creates the needed forms for clinical and invoicing uses.

Atomwise

AI for more efficient drug development pipelines

The process of drug development has historically been slow and cumbersome, often requiring years to match compounds to develop new drugs. Atomwise aims to speed this up exponentially by using a deep learning-based discovery engine to sift through its vast database (the company claims 3 trillion compounds) to find productive matches.

For a full portrait of AI healthcare applications, see our guide: AI Top AI Healthcare Software.

Financial Services AI Companies

It’s clear that financial services firms are actively embracing artificial intelligence. Bank of America, in a breathless note to the investment community, opined that “AI is the new electricity.” So what exactly does this look like in an industry that is riddled with regulations, complexities, and longtime, established vendors that may be hesitant to try something new? The companies in this category are vying to show us.

Intuit

Financial assistant technology for business leaders

Intuit is an enterprise that has focused on providing both guided and self-service finance and tax tools to users of products like TurboTax, Credit Karma, Mint, QuickBooks, and Mailchimp. The company recently released Intuit Assist, a generative AI financial assistant that is able to provide SMB leaders with smart recommendations for their financial and customer service decisions; Intuit Assist is available for TurboTax, Credit Karma, QuickBooks, and Mailchimp. Intuit also boasts an AI research program that focuses on developing and refining new AI innovations with explainable AI, generative AI, and more.

Stripe

Partnership with OpenAI to improve document and content processing

Stripe is a SaaS-based financial services company that is frequently selected for its user-friendly payment processing features. Most recently, Stripe has jumped aboard the AI train, partnering most heavily with OpenAI at this time. The two companies have something of an exchange going on right now: Stripe is using GPT-4 for smarter documentation and content summary generation in Stripe Docs, while Stripe is helping OpenAI improve its checkout process and other customer experiences through Stripe Billing, Checkout, Tax, and other Stripe products.

Capital One

AI for loan, fraud, and customer service management

Capital One is a prime example of how financial institutions are finding multiple ways to leverage artificial intelligence alongside tried and true business methods. The financial company’s many AI initiatives include explainable AI, which makes the loan approval process transparent; anomaly detection, which helps fight fraud; and NLP, which improves virtual assistants for customer service.

Brighterion (Mastercard)

AI solutions developer for financial services companies

Acquired by Mastercard in 2017, Brighterion serves Mastercard’s AI needs and also provides AI services to other companies. Brighterion’s AI Express offers customized AI solutions geared to the needs of financial services companies. Brighterion touts its “custom AI that’s production ready in 6-8 weeks.”

Numerai

AI-powered stock market prediction platform

Promoting itself as “the hardest data science tournament in the world,” Numerai’s AI-enabled, open-source platform offers a way for data scientists to predict trends in the stock market and make a profit if they’re right. The business model involves using machine learning models to forecast financial megatrends. The company is supported by Union Square Ventures, which co-founded Coinbase.

Skyline AI (JLL)

AI for intelligent real estate insights

An example of how AI can be leveraged to support virtually any financial transaction, Skyline AI uses its proprietary AI solution to more efficiently evaluate commercial real estate and profit from this faster insight. Competitors in the AI-driven real estate sector include GeoPhy and Cherre, which won the Business Intelligence Group AI Excellence Award. Since its acquisition by JLL in 2021, Skyline AI has continued to expand its teams and technologies for more intelligent real estate outcomes.

Ocrolus

Automated financial document analysis solutions

The need for AI-based automation is enormous in the financial sector because financial services firms always have oceans of metrics and data points to digest. Ocrolus enables banks and other lenders to fight fraud by automating financial document analysis. Significantly, Ocrolus’s human-in-the-loop solution maintains human experience as a core factor in document authentication.

AlphaSense

AI for finance intelligence in public and private companies

AlphaSense competes in the lucrative business data market against big players like Bloomberg. Among AlphaSense’s AI-fueled initiatives, the company is developing a solution that can summarize financial reports to more quickly reveal salient data trends. Recently, AlphaSense announced plans to acquire Tegus, which will certainly expand its financial data and workflow capabilities even further.

Zest AI

AI for borrower data research optimization

Zest AI uses AI to sift through troves of data related to borrowers with limited credit history, helping lenders make decisions with this limited data. In particular, it helps with the auto lending market, where the company claims it cuts underwriter losses by approximately 25% by better quantifying creditworthiness.

Signifyd

AI for fraud detection and mitigation

Signifyd is a company that uses AI to create a “score”—from 0 to 1,000—to fight fraud in the financial sector. While the trend of deploying AI to combat financial malfeasance is sweeping the industry, Signifyd claims to distinguish itself by boosting transaction approvals and dramatically lessening false declines.

HighRadius

Machine learning technology for accounts receivable automation

A leading player in the accounts receivable automation software sector, HighRadius uses machine learning to help with labor-intensive tasks like matching payments with invoicing and assigning credit limits. The company partners with Citibank, Bank of America, and SAP.

DataVisor

AI for fraud mitigation across transaction types

DataVisor deploys AI to combat fraud across many transaction types, from digital payments to fintech platforms. For instance, it monitors transactions in real time to block credit card fraud and protects ACH and Zelle payments to fight unauthorized payments.

Education AI Companies

One of the great promises of AI in education is that it will provide one-on-one tutoring and coaching opportunities, which will markedly boost student performance. If this were to fully mature, AI “teachers” would provide lessons at a far-lower cost than human tutors. AI can also support teachers, helping them quickly craft lesson plans and other educational resources. All of this is simply guesswork, as AI has only started to prove its capabilities in this area. In any case, learning how to use AI will become a core skill for students as it becomes woven into every element of work and culture.

Carnegie Learning

AI education app for mathematics

Focusing on the K-12 market, Carnegie Learning’s MATHia with LiveLab is well recognized as an advanced AI learning app. The app uses an AI-powered cognitive learning system to support math education, offering students one-on-one interactions that allow them to work at a pace that best suits their skill level.

CENTURY

Algorithmic matching for student learning programs

CENTURY is a UK-based educational platform company that uses neuroscience to enable enhanced learning in various high school and college core topics. CENTURY uses algorithms like those at Netflix and Amazon to match previous student experiences with what they should focus on next for optimal educational progress. Additionally, the platform offloads some repetitive teaching tasks so teachers can spend more time focusing on students’ needs.

ELSA

AI for learning English as a non-native speaker

ELSA is a company that uses AI to smooth out the user experience side of learning English as a non-native speaker. Its Speech Analyze tool uses AI to analyze user speech patterns, accents, and other details in order to give feedback on possible improvements. Users can also take assessments that ELSA’s AI uses to customize courses and learning timelines that fit that particular user. ELSA is used in both corporate and educational settings.

Kidaptive (McGraw Hill)

AI-driven child development and learning app

Kidaptive’s “adaptive” AI technology is referenced in its name. Founded by two Stanford alumni, Kidaptive’s Adaptive Learning Platform is heavy on next-gen technology: It uses a multi-tenant cloud deployment and is supported by Hadoop. Solutions include Learner Mosaic and Leo’s Pad to support what it calls “playful, whole child development.” Kidaptive was acquired by McGraw Hill in 2021.

Amira Learning

AI for gamified reading and literacy training

Winner of Time Magazine’s Best Inventions award in 2021, Amira Learning uses an AI-powered gamified learning environment to improve reading skills. Children read aloud as Amira provides real-time support; the solution has multiple tutoring techniques to coach young readers, including offering encouragement.

Duolingo

OpenAI-powered language-learning technology

Well known for teaching foreign language acquisition (they claim more than 50 million monthly users), Duolingo uses OpenAI’s GPT-4 to create free-flowing conversations with language learners, recreating the experience of chatting with a native speaker. Here’s an impressive credential for the company: The OpenAI website hosts a page detailing a Duolingo case study.

Cognii

AI for creative student assessment formats

Cognii’s VLA (virtual learning assistant) platform speaks with students in real time, providing one-on-one coaching. The goal is to transcend the limits of a multiple-choice question format and offer a wide-ranging conversation. The company’s NLP tools respond to students’ own language styles.

Querium

AI tutor for advanced STEM projects

Focusing on short-form lessons in the STEM sector, Querium’s StepWise AI tutor provides students with constant feedback as they work through challenging projects. It is designed to detect issues and provide personalized assistance. The company promotes its “AI based on the wisdom of master teachers.”

Squirrel AI Learning

AI for adaptive, personalized learning programs

Based in China, Squirrel Ai Learning uses artificial intelligence to drive adaptive learning for students at a low cost. Its focus is personalized tutoring on the K-12 sector. The company’s engineers work to break down subjects into smaller sections, enabling the AI platform to understand exactly where each student needs help.

Cybersecurity AI Companies

The challenge with creating a list of today’s AI cybersecurity companies is that every major cybersecurity company now claims to use AI. So a list of “top AI cybersecurity companies” is essentially the same as “top cybersecurity companies.”

Some industry experts doubt the efficacy of AI cybersecurity and say that, while the vendors make big noises about AI, the technology is still immature. That issue is open to debate, but one thing is true. For customers of these security companies, it’s very hard—if not impossible—to look under the hood and fully understand the depth and quality of a vendor’s AI.

CrowdStrike

Managed and comprehensive XDR solution

CrowdStrike offers XDR (extended detection and response), a growing theme in cybersecurity that makes heavy use of artificial intelligence and automation to patrol infrastructure and quickly alert admins to threats. CrowdStrike promotes its managed XDR system’s ability to use AI to close the skills gap in cybersecurity by performing the work of missing security pros.

To learn about the role of AI and perimeter protection in cybersecurity, see the eWeek video: CrowdStrike’s Amol Kulkarni on Trends in Cybersecurity.

Zscaler

AI-powered protection of zero-trust architecture

Zscaler uses a powerful emerging technology in cybersecurity called zero-trust architecture, in which the permission to move through a company’s system is severely limited and compartmentalized, greatly reducing a hacker’s access. The company’s AI models are trained on a massive trove of data to enable it to constantly monitor and protect this zero-trust architecture. In April 2024, Zscaler acquired Airgap Networks, another leading cybersecurity and AI solutions provider. With this move toward AI expansion, expect to see Zscaler’s technologies benefit from Airagap’s innovations, such as ThreatGPT, an OpenAI-powered solution for security analytics, vulnerability detection, and network segmentation support.

SentinelOne

Comprehensive AI endpoint, cloud, and identity security solutions

SentinelOne’s Singularity platform is an AI-powered, comprehensive cybersecurity solution that includes extended detection and response, an AI data lake, AI threat detection, and other features for endpoint, cloud, and identity-based security needs. Most recently, SentinelOne expanded its generative AI capabilities, using generative AI for reinforcement learning and more efficient threat detection and remediation.

Abnormal Security

AI intelligence for employee security practices

Protecting email is a bit of a mind game: Hackers can send deceptive phishing appeals directly to every staffer in the company, so it’s likely that someone’s going to fall for the scheme. To combat this, Abnormal Security uses AI to learn the typical behavior of every employee to help block malicious entry to the perimeter. Impressively, security leader CrowdStrike has invested in and partnered with Abnormal.

Vectra AI

AI for multi-mode cybersecurity measures

Vectra AI’s Cognito platform uses artificial intelligence to power a multi-pronged security offensive. This includes Cognito Stream, which sends enhanced metadata to data repositories and the SIEM perimeter protection; and Cognito Protect, which acts to quickly reveal cyberattacks.

Sophos

Longstanding leader and innovator in AI-powered cybersecurity

Clearly a leader in AI-based cybersecurity long before the current AI hype cycle, the UK-based company launched Sophos Artificial Intelligence way back in 2017. This initiative focuses on developing forward-looking advances in machine learning and data for human-AI interaction and other security uses. Sophos’s deep tool set ranges from endpoint detection to encryption to unified threat management.

To learn how AI is dramatically reshaping security, see the eWeek video: Sophos CTO Joe Levy on AI in Cybersecurity.

Fortinet

AI for automated SOC optimization

At the center of today’s enterprise cyber protection is the security operations center (SOC). Fortinet’s automated SOC uses AI to ferret out malicious activity that is designed to sneak around a legacy enterprise perimeter. The strategy is to closely interoperate with security tools throughout the system, from cloud to endpoints. In June 2024, Fortinet announced that it would be acquiring Lacework, a leading provider of AI-powered cloud, code, and edge security solutions. Fortinet plans to integrate Lacework’s CNAPP into its current AI solutions in order to create a more comprehensive, full-lifecycle AI cloud solution for its customers.

Palo Alto Networks

AI-human partnership for security observability taskwork

With a strong reputation as a cybersecurity company with an advanced strategy, Palo Alto Networks’ AI-powered Prisma SASE (secure access service edge) solution is integrated with its Autonomous Digital Experience Management (ADEM) tool. The net result is that AI helps human security admins with observability across their infrastructure, which is crucial for enterprise security.

Check Point

AI for phishing and DNS threat detection

Check Point’s Quantum Titan offers three software blades (security building blocks) that deploy deep learning and AI to support threat detection against phishing and DNS exploits. The company also focuses on IoT, with tools that apply zero-trust profiles to guard IoT devices in far-flung networks.

SecurityScorecard

Democratized risk management recommendations

SecurityScorecard is a threat and risk intelligence company that provides smart security ratings, automatic vendor detection, cyber risk quantification, and other products and services to identify risks and vulnerabilities before they spiral out of control. The company recently added generative AI to its toolkit through a security ratings platform that has OpenAI’s GPT-4 as one of its foundational models. With this new feature, users don’t have to have cybersecurity or risk management experience to ask questions and receive risk management recommendations.

Cylance AI (BlackBerry)

AI security specialized in mobile IoT

A division of BlackBerry, Cylance AI touts its “seventh generation cybersecurity AI.” Due to its extended lifecycle in use by clients, the AI platform has been trained on billions of cyberthreat datasets. Given its mobile credentials, Cylance is a key player in cybersecurity for the mobile IoT world, a quickly growing sector.

BigPanda

Machine learning and automation for improved observability

Considered a leader in the AIOps sector, BigPanda uses AI to discover correlations between data changes and topology (the relationship between parts of a system). This technology works to support observability, a growing trend in infrastructure security. In essence, BigPanda uses machine learning and automation to extend the capabilities of human staff, particularly to prevent service outages.

Retail AI Companies

AI in retail typically focuses on personalizing the customer experience and supporting automation and data analytics to improve the supply chain. To fully portray AI’s role in retail, this section lists both AI vendors and large retailers that deploy AI. Both groups play a crucial role in creating and enhancing the many uses for AI in retail.

Shelf Engine

AI for stock and inventory optimization

Shelf Engine is an AI startup with a goal to solve one of the most problematic questions in retail: What is the optimal amount of inventory to order? This question is particularly crucial for sellers of perishable goods like fruits and vegetables. Shelf Engine works to automate the stocking process so retailers can hold the optimal inventory level and so customers find what they need while stores need handle only minimal waste.

Deep North

Computer vision and AI for storefront monitoring

Combining computer vision with artificial intelligence, Deep North is a startup that enables retailers to understand and predict customer behavior patterns in the physical storefront. The company specifically provides tools so businesses can use this information to improve customer experience and boost sales. Deep North is an example of how AI is evolving toward analyzing nearly every aspect of human action.

Lowe’s

Partnership with Nvidia to create more data-driven retail processes

Using Nvidia’s AI-based omniverse technology, Lowe’s built a digital twin deployment that allows the store’s retail assistants to quickly see and interact with the retailer’s digital data. The goal is to streamline operations and improve customer service. The AI system will also power a virtual 3-D product catalog.

Bloomreach

AI-driven search and merchandising

A prime example of an AI vendor for the retail sector, Bloomreach’s solutions include Discovery, an AI-driven search and merchandising solution; and Engagement, a consumer data platform. This type of stand-alone AI vendor serving an industry vertical is likely to flourish because many large companies are not equipped to develop AI tool sets themselves.

Accenture

Platform for dynamic merchandising and other retail optimization opportunities

Consulting giant Accenture’s ai.RETAIL solution enables retailers to use AI to turn data —which retailers have reams of—into action that boosts the bottom line. The platform includes dynamic merchandising, providing more real-time actionable data to store clerks, and driving predictive insights to stay ahead of retail trends.

Standard AI

AI for autonomous retail experiences

Clearly the wave of the future, Standard AI is an AI platform that allows customers browsing in stores to select and buy their item choices without the delay of paying a cashier. The strategy is “autonomous retail,” in which retail locations are retrofitted with AI technology to streamline the shopping experience.

Lucyd Eyewear

Smart glasses that can hear

So you’ve been waiting for the first ChatGPT-enabled eyewear? Wait no more: Lucyd, a retailer of “smart” eyewear under the Eddie Bauer and Nautica brand names, has unveiled a smartphone app that allows you to speak to your glasses and hear responses through tiny speakers. The “wearables” sector now has a niche called “hearables.”

Veesual

AI-powered virtual-try-on app

Veesual is an AI-powered virtual-try-on app that allows users to customize their outfits, virtual models, and the digital dressing room where they try on clothing. The tool uses deep learning so clothing images look realistic and maintain their definition when merged with human model images. Additionally, Veesual’s CX-focused approach to AI pays attention to finding and showing customers the best sizes for their needs.

Companion

AI companion for pets

An AI-powered companion for your dog, Companion’s box (about the height of an average dog) uses machine vision and machine learning to interact with your pet in real time. The device can even dispense treats, which should help with any behavioral training goals. The company also plans on an AI companion for cats; given feline insouciance, the training modules might not be so well received.

AI Industry Organizations

These industry organizations for the AI sector play a number of roles. First and foremost, they advocate for the regulation of artificial intelligence. This is an enormously important focus, given how AI’s exponential growth will affect everything around it. To what extent can we as a society impose guidelines on AI’s growth, which has thus far been driven by pure profit?

These groups also lobby for greater diversity in AI, which is essential. We’ve already seen that AI systems embody legacy bias; this must be corrected more proactively to create inclusive systems. Additionally, these AI organizations support cross-vendor development of AI to promote the overall advancement of the technology.

Association for the Advancement of Artificial Intelligence

Founded in 1979, the AAAI is an international scientific group focused on promoting responsible AI use, improving AI education, and offering guidance about the future of AI. It gives out a number of industry awards, including the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, which provides $1 million to promote AI’s efforts to protect and enhance human life.

AI4Diversity

This nonprofit’s motto is “Leveraging AI, education, and community-driven solutions to empower diversity and inclusion.” AI4Diversity was founded by Steve Nouri, a social media influencer and AI evangelist at Wand. Given that AI platforms have been found to perpetuate the bias of their creators, this focus on diversity and inclusion is essential.

AI Infrastructure Alliance

Supported by a group of major enterprise vendors that includes Hewlett Packard Enterprise, and sponsored by the likes of Nvidia, the AI Infrastructure Alliance “aims to foster collaboration and interoperability between leading MLOps tools to allow a CS [canonical stack] to form more quickly and effectively.” The organization supports open-source and open-core software so users aren’t locked into narrow proprietary solutions.

Partnership on AI

Founded by a consortium of tech giants—Google, Meta, Amazon, IBM, and Microsoft—Partnership on AI is a nonprofit with a mission to research best practices for AI systems. It works to “bring together diverse voices from across the AI community.” Partnership on AI includes more than 100 partners from academia and business.

Black in AI

Founded in 2017, Black in AI is a technology research and advocacy group dedicated to increasing the presence of black tech professionals in artificial intelligence. Black in AI notes that “representation matters,” and that AI algorithms are trained on data that reflects a legacy of discrimination, so promoting black voices in AI development is crucial to the technology’s growth.

Machine Intelligence Research Institute (MIRI)

Originally known as the Singularity Institute for Artificial Intelligence, MIRI supports research to “ensure that smarter-than-human artificial intelligence has a positive impact.” Among the cautionary articles that MIRI has posted: Pausing AI Developments Isn’t Enough. We Need to Shut it All Down.

AI Now Institute

AI Now Institute creates policy research to address the concentration of power in the tech world. Their report Confronting Tech Power notes that “there is no AI with big tech,” and that “a handful of private actors have accrued power and resources that rival nation-states while developing and evangelizing artificial intelligence as critical social infrastructure.”

The Alan Turing Institute

Funded by the UK government, The Alan Turing Institute produces research that addresses crucial issues in artificial intelligence, society, and the economy and collaborates with businesses and public groups to use the research to deal with pressing concerns. That this group is government-funded raises a major question: Will more governments around the world step up to fund groups that prompt the AI sector to work for greater social good?

The Rockefeller Foundation

While AI is only one of many focuses for this famed nonprofit, the Rockefeller Foundation is quite active in the AI sector; for example, one of its core focuses is the responsible governance of AI. The organization issued a report called AI+1: Shaping Our Integrated Future, which is based on conclusions from a diverse group of experts who seek to deploy machine learning for positive social impact. Additionally, the foundation makes grants, including donating $500,000 to Black in AI.

To learn about the many challenges in AI ethics, see the eWeek video: Rockefeller Foundation’s Zia Khan on AI and Ethics.

Bottom Line: AI Companies

This list of AI companies is, admittedly, a partial portrait. In truth, it’s a blurry snapshot of something whizzing by too fast to completely capture. The generative AI landscape in particular changes daily, with a slew of headlines announcing new investments, fresh solutions, and surprising innovations.

The progress of artificial intelligence won’t be linear because the nature of AI technology is inherently exponential. Today’s hyper-sophisticated algorithms, devouring more and more data, learn faster as they learn. It’s this exponential pace of growth in artificial intelligence that makes the technology’s impact so impossible to predict—which, again, means this list of leading AI companies will shift quickly and without notice.

As investment pours in, the underlying technologies that fuel artificial intelligence are each seeing their own rocket blasts of innovation. Machine learning, deep learning, neural networks, generative AI—legions of researchers and developers are creating a remarkable profusion of generative AI use cases. In sum, the lifecycle for these AI companies is not so much digital transformation as digital revolution, and the next version of this list is likely to look completely different.

To learn about AI certifications that can advance your career, see our guide, 30 Top AI Certifications.

The post 150 Top AI Companies: Visionaries Driving the AI Revolution appeared first on eWEEK.

Generative AI ethics is an increasingly urgent issue for users, businesses, and regulators as the technology becomes both more mainstream and more powerful. Although still a nascent form of artificial intelligence, generative AI (GenAI) has already attracted enormous investment due to its remarkable ability to generate original, human-like content based on massive datasets and neural network technology. This ability raises challenging questions about how developers and users of generative AI can remain compliant with privacy, security, and intellectual property regulations, making the need to establish clear guardrails and guiding ethical principles paramount.

Businesses need a clear understanding of how to use generative AI responsibly and how to align their goals for the technology with their company values to protect customers, data, and their business operations, and vendors need legal and ethical frameworks for developing and training GenAI tools to ensure they’re contributing to the appropriate use of the technology moving forward. Here’s what you need to know.

Generative AI Ethics: 10 Key Ethical Challenges

Ethical challenges abound with generative AI, given the relative newness and remarkable potential of this form of artificial intelligence. These ethical issues include concerns around privacy, security, accountability, environmental impact, and more. The degree of difficulty each of these challenges offers varies considerably, but taken in their totality, they require considerable resources to handle.

Preventing Bias in Datasets

Like other types of artificial intelligence, a generative AI model is only as good as its training data is diverse and unbiased. Biased training data can teach AI models to treat certain groups of people disrespectfully, spread propaganda or fake news, and create offensive images or content that targets marginalized groups. Less directly harmful, but still problematic, content inaccuracies can perpetuate outdated cultural tropes as facts.

Protecting the Privacy of Users

Whether you elect to use generative AI technology or not, there’s a chance your personal data could be used without your knowledge as part of the model’s training dataset. For example, some models collect training data from unauthorized or unverified corners of the internet, where your information may live without your consent. More commonly, users of chatbot tools like ChatGPT will create a free account and use the free version of the tool without fully understanding when and how their data is collected and used. Depending on the type of data you choose to submit in these tools, it could lead to identity theft, credit card theft, and other personal violations as a result of your personal data being exposed.

OpenAI recently added a new ChatGPT feature that lets free plan users turn on a temporary chat that won’t save their data for training purposes. While this is a great step toward privacy, many users still aren’t aware of this feature and don’t realize that ChatGPT can and often will save your inputs as training data if you don’t turn this feature on.

Increasing the Transparency of Training Processes

Companies like OpenAI are working hard to make their training processes more transparent, but for the most part, it isn’t clear what kinds of data are being used, where training datasets are being collected, and how they’re being used to train generative AI models. This limited transparency not only raises concerns about possible data theft or misuse but also makes it more difficult to test the quality and accuracy of a generative AI model’s outputs and the references on which they’re based.

In addition to updating their policies and sharing their development plans, some generative AI leaders are adding additional features so users can check the sources behind generated information. For example, Google Gemini lets users click an icon to reveal sources if they’re unsure about how and where information was sourced. In some cases, Gemini will highlight content in orange if it cannot definitely prove the content’s source.

Holding Developers Accountable for Content

Accountability is difficult to achieve with generative AI precisely because of how the technology works. Since AI runs on algorithms that allow the technology to functionally make independent decisions, AI developers and companies frequently argue that they cannot control AI hallucinations or other reckless decisions that an AI tool makes algorithmically. So far, this lack of transparency has effectively gotten AI companies off the hook for offensive content outputs, as they claim ignorance about how these models are evolving over time.

Preventing AI-Assisted Cyberthreats

Although generative AI tools can be used to support cybersecurity efforts, they can also be jailbroken and/or used in ways that put security in jeopardy. For example, ChatGPT tricked a TaskRabbit worker into solving a CAPTCHA puzzle on behalf of the tool by “pretending” to be a blind individual who needed support to receive this assistance.

The advanced training these tools receive to produce human-like content gives them the ability to convincingly manipulate humans through phishing attacks, adding a non-human and unpredictable element to an already volatile cybersecurity landscape. Many of these tools also have little to no built-in cybersecurity protections and infrastructure. As a result, unless your organization is dedicated to protecting your chosen generative AI tools as part of your greater attack surface, the data you use in these tools could more easily be breached and compromised by a bad actor.

Mitigating the Environmental Impact of AI

Generative AI models consume massive amounts of energy very quickly, both as they’re being trained and as they handle user queries. The latest generative AI tools have not had their carbon footprints studied as closely as other technologies, yet even as early as 2019, research indicated that BERT models (a type of large language model) had carbon emissions roughly equivalent to the emissions of a roundtrip flight for one person in an airplane. Keep in mind this amount is just the emissions from one model during training on a GPU. As these models continue to grow in size, use cases, and sophistication, their environmental impact will surely increase if strong regulations aren’t put in place.

Guarding Against Misuse of AI

Whether intentional or not, it’s very easy to misuse a generative AI tool in a way that compromises data security and privacy or otherwise causes harm. For example, an employee in a healthcare setting may accidentally expose key patient or payment information to a generative AI tool in a way that compromises that data and allows it to be stolen.

In other, less well-meaning cases, generative AI may be used to generate AI deepfakes, or realistic-looking videos, images, audio clips, and texts that are wrongly attributed to someone in order to make them look bad or spread misinformation. In these cases and many more, generative AI is an all-to-eager assistant in the chaos, though many AI companies are working to reduce the chances for harmful content generation and misconduct by users.

Protecting Intellectual Property Rights

Especially in opposition to AI image and video generation tools, several artists and creators have come forward with claims and lawsuits stating that AI companies are using their original artwork and IP without their permission. Stability AI, Midjourney, and DeviantArt are three of the most notorious AI companies in this regard, with users collectively suing them for training their image models on copyrighted images without consent. Most of these cases and similar ones are still working their way through the legal system, so it’s unclear what the outcome will be and how it will impact IP cases in the future.

Understanding AI’s Impact on Employment

As generative AI use cases become more mature and capable of handling workplace tasks, there’s a growing fear that artificial intelligence will replace large sections of the workforce. There really aren’t any protections in place for human workers against AI bots, at least at this time, so as this technology progresses, people will need to upskill or shift to a different industry in order to remain competitive. It is very likely that certain types of creative, clerical, and technical roles will be replaced or partially upended by generative AI in the coming years.

Addressing the Need for More Regulation

Some basic AI regulations already exist, but few address the complexities and nuances of generative AI versus traditional AI and machine learning. What’s more, few national or international laws and regulations have actually been passed into law, with the EU AI Act being the most notable exception. For the most part, there are only best practices and recommended use policies, which still leaves room for AI and tech corporations to more or less do as they please with their technology and users’ data.

Why Are Generative AI Ethics Important?

The development of an ethical framework for generative AI is critical for a technology powerful enough to replicate human output in so many ways because it’s far too easy to unintentionally misuse its potential. Such misuse can create major problems, including legal liabilities. Developing guidelines to monitor and manage generative AI is necessary to help your organization do the following:

• Protect customers and their personal data
• Protect proprietary corporate data
• Protect creators and their ownership and rights over their work
• Prevent dangerous biases and falsehoods from being proliferated
• Supplement existing cybersecurity frameworks and best practices
• Align with emerging governmental AI and data compliance regulations

To learn more about how businesses are creating guidelines for responsible AI usage, read our guide to AI policy and governance.

Best Practices for Using Generative AI Ethically

It’s clearly perilous for a company to adopt generative AI without clear guidelines in place. The core best practices for ethical use of generative AI focus on training employees, implementing data security procedures, continuously fact-checking an AI system’s output, and establishing acceptable use policies.

Train Employees on Ethical AI Use

If employees are allowed to use generative AI in their daily work, it’s important to train them on what does and doesn’t count as an appropriate use case for AI technology. For the best possible outcomes, train your staff on what data they can and absolutely cannot use as inputs in generative AI models. This will be especially important if your organization is subject to regional or industry-specific regulations.

Additionally, if generative AI is part of your organization’s internal workflow or operations, it’s best if your customers are aware of this, especially when it comes to their personal data and how it’s used. Train your staff to be transparent about this, and explain on your website and to customers directly how you’re using generative AI to make your products and services better. Most important: Clearly state what steps you’re taking to further protect your customers’ data.

Implement Strong Data Security and Management

If your team wants to use generative AI to get more insights from sensitive corporate or consumer data, certain data security and data management steps should be taken to protect any data that’s used as inputs in a generative AI model. Data encryption, digital twins, data anonymization, and similar data security techniques can be helpful methods for protecting your data while still getting the most out of generative AI. Highly powerful modern cybersecurity solutions, such as extended detection and response (XDR) tools, may also help to protect this unconventional but highly vulnerable attack surface.

Check AI Responses for Accuracy and Appropriateness

Generative AI tools may seem like they’re “thinking” and generating truth-based answers, but what they’re trained to do is produce the most logical sequence of content based on the inputs users give and the algorithms on which they’re trained. Though generative AI models generally give accurate and helpful responses through this training, they still can produce false information that sounds true.

Make sure your team is aware of this shortcoming and does not rely on the tool for research needs. Online and industry-specific resources should be used to fact-check all responses received from generative AI tools, especially if you plan to base research, product development, or new customer initiatives on content generated through this technology.

Establish and Enforce Acceptable Use Policies

An acceptable use policy should cover in detail how your employees are allowed to use artificial intelligence in the workplace. This policy must include the ethical expectations of your organization as well as any regional or industry regulations that need to be followed.

Significantly, this acceptable use policy must name various managers and company officers as the responsible parties to monitor the organization—consistently—and hold their respective divisions accountable for adhering to written AI policies. Enforcement procedures and penalties for misuse and non-adherence should be carefully spelled out and distributed to the staff on an ongoing basis.

Generative AI Ethics Laws and Frameworks

Your organization cannot create its policy for ethical use of generative AI in a vacuum. There are a number of national and international regulations and policy standards that should be studied for guidance. Remaining in true compliance over time means staying current with these regulations, which are expected to change rapidly going forward.

European Union AI Act

To date, the only major international AI regulation that has become law is the European Union’s AI Act, which regulates artificial intelligence and related data usage across the EU. The act enforces rules for AI systems that pose any risk to consumer privacy and includes various rules and obligations for developers, deployers, and consumers of AI products. It is heavily focused on transparency and risk mitigation.

The EU AI Act was adopted by the European Parliament and Council in May 2024 and officially published in the Official Journal of the EU two months later. It will be enforced in phases, with some aspects going into effect within a month of publication while others may not be enforced until two or three years later.

Current enforcement mechanisms include a non-legally-binding Code of Practice that requires AI companies to provide technical documentation to the appropriate authorities, provide information on capabilities and limitations to downstream providers, summarize what training data they use, and set up and follow policies that comply with EU copyright laws. Since this is not legally binding, it’s difficult to say what happens to organizations that do not comply; however, stricter legal consequences will likely come once EU AI Act standards are fully in effect.

Other International Regulations

A number of standards bodies around the world have published resources for guidance and support. The most widely recognized include the following:

• The European Union’s ethics guidelines for trustworthy AI
• The Organisation for Economic Cooperation and Development’s “OECD AI Principles”
• SHRM’s “Generative Artificial Intelligence (AI) Chatbot Usage Policy”

U.S. Policies and Standards

The United States government has expressed concerns about the quick development and unfettered growth of AI companies, but for the most part, AI regulations are not yet law. Instead, the government has focused on releasing voluntary frameworks and ethical documents that organizations can choose to follow or abstain from. These are two of the most notable:

• NIST’s “Artificial Intelligence Risk Management Framework”
• The White House’s “Blueprint for an AI Bill of Rights”

Industry-Specific Frameworks

While several industries have developed their own frameworks for AI usage, for the most part, this is happening on an organization-by-organization and case-by-case basis. Some highly regulated industries, however, are using existing laws and frameworks to ensure sensitive data is protected when used in AI systems:

• Healthcare: Both FDA regulations and regulations that are part of HIPAA are used to ensure AI-driven medical devices and all other AI instances used in healthcare are following existing protocols for data privacy and security.
• Finance: SEC regulations and fair lending laws are being applied to AI technology to ensure that trading, investing, and lending decisions that are informed by AI do not discriminate against certain consumers or fail to disclose when and how AI is being used to make those decisions.
• Transportation: Particularly in autonomous vehicle AI technology, the National Highway Traffic Safety Administration (NHTSA) and several state transportation regulations are requiring generative AI companies to demonstrate comprehensive safety and performance testing before their vehicles can become street legal.

Emerging Trends in AI Ethics Regulations

AI ethics has quickly become a popular topic in the legal field, especially as lawsuits related to intellectual property theft, data breaches, and more come to the fore. Current areas of focus for AI ethics in the legal system include AI liability, algorithmic accountability, IP rights, and support for employees whose careers are derailed by AI development. As more AI regulations pass into law, standards for how to deal with each of these issues individually are likely to pass into law as well.

3 Top Generative AI Ethics Courses to Consider

Given the many ethical complexities created by the rise of generative AI, business decision makers are increasingly looking for formal coursework to gain the necessary background to navigate these challenges. We’ve identified three online courses that provide a mix of theory and practice to help industry professionals best understand the core issues involved with ethics and generative AI.

Generative AI: Implications and Opportunities for Business

This generative AI course from Class Central, RMIT University, and FutureLearn is a beginner-friendly course that focuses on developing a basic understanding of the technology, how it applies to different industries and use cases, and challenges that come with adopting and using this technology at scale. In the final week of the four-week course, users focus primarily on challenges and ethical dilemmas with the technology, looking at best practices for adoption and use, legal and regulatory considerations, and the latest trends and controversies with the technology. This course costs $189.

Generative AI Law and Ethics Cornell Course

The Generative AI Law and Ethics Cornell Course is an online course from Cornell University that discusses the latest laws and ethical concerns and solutions in the world of generative AI. The course is two weeks long and requires six to eight hours of work per week. It is designed primarily for business leaders, entrepreneurs, and other employees who are hoping to use AI effectively within their organization. The class is taught by a Cornell University professor of law and covers AI performance guarantees, the consequences of using AI, legal liability for AI outcomes, and how copyright laws specifically apply with AI. Cornell University does not publicly disclose the cost, but an AI bot on the site noted that courses typically cost around $3,600.

Generative AI: Impact, Considerations, and Ethical Issues

Generative AI: Impact, Considerations, and Ethical Issues is a Coursera course that partners with IBM to teach users about generative AI limitations, ethical issues and misuse concerns, how to use generative AI responsibly, and the economic and social impact of generative AI. The course is a beginner-level class that includes approximately five hours of online coursework and six assessments. Upon completion, users can get a shareable LinkedIn certificate and/or use this course to work toward a Generative AI Fundamentals Specialization. This course is free of charge.

See the eWeek guide to the best generative AI certifications for a broad overview of the top courses covering this form of artificial intelligence.

Bottom Line: Generative AI Ethics Remains Challenging

It’s often challenging to know if you’re using generative AI ethically because the technology is so new and the creators behind it are still uncovering new generative AI use cases, many of which create their own concerns. And even as generative AI technology is changing on what feels like a daily basis, there are still few legally mandated regulations surrounding this type of technology and its proper usage.

Yet despite these challenges, you owe it to your customers, your employees, and your organization’s long-term success to establish your own ethical use policies for generative AI long before regulations require this commitment.

To learn more about the power and potential of these emerging applications, see eWeek’s guide to generative AI tools and applications.

The post Generative AI Ethics: 10 Ethical Challenges (With Best Practices) appeared first on eWEEK.

How Does Intelligent Automation Work?

Intelligent automation is an advanced automation process that merges artificial intelligence and machine learning with robotic process automation to automate business process workflows and create intelligent, robotic agents that can take over some of an organization’s routine, workflow-based tasks. Robotic process automation (RPA) bots alone can handle a number of automated business tasks. Unlike IA solutions, RPA bots don’t possess the additional human-like capabilities to go beyond routine training and take on new tasks that require cognitive and sensory capabilities.

When supported by RPA, artificial intelligence, and machine learning, intelligent automation bots have the algorithmic toolsets to comprehend and execute automated tasks at a deeper level. Some upper level IA bots are trained with deep learning, neural networks, and natural language processing so they can understand human language and generate unique content on a range of topics. The training data involved in IA is typically a large set of data from various sources and in diverse formats, both structured and unstructured. In essence, this sophisticated AI training gives RPA-powered machines the capacity for decision intelligence, or at least the context to make data-driven decisions that are largely independent from regular human intervention.

To give IA machines the ability to “see” or interact with their surroundings, many of these bots also receive training based on computer vision and optical character recognition (OCR). With this training in particular, intelligently automated machines can take on complex tasks in retail, manufacturing, and other settings that typically require a pair of eyes and sensory skills.

Key Components of Intelligent Automation

The underlying components of AI include workflow orchestration techniques, integration techniques, and real time data processing, all of which are interrelated with machine learning technologies.

Workflow Orchestration

Workflow orchestration effectively acts as the “project coordinator” in intelligent automation. Orchestration management tools within IA platforms are in charge of organizing and managing task and decision sequences within an automated process. This responsibility includes assigning tasks to either humans or robots, deciding on the best order of operations, and identifying and handling exceptions or errors in real time. When done well, workflow orchestration optimizes how your organization uses its resources and helps intelligent automations to be managed more efficiently, quickly, affordably, and with scalability.

Integration Technologies

Intelligent automation can only perform its role effectively if it’s integrated with business applications where data exists and daily operations happen. To achieve optimal levels of integration, most IA platforms directly integrate with common clouds and business applications through integration technologies like APIs and built-in integrations.

Alternatively, some IA vendors offer third-party marketplaces, consultants, and design studios to set up custom niche integrations as needed. The integration step of IA is incredibly important, as it enables end-to-end process automation without the errors inherent with manual data transfers.

Machine Learning and AI

ML and AI provide the complex cognitive capabilities necessary for IA systems to handle upper level assignments that include unstructured data or continuous learning and development. IA’s main sensory skills—including vision, language processing, and image recognition—come from the incorporation of AI technology. In essence, AI’s presence can take a chatbot or other type of RPA bot and enhance it with something resembling human comprehension and decision-making skills.

Real-Time Data Processing and Analytics

In addition to initial training datasets and data uploads, intelligent automation systems require real-time data capture and processing to keep up with system demands. These data tools work to capture, process, and then analyze data as it comes in, using both predictive and prescriptive analytics. From there, the IA system can make recommendations and take actions based on what the latest data says.

Intelligent Automation Use Cases by Industry

Intelligent automation can be incorporated into a range of business use cases and industries. With the right training and monitoring in place, many organizations are beginning to bring IA into their workflows.

Insurance

In complex and tedious insurance workflows like claims and risk management, IA bots can comb through large amounts of data quickly and automate tasks like claim intake and settlement. When these tasks are automated at scale, it can increase insurance company productivity and reduce the chance of risky or erroneous claims. Intelligent automation can also be used to improve fraud detection methodologies in insurance as well as in banking and other finance settings.

Healthcare

IA in healthcare can handle some of the back-office administrative tasks of a healthcare facility, following automated workflows while adhering to cybersecurity and compliance requirements for data processing. For healthcare administration, IA is often used to support the work of medical claims and bills processing. IA has also been used to manage large-scale tasks in public health, like COVID-19 vaccination distribution and tracking. More recently, it has also become part of the AI-driven drug discovery and pharmaceutical product development trend.

Business

Some organizations are using IA tools to create more sophisticated robotic call center agents to handle calls and chats without sounding so scripted. IA tools may also be used to more efficiently manage call logs, score leads, personalize marketing campaigns, and make recommendations based on buyer history or buyer sentiment. Certain key aspects of recruitment and HR can also be automated with IA agents, including onboarding and payroll processing tasks, candidate screening, and general document processing.

Manufacturing

IA-powered robots can take on human tasks—or even chains of tasks—on factory production floors and make adjustments to their performance based on real-time training and feedback. They can also use applied predictive analytics and computer vision/machine vision to manage quality, maintenance, inventory stocking, and order fulfillment schedules for both factory machines and manufactured products. These manufacturing-specific IA solutions do all this while also considering how changes will impact supply chain schedules and logistics.

IT and Cybersecurity

Intelligent automation is particularly effective for automating software testing and recommendations and actions for continuous integration and continuous deployment (CI/CD). It can also be used to manage cybersecurity efforts in DevSecOps scenarios. IA bots can handle the full cybersecurity management lifecycle, not only detecting vulnerabilities and issues on a massive scale but also using predictive analytics and smart recommendations to execute the necessary improvements and handle threat response activities.

Consumer Technology

While technologies like self-driving cars, smart checkout kiosks, and similar self-service technologies are still fairly new, they are becoming more capable with the help of IA. IA technology found in consumer tech is often the most adaptable and dynamic, frequently adjusting to match the sentiments and range of requirements that customers have. To truly understand what customers need, consumer tech’s intelligent automation solutions frequently include biometric identification, computer and machine vision, optical character recognition, and other features that help to identify and understand human preferences.

Best Practices for Using Intelligent Automation

Intelligent automation is a complex and multifaceted automation strategy that requires full management buy-in, dedicated training, change management, thoughtful planning, and ongoing strategic pivots. Incorporating best practices into your intelligent automation initiatives can help you ensure its success.

Involve All Relevant Company Stakeholders

Data scientists, automation engineers, IT staff, and business leaders should be involved from the start in customizing IA to fit the organization. Gathering a multidisciplinary team will ensure the technology meets organization-wide demands and gets ongoing support and input from all departments and project teams.

Set Goals and Consider Important Use Cases

At an early stage, seek out employee feedback on tedious task work that could be automated or otherwise handed off; don’t simply ask managers, but be willing to talk to employees who are in the weeds of the organization’s most tedious tasks. Additionally, consider your budget and any tools or resources you may still need to get started, as well as any measurable goals or outcomes you hope to achieve with intelligent automation. All of the initial goals you set should be documented for future reference.

Invest in Flexible IA Tools You Can Integrate

The market is full of AI software and RPA tools, but not all of them effectively combine the strengths of both technology types to support intelligent automation. Research available options, paying particularly close attention to any advanced technologies and features that meet your needs. Also, take the time to evaluate how—or if—these platforms will integrate with your other business process management tools.

Test and Monitor Automations at All Stages of Deployment

At all stages of IA implementation, test how automations are performing and if they are meeting their intended purpose. It’s especially important to quality-test automations that affect customer-facing interactions, such as intelligent customer service agents or autonomous devices. The QA specialists or automation engineers on your team are likely the best fits to test how automations are performing. Be aware that different types of automation testing and monitoring tools can supplement their work; this will likely require some research.

Follow AI Ethics and Ethical Best Practices

Because intelligent automation is so heavily entwined with artificial intelligence, it’s important to consider the AI-focused ethical implications of the data you’re using and where and how you apply artificial intelligence in your workflows. Ensure that all of your most sensitive data—particularly PHI and PII—is stored securely and separately from these technologies, and frequently audit your IA tools and results to ensure data is being used ethically.

If the tools you’re using aren’t transparent enough to give you this kind of visibility, consider switching up your toolset or strategy to create more visibility. Taking this step will help you to protect your consumers’ data as well as any other sensitive business data from unauthorized access and usage.

Read our guide to generative AI ethics for an in-depth look at the challenges and solutions involved with using AI in business.

Challenges in Implementing Intelligent Automation

Intelligent automation is a new and complex automation strategy that can be difficult to implement and maintain. Being aware of the challenges you’re likely to encounter—and their solutions—can prepare you for a successful IA implementation.

Change Management and Employee Adoption

Introducing new, complex types of automation can disrupt existing workflows and lead to frustration or resistance among employees. To overcome this hesitation among employees, business leaders need to effectively communicate, provide role- and task-specific training support, and clearly demonstrate how IA will benefit both employees and the organization.

Data Security and Privacy Concerns

Intelligent automation is only possible with massive amounts of training data and data inputs, some of which may be sensitive or personally identifiable. Especially when you are working with data that may compromise a customer’s privacy, it’s absolutely critical that you follow all data privacy regulations and establish security safeguards within your IA system to protect against breaches and unauthorized data access.

Cost and ROI Assessment

IA can be an expensive implementation process, and even more so if you aren’t clear on your priority projects and how much each component will cost initially and over the long term. It’s a good idea to complete an ROI assessment to determine where IA will be most impactful and cost-efficient for your business.

Automation Bias

Much like with other AI-based solutions, outcomes can be biased, unethical, or otherwise contain errors if the training data you use is biased or incomplete. From the start and on an ongoing basis, you’ll need to identify and mitigate biases both in your datasets and in the systems once they’re up and running. A QA analyst or team is a great way to stay on top of bias and performance issues.

How to Select an Intelligent Automation Solution

When your business has decided to invest in an intelligent automation solution, consider the following features and capabilities to make the best choice:

• Process Discovery: The best IA solutions include built-in assistance and features to support process discovery, process mapping, data collection, and risk identification. Look for features like task mining, process mining, document analysis, and other tools that support in-depth research.
• Robotic Process Automation (RPA): Robotic process automation capabilities are at the core of what makes IA bots actually operate, so you can’t invest in a tool for IA if it doesn’t include RPA. Look specifically for task automation, process orchestration, process modeling, error handling, system integration, and data transformation capabilities that work for structured and semi-structured data (AI will take care of the unstructured data).
• Integrations: IA tools are most effective when they can directly integrate with your most-used data sources and business software solutions. Specifically, look for intelligent automation solutions that will integrate with ERPs, CRMs, and major databases in your tool stack.
• Intelligent Document Processing (IDP): Much of the data that helps IA perform its programmed tasks is found in structured and semi-structured documents, so it’s important to select a tool with IDP capabilities. Look for computer/machine vision, document capture and classification, document handling for different document formats, data extraction, data validation, and data enrichment capabilities before making your choice.
• Generative AI (GenAI): Generative AI is increasingly becoming a part of the IA tool stack because it helps IA bots generate believable and scalable content in real time. At a minimum, your selected tool should have basic AI/ML capabilities, natural language processing, and natural language understanding, but it’s also worth looking at tools that include or integrate with generative AI models and solutions.
• Scale Supported by the Cloud: An IA tool that is compatible with or hosted on a major cloud platform offers more scalability, cost efficiencies, innovation opportunities, security and disaster recovery features, and general accessibility. If you’re not sure if the IA tool you’re evaluating includes cloud-driven scalability, look for keywords and features like elasticity, global reach or localization, scalable and tiered pricing structures, and integrations with popular cloud platforms and services.
• Security, Privacy, and Compliance: IA tools can only run effectively and comply with regulations and customer privacy expectations if security, privacy, and compliance features are natively included. Look for admin and access controls, data encryption, data masking and anonymization, compliance certifications, security assessments, data retention policies, and other features that indicate your chosen tool’s commitment to comprehensive data protections.

3 Key Tools for Intelligent Automation

Among the large and growing group of solutions that serve the intelligent automation market, we recommend considering three leading solutions: Automation Success Platform, UiPath Business Automation Platform, and SS&C Blue Prism Enterprise.

Automation Success Platform

Automation Anywhere’s Automation Success Platform is a large IA platform that includes dedicated, integrated systems for automation and AI that are open, trusted, flexible, and capable of operating on-premises or in the cloud. Its automation system includes RPA bots, API tasks, a center of excellence (CoE) manager, and several relevant enterprise integrations. Its AI system includes an AI agent studio, document automation, process discovery, and several different generative AI models, process models, and specialized AI solutions. Many users also appreciate the built-in automation co-pilot that offers use-case-specific support in healthcare and life sciences, banking and finance, service ops and supply chain, IT, and HR.

Automation Anywhere’s entry level solution starts at $750 per month. This includes one control room, one unattended bot, and one bot creator. Additional bots cost $125 per month for attended, or $500 per month for unattended.

UiPath Business Automation Platform

The UiPath Business Automation Platform is an IA and RPA solution that offers dedicated solutions for discovery, automation, and operations management. For discovery, the platform includes dedicated tools for process, task, and communications mining, as well as a dedicated automation hub for brainstorming and planning. Its automation solutions include a range of prebuilt apps, studios, robots, assistants, autopilots, and marketplaces to help users build the exact solutions they need. To reach and maintain enterprise-level standards, the platform also includes a dedicated test manager, orchestrator, AI center, insights, and automation ops.

UiPath’s Pro solution starts at $420 per month and includes robots to enable on-demand execution, advanced automation tools with user governance, and basic support.

SS&C | Blue Prism Enterprise

SS&C | Blue Prism Enterprise is an enterprise-level automation solution that focuses on helping businesses build highly capable digital workforces. The platform is divided into three main sections: the Digital Workforce section, the Design Studio section, and the Control Room section. With Digital Workforce, users can set up RPA and autonomous software robots for their applications and specific use cases. In Design Studio, businesses can access user-friendly, no-code tools for process automation design and access the Digital Exchange (DX) marketplace for third-party integrations. Through the Control Room, business leaders can assign digital workers and scale their task requirements, set up SLA-based orchestration, and monitor results with centralized analytics.

Blue Prism does not publicly disclose its pricing; contact the vendor.

3 Intelligent Automation Courses to Consider

As the intelligent automation sector expands rapidly, creating job openings for qualified individuals, those interested in a career in the field will need to seek out training to boost their skills. We’ve identified three online courses that provide a solid mix of skills and hands-on knowledge to help you get started or advance your career in IA.

Automation Anywhere Essentials RPA Certification

Automation Anywhere is one of the leading IA solutions providers in the market today, so it’s strategic to pursue one of their certifications. The Essentials RPA Certification is specifically designed for university students who want to pursue a career in this field and is one of the only options available for individuals who are new to a technology career. The certification includes a handful of prerequisites; from there, you’ll take a 60-minute online certification test and complete an automation development assessment to earn your certificate. More advanced certification paths are also available from Automation Anywhere.

There’s no cost for this Essentials program; more advanced Automation Anywhere certification costs $50-$80 per program.

Certified Intelligent Automation Professional (CIAP) Program

The Certified Intelligent Automation Professional (CIAP) Program is a six-week certification opportunity that focuses on teaching procurement professionals and other backoffice leaders how to apply intelligent automation, best practices, and tools to their line of work. Each week of the program, students will have approximately two to five hours of coursework, discussions, and quizzes to complete. At the end of the program, a final essay is assigned to assess skills from across the program. This program is intended more for business leaders who need to understand IA from a strategic standpoint rather than for technical team members who need to grasp IA on a tactical basis.

This course costs $1,795.

Intelligent Automation Foundations (LinkedIn)

Intelligent Automations Foundations is a LinkedIn Learning certification program that focuses on the basics behind intelligent automation, how it works, and how it can be applied through strategic methodologies and frameworks. Many past students have complimented the program’s focus on explaining how the sensory technology works within IA systems. The program consists of six chapter quizzes, with one at the end of each section. Upon completion of this program, students earn a LinkedIn Learning certificate of completion and are eligible to earn CPE credits from the National Association of State Boards of Accountancy (NASBA).

This course can be taken at no cost during a one-month free trial of LinkedIn Premium.

Frequently Asked Questions (FAQs)

What Are Examples of Intelligent Automation?

Examples of intelligent automation include automated claims processing in both healthcare and insurance, automated onboarding workflows and recruitment screenings in human resources, automated predictive and prescriptive analytics across industries, and intelligent chatbots for customer service. New examples and use cases are launching regularly, typically to reduce manual labor and human error in routine or complex taskwork.

What Is the Difference between AI and Intelligent Automation?

Artificial intelligence (AI) is one component of what makes intelligent automation work. While AI primarily consists of algorithms, big-data training sets, and machine learning components, intelligent automation takes all of these strengths and combines them with robotic process automation (RPA) to achieve intelligent automation workflows at scale.

What Is the Role of Intelligent Automation?

Intelligent automation plays an important role in optimizing existing business workflows and transforming operations to improve outcomes for both employees and customers. Intelligent automation is frequently used to supplement or replace manual human labor, a step that can automate repetitive tasks and improve processes, support complex data analytics taskwork, improve customer experience, enhance risk mitigation strategies, and create new efficiencies that may ultimately reduce business expenses.

Bottom Line: Intelligent Automation Improves Productivity

Intelligent automation has gained steam in recent years, not only because it incorporates today’s most sophisticated AI technologies but also because it offers a significant improvement in business productivity. This is a dynamic time for intelligent automation and a great time to get started with the technology. For optimal results, follow the best practices listed above and don’t lose sight of the people who need to be involved. Especially as this technology and its capabilities evolve, you’ll want to ensure that all relevant stakeholders in your business receive the upskilling training they need to support the major productivity boost enabled by intelligent automation.

Read our guide to the best machine learning platforms to find out more about the latest marketplace of tools, what they can do, how much they cost, and which might be a good fit for your business.

The post What Is Intelligent Automation? Impacts on Modern Enterprises appeared first on eWEEK.

Enterprises that want to gain competitive advantage need to learn how AI is reshaping the retail world for customers, employees, and business leaders alike and establish a foundational knowledge of the pioneering AI tools enabling this dynamic technology in the retail sector.

Understanding AI in Retail

Artificial intelligence in retail can take many forms depending upon what it is programmed to support. Consequently, AI adoption in retail depends on how and where business leaders choose to focus their AI efforts.

For most retail organizations, AI models are fine-tuned to act as standalone digital retail platforms or are embedded into existing retail platforms, ERP systems, AI CRM software, and business websites. These models are trained to handle a variety of behind-the-scenes and customer-facing tasks, including helping to manage inventory, supply chain processes, customer interactions, data analytics, and other features of the retail lifecycle.

Comprehensive retail AI deployments can view, absorb, and make future adjustments based on every customer interaction, click, and movement of inventory, looking at each of these steps in the retail lifecycle as a unique data point. Often, this data is absorbed into the AI model’s training set and used to further specialize and fine-tune the model’s ability to interact with customers. The model grows iteratively to deliver the personalized support and buying experience they want.

While many people fear that incorporating too much AI into retail will hurt business outcomes because of the loss of human touchpoints with customers, early adopters are finding the opposite to be true. AI can successfully mimic many human qualities in customer interactions, freeing up time for human employees to tackle more complex customer concerns and work on the strategies behind more user-centric customer experiences for the future.

5 Reasons Why Businesses Need AI in Retail

AI can improve retail operations in an ever-growing number of ways, including boosting customer engagement, improved marketing strategies and streamlined supply chains.

Enhanced Customer Experience

As shopping increasingly moves to an e-commerce format, retailers confront a difficult paradox: Customers are physically more distant yet expect a more personalized experience when shopping online. While hyper-personalization is difficult to achieve at scale for any retailer, AI software can ingest a vast number of data points and parameters and apply this information to create more personalized customer support, ads, product listings and other customer engagement strategies.

Supported by AI technology that is specifically designed with retailers and their customers in mind, ads are better targeted at what users actually want, chatbots can more clearly answer user questions on a customer’s schedule, and AI-driven apps give users access to new types of shopping experiences that fit their preferences. Although there may be less human-to-human contact in retail as AI is adopted, customers are on the receiving end of a customer-first experience that relies on intelligent algorithms to learn and adapt to their shopping behaviors.

Optimized Inventory Management

Artificial intelligence can automate conversational workflows, inventory and supply chain management, and other repetitive retail tasks that have traditionally required human touch. In inventory management, using AI reduces the chance for human error, ensuring inventory levels are neither too high nor too low and are proactively adjusted for predicted changes in demand. AI is already being used to support demand forecasting for highly specific retail seasons and shifts, as well as to automate stock replenishment.

As AI takes over customer service and inventory management touchpoints, retailers can reduce their staff or focus their attention on more strategic tasks. Especially as more and more of the workforce moves away from retail and service-based industries, these AI instances will help to fill in a production gap with limited retraining and recruitment requirements.

Improved Sales and Marketing Strategies

Sales and marketing strategies of yesteryear have sometimes felt like a shot in the dark, requiring retailers to make decisions based on limited data and visibility into customer behavior. With AI technologies in place, businesses can develop comprehensive buyer personas that take into account all buyer behaviors, more specific demographics, and multichannel interactions (including unstructured data from these channels).

Sales and marketing AI tools not only help users to better understand their customers, but can also help them to craft strategic content for these customers. For example, many marketing and sales tools now include generative AI elements to help businesses quickly craft blogs, ads, product descriptions, and other content that is well-suited to reach and engage the right audiences.

Advanced Data Analytics and Insights

AI-powered analytics tools democratize the analytics process with natural language inputs and outputs, contextualized explanations, and more detailed and accurate predictive analytics that are useful to marketing and sales teams. These tools can also analyze greater quantities and different types of data than most traditional marketing analytics tools can. A growing segment of AI data analytics tools also offer a prescriptive component, providing useful recommendations or even carrying out recommendations for improving operational workflows in a retail ecosystem.

Streamlined Supply Chain Operations

Advanced AI technologies like computer vision and robotics are designed to monitor the entire supply chain and inventory management lifecycle, identifying an error as soon as it occurs. This makes it easier to mitigate stocking and shipping errors before they lead to unhappy customers or inventory shortages. The level of detail and insight AI provides also helps businesses of all sizes to develop dynamic and just-in-time stocking methodologies, so they’re less likely to over-order or under-deliver in any of their markets.

Essential AI Tools and Technologies for Retailers

The number of AI technologies supporting retail operations is growing rapidly; some are still pioneering, like visual recognition, while others are well established but improving quickly, like AI-based chatbots.

AI-Powered Chatbots

AI chatbots with access to varying degrees of information can be embedded into customer-facing e-commerce sites, social media, and other applications. Going a step beyond chatbots that rely on a few manual workflows for specific conversational topics, AI chatbots are trained on a variety of subjects—often including an organizational database. This allows them to more effectively understand and respond to diverse customer questions and requests. With algorithmic training on such large sets of historical data, AI chatbots can handle much more complex customer questions and requests than previous chatbot iterations.

Some AI vendors don’t take over customer service interactions altogether but instead provide live coaching and suggestions to human customer service representatives. These live suggestions are often combined with detailed customer dashboards that give reps both the tools and the language they need to have a more product-focused conversation with customers on the phone.

Personalization and Recommendation Engines

Based on individual users’ metadata, past purchases, ad engagement, sentiment analysis and other data-driven inputs, retail AI solutions can now recommend products and services to customers that they may not have otherwise considered purchasing but are likely to want. Indeed, recommendation AI is one of the fastest-growing AI-retail areas because of how well it connects with and monetizes customer preferences.

Historically, human marketers and ad managers work behind the scenes to analyze how ads are performing and then decide what changes should be made to get more audience engagement. AI is quickly taking over this task on a widespread scale and making more accurate targeting decisions, primarily because these technologies are able to analyze a greater quantity of engagement data points more quickly.

Retail AI software also more frequently identifies subtle data patterns that humans may overlook. Additionally, these tools can make intelligent, fast-paced decisions based on past data, whether that’s updating ad copy based on user sentiment or changing where the ad falls on a webpage based on previous heatmap data.

As this technology continues to advance, AI is targeting hyper-personalized ads to individuals based on their metadata, prompting them to be more interested in and engage with advertising materials. Also significant, some AI models are monitoring, learning, and updating ads in real time, ensuring that ads are always optimized for the current audience.

Read our guide to the top 20 generative AI companies to learn how today’s generative AI leaders are serving businesses in a range of sectors.

Intelligent Analytics

AI-driven data analytics give users—including non-data scientists—a better understanding of all the different kinds of data at their disposal. These models can analyze data in different formats and in large quantities, looking at historical and current purchasing metadata across different categories to more accurately forecast demand.

For example, business owners may use AI analytics to learn that the sweaters they sold last year not only sold quickly but were reviewed favorably in customer reviews and conversations. They may also learn that while the sweater sold well in the Midwest, Northeast, and most of Europe, it did not perform as well in other U.S. regions or most of Asia. Armed with this data, the retailer will know that it should bring back this product but that some markets require more stock while others require less.

For the non-data scientist, AI analytics tools are particularly effective at offering prescriptive analytics, or analytics that make recommendations for how to adjust business tactics in the future based on current data. The natural-language approach these AI tools can take helps business users across departments and areas of expertise deploy this data for better results.

Automated Inventory Management Systems

AI-supported demand forecasting is one of the ways retailers are now more accurately predicting how much inventory they need and where and when it should be stocked. AI-driven data analytics may also help retailers determine when prices should be changed, how seasonal purchases impact inventory levels and supply chain movement, and where customer returns require more frequent inventory shifts.

In a more tangible sense, AI-driven robotics and computer vision can be used to support automated inventory management, whether that’s through just-in-time restocking or dynamic safety stock management. A growing number of retail warehouses are relying on AI assistive robots to scan inventory and monitor stock levels and then restock or remove stock as needed.

Visual Recognition and Security Enhancements

Through a combination of biometrics and AI recognition technology, storefronts are starting to simplify the user checkout experience, including in physical stores. For example, some stores are now allowing repeat customers to simply grab their items and walk out the door; the store’s AI recognition and scanning technology recognizes the customer and automatically charges them without requiring them to physically check out.

This same system’s reliance on biometric technology may also help retailers more quickly identify and solve problems when it comes to shoplifters. For customers that want a more user-friendly e-commerce shopping experience, many companies have added AI assistive elements to their retail apps. These features may make purchase suggestions or integrate with virtual wallets for a smoother shopping experience.

AI Copilots

AI copilots are essentially virtual assistant AI models that have been trained to support business leaders and employees with their day-to-day tasks. In retail, AI copilots may be tasked with providing real-time updates on sales and performance analytics or producing new product descriptions or listings. They also give in-store employees up-to-date information about store policies and inventory availability, and equip managers with information about employees’ schedules and staffing gaps.

Machine Learning Algorithms

Machine learning algorithms are trained to recognize and act on the differences between different users and data points, including ad clicks, purchases, and customer service conversations. This type of AI is particularly useful for making predictions about overall brand sentiment, what users most want to purchase, and how purchase trends will change over time.

Natural Language Processing

Generative AI is well-suited for building chatbots, virtual assistants, and other AIs that help customers directly by generating content in response to their queries. This is possible because of the natural language processing (NLP) and natural language understanding (NLU) that are inherent to generative AI and large language models. With their deep training in and understanding of human language and communication, as well as data stored from previous interactions, generative AI models can directly answer customer questions and even make purchase suggestions based on a customers’ past purchases or preferences.

Computer Vision Systems

Computer vision is a type of artificial intelligence technology that has been trained to see and respond to its physical environment based on visual triggers. Retail greatly benefits from this type of AI, as computer vision supports cashierless checkout infrastructure and other personalized shopping experiences that require fewer human interactions to run smoothly. Behind the scenes, computer vision may also be used to monitor and manage inventory levels, analyze customer behaviors in physical storefronts, and better detect and mitigate suspicious shopper activities in stores.

Robotics and Automation

AI-powered robotics is a fast-growing subfield of retail AI that can power physical machines that move inventory or provide a useful shopping interface for customers onsite. Robotics and automation workflows may be used to scan, update, or shift inventory from warehouse to storefront; drive delivery vehicles and drones; or physically interact with or provide a checkout mechanism for customers in stores.

Predictive Analytics Platforms

Predictive analytics platforms combine AI, statistical data modeling, and massive historical datasets to create accurate predictions for retail metrics related to future purchases and buyer behaviors. Some of these tools also rely on third-party datasets, internet connectivity, and APIs to assess competitor performance and decisions and determine how these might impact retailer pricing and stocking decisions.

With a predictive analytics platform that is supported by AI, the AI component can quickly give users natural language insights into where sales, marketing, and operational decisions are working and where they could use adjustments. Hyper-personalized advertising and product listings, targeted loyalty programs, and price changes are all examples of retail shifts that may be informed by demand forecasting and other data provided by a predictive analytics tool.

Ways to Drive Your Retail Marketing With AI

Traditional marketing technologies lack the detailed insights and extensive support that AI can provide. Here are some of the ways AI can support a far more effective marketing strategy for retail businesses:

• Targeted Advertising and Promotions: AI can analyze past customer purchases and clicks, heatmaps, and granular customer demographic information that may not be noticeable to humans who are less familiar with demographic data. From there, AI can be used to develop targeted ad content and send it to the right people at the right time.
• Customer Segmentation and Insights: Unlike traditional customer segmentation based on broader demographics or purchasing history, AI-driven customer segmentation can use highly specific data to create more targeted, purchase-ready segments of buyers. Within these segments, AI can also be used to uncover even more insights and information that may inform how you develop ads—or even new products—that cater to this group of buyers.
• Personalized Shopping Experiences: By recording and analyzing responses to a suggestion on a product page, AI quickly identifies likely buyers and predicts what they will add to their cart based on their behaviors and purchasing history. These suggestions are provided in a way that does not disrupt the shopping process—in fact, many customers appreciate being targeted with products they may not have known about but can benefit from.
• Dynamic Pricing Strategies: In real time, AI technology can assess product demand, competitor prices, customer sentiment, and other factors to determine if products should be repriced in that moment. Dynamic pricing often leads to higher sales numbers without requiring retailers to take hands-on action.
• Social Media and Sentiment Analysis: AI models can collect and analyze multichannel customer feedback to give retailers a better understanding of how customers feel about the brand and specific products. Particularly with large language models and generative AI models, unstructured data like text can be collected from multiple different sources, and at the same time, spammy reviews can be filtered out so they don’t muddy the waters of your analysis. Retailers can apply these new sentiment insights to more personalized customer experiences, including catered ads and more precise audience segmentation.

3 AI Tools for Retailers You Should Consider

Given the advantages offered by AI retail tools, it’s imperative that retailers employ an array of AI applications to boost sales and streamline business processes.

ai.RETAIL: Better Insight Into Data

Accenture, the global professional services firm, is the mastermind behind ai.RETAIL, an AI and data analytics platform that helps retailers better understand their data both at an individual customer and big picture level. Its features include customizable customer-level views that show historical buying patterns and loyalty, dynamic merchandising for different customer channels, supply network digital twin development, and customer targeting capabilities.

For example, Canada-based restaurant chain Tim Hortons has used ai.RETAIL and other products and services from Accenture to set up its customer loyalty app. This app includes personalized rewards and intelligent data analytics that help the vendor to keep customers engaged with the brand.

Stackline Shopper OS: CRM for Retailers

Stackline’s Shopper OS is a back-end platform for retailers that allows them to manage different aspects of customer relationships with the help of AI, going beyond the traditional CRM. Its main features include a multi-retailer CRM, syndicated ratings and reviews, reward and loyalty program development, AI-targeted surveys and automated customer messaging. It also offers an AI-driven analytics tool that looks at shopper behaviors across different retailers, channels, and platforms.

Amazon One: Streamlined Shopping

Amazon One is an AI-driven solution that Amazon has built to simplify the customer-side of each retail interaction with a shopping experience it calls “Just Walk Out” shopping. With Amazon One, a customer’s payment information is linked to their palm, and AI and machine learning are used to identify that palm and charge the appropriate person’s account when they leave a participating store with a purchase. This means users can quickly pick out their items and leave without going through a traditional checkout process.

Read our guide to the top AI retail solutions transforming the industry to learn more about the vendors and products having the biggest impact on businesses.

Case Studies: How Leading Brands Use AI in Retail

Leading players, from e-commerce to traditional in-store retailers, have already invested heavily in AI solutions to boost sales. In fact, many of the solutions these giants use are used across several industries.

Amazon: AI in E-Commerce

As a longtime pioneer of AI and machine learning solutions, developing tools both internally and for its vast network of partners and clients, Amazon has infused AI into its operations at various levels and for a range of purposes. A recent example of how it has used AI to improve customer experience is the “customers say” feature added to Amazon customer reviews. This AI-generated content is effectively a summary of all reviews provided on any given product, giving customers a quick-glance opportunity to learn what customers do and don’t like about the product they’re thinking about buying.

Walmart: AI in Traditional Retailing

Walmart has leaned heavily into conversational AI in recent months, especially as many of its customers have begun to engage with the brand online. With Walmart Voice order and Text to Shop, users can purchase and repurchase different items through their mobile devices without going through the actual motions of making the purchase. Walmart’s AI recognizes the buyer and their past purchases and payment methods to make the purchase on their behalf.

Additionally, Walmart has improved both its internal and customer-facing chat interfaces. Its customer support chatbots are now supported by AI, enabling chatbots to handle most customer queries and requests, while only the most complicated conversations are handed off to human support representatives. Internally, Ask Sam is a voice assistant that helps in-store associates whenever they have questions about their schedules, their coworkers, and other important workplace information.

Kroger: AI in Grocery and Food Retailing

Kroger has begun working with Intelligence Node, an AI retail intelligence company, to optimize its product pages and taxonomy so the information customers want about different products is easier to find, more detailed, and more up-to-date. This work may include improving rankings on search result pages, generating new and more effective product descriptions, and providing more detailed analytics data for how product pages are performing.

Zara: AI in Fashion and Apparel

Zara is an international fashion brand that is using AI technology to manage supply chain, inventory, and customer return workflows in particular. It uses a process called Just-In-telligent supply chain system management, which combines AI-driven inventory management and data analytics to improve real-time supply chain updates. The brand has also worked with Jetlore (now owned by PayPal), a predictive retail AI company, to help Zara better predict customer behaviors and purchasing decisions based on more granular clothing data points. The company reported that using AI in these ways has improved its deliveries, inventory carrying costs, and issues with returns.

Bottom Line: Retailers Embrace AI for a Competitive Edge

Retailers are getting creative and finding all kinds of ways to incorporate AI to gain a competitive advantage. This ranges from allowing AI to directly interact with or indirectly influence customer interactions, restock and monitor inventory across distributed sites, or give business leaders a more detailed view of current performance data. As retailers implement AI strategically, it can benefit both employees and customers with automation, personalization, and hands-off features that improve the overall retail experience.

See our guide to the top generative AI tools and applications to explore the most popular solutions in this dynamic and emerging technology on the market today.

The post AI in Retail: Cutting-Edge Solutions for Modern Challenges appeared first on eWEEK.

Artificial intelligence as a service (AIaaS) companies use a combination of AI technology and the “as a service” model to deliver advanced solutions to businesses that may not otherwise be able to handle the complexities, costs, and skill requirements of implementing AI.

Many businesses use at least one cloud-based AI service, and since few of these organizations have the in-house expertise to do this work effectively, they outsource their AI needs to third-party vendors. Consequently, AIaaS companies have become a crucial part of building a successful AI strategy.

Here are my picks for the best AI as a service companies:

• IBM: Best for Automating Complex Processes
• Amazon Web Services (AWS): Best for Features and Global Presence
• Microsoft: Best for Developers and Data Scientists
• Google: Best for Data Preparation and Management Resources
• OpenAI: Best for Generative AI
• NVIDIA: Best for AI Hardware and Infrastructure Expertise
• ServiceNow: Best for IT and Business Workflows
• Salesforce: Best for Marketing and Sales Teams
• MonkeyLearn: Best for Text Analysis
• DataRobot: Best for Hands-On Support
• H2O.ai: Best for Industry-Specific Service Solutions
• Oracle: Best for Custom Business Use Cases

Top AIaaS Company Comparison

IBM

Best for Automating Complex Processes

Long a teach leader, IBM has emerged as an early AI leader with services that it offers under the umbrella of IBM Watson and watsonx. Watson is an AI system that combines natural language processing, machine learning, and other advanced technologies to analyze and understand large amounts of data. It has been used in various industries, including healthcare, finance, retail, and customer service, to provide insights, make recommendations, and assist with decision-making.

You can select from a variety of prebuilt apps from IBM Watson, including watsonx Orchestrate, for automating complex processes. Because of the depth and maturity of IBM’s offering, the company excels at automating even the most highly configured complex systems.

Rounding out the IBM AIaaS offering is conversational AI through natural language prompting, watsonx Assistant for building and commissioning virtual agents, watsonx Code Assistant to help developers with AI-generated code, and Watson Discovery for content analysis in business documents. The IBM Garage is a solution that enables co-creation of solutions between IBM professionals and clients.

Pros and Cons

Pricing

IBM charges different rates for its various products and services within its AIaaS offerings:

• The IBM watsonx Orchestrate base edition costs approximately $1,500 per month
• watsonx Assistant is available in a free version up to a higher, custom-priced enterprise version
• The base Watson Discovery Plus plan starts at $500; the Enterprise plan starts at $5,000
• Contact the IBM sales team to get the actual rate for your selected AI services or products

Key Features

• Natural language processing (NLP) and model development
• AI developer tools and APIs
• Data and model governance capabilities
• Advanced, AI-powered text, image, and video analytics
• IBM Garage for co-creation among IBM experts and customers

Amazon Web Services (AWS)

Best for Features and Global Presence

Amazon Web Services (AWS) is a leading provider of cloud computing and AIaaS services. The company offers various AI and ML services that can be segmented into different categories, including computer vision services like Amazon Rekognition, an image and video analysis tool, and Amazon Lookout for Vision for detecting defects and automating inspection. AWS also offers language AI tools such as Amazon Lex, a tool for building chatbots and virtual agents, and Amazon Transcribe for automatic speech recognition and other speech-to-text projects.

The company has also done extensive work to advance its generative AI products and services, including Amazon Bedrock and Amazon Q. Specifically with the announcement of Amazon Q, AWS made it clear that it is working to develop services and functions across all three layers of the generative AI stack: bottom-layer infrastructure, middle-layer for building, and top-layer with useful applications.

AWS offers hundreds of services worldwide, with data centers in multiple regions; its large reach supports its AIaaS offering. The company’s strategic plans for full-stack AI innovation make it an ideal choice for companies seeking an AI service provider with a true enterprise-ready presence. It also offers its products and services at a wide variety of price points. For instance, the AWS Free Tier is one of the most generous service tiers on the market, giving businesses with all different budgets the opportunity to work with enterprise-level experts and partners.

Pros and Cons

Pricing

The amount you pay for AWS’s AIaaS products and services depends on your selected service:

• A limited free tier of solutions is available
• Use the AWS calculator for an estimate
• Contact the AWS sales team about custom services and implementations

Key Features

• AWS marketplace online store for software and applications
• AWS Free Tier with AI, ML, and other enterprise technology solutions
• Access to third-party models and generative AI building tools in Amazon Bedrock
• Amazon Q generative AI assistant for software development and business data management
• Code and DevOps services for application and code optimization

Microsoft

Best for Developers and Data Scientists

Azure AI is a portfolio of AI services from Microsoft built for data scientists who have the coding knowledge necessary to create custom and unique versions of existing Azure solutions. These data experts along with developers, engineers, and ML experts use the company’s customizable AI models and APIs to complete a variety of complex tasks. These include knowledge-mining-driven searches, text-to-speech and content translation tasks, image analysis with computer vision, and OpenAI-driven generative AI content creation and model development.

Among its impressive services is Azure AI Search, which enables users to get insights from various types of content, including documents, images, and media. Businesses can also make their customer service processes more efficient by using multilingual and multimodal Azure bot services to stand in for certain communication workflows so human agents can focus on more complex support tasks. Microsoft also has a deep partnership with OpenAI, which is reflected in its many advanced generative AI services and offerings.

Pros and Cons

Pricing

Microsoft Azure pricing varies based on your selected service:

• Limited free tier available
• Azure AI Search costs up to $7.68 per hour
• Azure OpenAI Service prices vary by model, context size, token quantity, and other variables
• Contact a sales representative for detailed quotes

Key Features

• Azure OpenAI Service for LLM-powered app development and services
• Azure AI Search for enterprise search and app development tasks
• Azure AI Document Intelligence
• Speech, language, and translation capabilities
• Image analysis and categorization with Azure AI Vision

Google

Best for Data Preparation and Management Resources

Google, a leader in AI and data analytics, has been at the forefront of AI research and development for a long time and in significant ways. In fact, it was a group of Google technologists who ideated and first invented transformer architecture, the neural network design for artificial intelligence that now powers many of the largest AI models and generative AI models on the market today. Google has also made significant contributions to the field of machine learning and deep learning through its development of the TensorFlow framework, an open-source library widely used for building and deploying machine learning models.

While Google is clearly a mastermind behind many AI innovations, it is also an effective provider of various AI and data services, with a platform full of tools that is ideal for data prep and management–the very foundation of AIaaS.

Many of these tools are offered through Google Cloud and Vertex AI, Google’s fully managed platform for AI application and solutions development. For generative AI specifically, Google offers access to Gemini, a collection of multimodal generative AI models and solutions that emphasize user-driven feedback and improvements over time. For users who are most concerned about data lifecycle management and ensuring the data they feed into their AI models is sound, Google offers a number of helpful tools and solutions, including strategic partnerships with Acxiom, CoreLogic, Shutterstock, TransUnion, and Dun & Bradstreet, all of which provide big datasets to Google users who need different types of data to train their AI models.

Pros and Cons

Pricing

Google’s AIaaS prices vary widely depending upon product, platform, and service:

• Google AI Studio is free to use for certain versions of different Google products and services, including the free version of the Gemini API
• The paid version of Gemini API charges based on rate limits, input quantities, output quantities, and prompt context caching quantities
• Vertex AI is priced based on products, models and machines, data quantities, region, and other variables

Key Features

• Gemini ecosystem of multimodal language models and chat interface
• Vertex AI and Vertex AI Studio for fully managed AI development platform and resources
• User feedback and fact-checking mechanisms
• Partner network for OSS and foundation models, infrastructure, platform, code assist, data, SaaS and applications, and systems integrators
• Google AI and Android developer tools

OpenAI

Best for Generative AI

By the standards of many of the other companies listed in this guide, OpenAI is fairly new and small, a generative AI startup that first took the world by storm when it released ChatGPT to the public in late 2022. But it is the most impressive innovator and product leader in the generative AI landscape, delivering multimodal solutions for text, code, image, and even video generation to business and casual users alike.

While anyone with an internet connection can access ChatGPT’s lower-tier features and functions, business users get additional features and benefits through OpenAI’s business-tier products and services. ChatGPT Enterprise expands context windows, improves enterprise data management and privacy capabilities, enhances analytics, and extends priority support and account management capabilities to business users.

The company’s various API versions allow users to fine-tune, embed, and otherwise customize powerful existing generative models for their specific needs. While OpenAI isn’t necessarily a traditional AIaaS partner since it is more focused on research and development, the business arm of the organization ensures that businesses of all types can get the support they need to develop generative models on an AIaaS basis.

Pros and Cons

Pricing

The company’s pricing plan varies based on whether customers are using it via an API.

API Pricing

Price points listed below are for OpenAI’s flagship models available via API:

• GPT-4o: $5 per 1 million input tokens and $15 per 1 million output tokens
• GPT-4 Turbo: $10 per 1 million input tokens and $30 per 1 million output tokens
• GPT-3.5 Turbo: $0.50 per 1 million input tokens and $1.50 per 1 million output tokens

Find information about other models available via API, including legacy models and fine-tuning models, on OpenAI’s pricing page.

ChatGPT Pricing

• Free: Limited access to ChatGPT features and functions at no cost
• Plus: $20 per month, primarily for individuals who require more advanced features
• Team: $25 per user billed annually, or $30 per user billed monthly
• Enterprise: Pricing information is available upon request

Key Features

• GPT-4o, GPT-4 Turbo, and GPT-3.5 Turbo flagship models available via API
• Multimodal generative AI models
• Account management and custom security reviews by OpenAI experts
• Generative AI app development and embedding in business applications
• Playground for AI modeling experimentation

NVIDIA

Best for AI Hardware and Infrastructure Expertise

NVIDIA is widely acknowledged as a leading AI company, which is well-deserved if you consider the impressive investments, strategic partnerships, and innovative research they’ve moved forward in the past several years. NVIDIA is a unique leader among this list because it provides products and services for nearly all aspects of AI model development, storage, and hosting; most significantly, it is one of the leading providers of GPUs that can handle larger AI models and data loads.

The NVIDIA GPUs are arguably the very foundation of AI hardware, which of course supports infrastructure. Without these ultrafast GPUs, the development of AI would be far slower.

As far as its services go, most are offered through the NVIDIA AI platform, which includes multiple layers of infrastructure, software, and applications to support different users’ needs. This is a particularly strong AIaaS option for specific industries’ niche needs. Healthcare and pharmaceutical customers are a great example of this, as the BioNeMo solution offers mature and easy-to-use technology for complex drug discovery workflows and processes.

Pros and Cons

Pricing

• NVIDIA products and services are not listed with prices
• Contact the NVIDIA sales team directly for pricing information
• Pricing information can be found in the NVIDIA Marketplace for users looking to invest in GPUs or other hardware for AI development

Key Features

• NIM inference microservices in NVIDIA AI Enterprise
• Multilayer approach to NVIDIA AI platform
• Access to NVIDIA and third-party AI models
• Industry-specific AI solutions and offerings, including BioNeMo
• AI training service offered via NVIDIA DGX Cloud

ServiceNow

Best for IT and Business Workflows

ServiceNow is known for its deep focus on automating workflows and improving service delivery across various departments within its customer organizations. With the continued integration of AI into its product and services portfolio over time, ServiceNow has specifically been able to optimize its IT operations management (ITOM) and predictive AIOps services for users. With this maturation, ServiceNow uses AI to help business users identify and predict issues in their business workflows and, from there, automate IT solutions that follow business rules and governance best practices without severely disrupting business operations.

ServiceNow’s ITOM-based services include generative-AI-powered Now Assist, operations footprint discovery, service mapping, certificate management, firewall audits and reporting, service graph connectors, configuration management database, community monitoring and metrics, event management, metric intelligence, health log analytics, and automated service delivery. AI service capabilities outside of ITOM include generative AI, virtual agents, task intelligence, predictive AIOps, automation discovery, and process mining. In sum, clients can assemble a robust AIaaS offering from its many tools.

Pros and Cons

Pricing

• ServiceNow does not directly offering pricing information
• Prospective buyers should book a demo to learn more about the platform
• Customer requirements are used to create custom rates

Key Features

• Now Assist generative AI embedded into all ServiceNow workflows
• AI agents and customer service solutions
• Supportive AI apps for generative AI, process mining, predictive AIOps, AI search, and workforce optimization
• AI-automated IT operations management (ITOM)
• Compatibility with on-premises and cloud environments

Salesforce

Best for Marketing and Sales Teams

Salesforce is the leading provider of cloud-based CRM technology and has cemented that lead with its commitment to developing innovative AIaaS platform for marketing and sales use cases. The Einstein 1 Platform is its collection of AI, data, and cloud-specific solutions that allow users to create generative AI apps and infuse AI into different business workflows. The platform is integrated into all aspects of the Salesforce platform, including Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, and Heroku.

The Salesforce and Einstein combination enables businesses to build AI-powered apps, automate processes, and gather insights to transform customer experiences at all stages of the customer lifecycle. Einstein AI can perform complex tasks such as natural language processing, predictive analytics, and machine learning to automate operations and uncover insights into customer data and behavior. It can also help to generate multimodal content for AI marketing campaigns and sales mediums, all of which help these teams to spend less time creating content and collateral and more time developing the strategy behind these materials.

Pros and Cons

Pricing

While most Salesforce products and tiers include some basic AI capabilities, only higher-level tiers include Einstein AI features or other advanced AI services. These are the best subscription options for interested users:

• Einstein 1 Sales: $500 per user per month, billed annually
• Einstein 1 Service: $500 per user per month, billed annually
• Marketing Cloud Growth Edition: $1,500 per month, billed annually
• Einstein 1 Platform Starter: $25 per user per month, billed annually
• Einstein 1 Platform Plus: $100 per user per month, billed annually

Key Features

• Einstein Copilot for built-in AI assistance and workflows
• Copilot and AI customization through Einstein 1 Studio
• Sales, Marketing, Customer Service, and Commerce Cloud-specific Einstein applications and use cases
• Low-code AI builders
• Einstein Trust Layer for security and privacy guardrails

For an in-depth view of how Salesforce AI aids business, see the eWeek guide: Salesforce and AI: How Salesforce’s Einstein Transforms Sales

MonkeyLearn

Best for Text Analysis

MonkeyLearn is one of the best text analysis tool and service providers in the market. It not only offers high-quality services but also meets the cost and accessibility AIaaS needs of smaller and midsize businesses. With MonkeyLearn, users can access a repository of pre-trained AI models or train their own models to handle different tasks such as sentiment analysis, topic classification, keyword extraction, and content categorization.

With customer sentiment analysis, users can know their customers’ opinions about their products and services in greater depth and typically with more proactive notice. This level of insight can be used to improve their customer experience and determine future directions for the business. MonkeyLearn also makes it easy to analyze large amounts of unstructured data, such as social media posts and customer reviews.

Pros and Cons

Pricing

• MonkeyLearn does not discuss pricing on its website
• Third-party sites indicate that plans start at $299 per month for 10,000 queries per month
• MonkeyLearn Studio and educational access may also be available at little to no cost

Key Features

• Color-coded, multi-format data visualizations
• No-code text analytics and business templates
• Text classifiers and extractors
• NPS, review, CSAT, support, survey, and VoC analysis
• Prebuilt and customer machine learning models

DataRobot

Best for Hands-On Support

DataRobot is an enterprise AI service provider that helps companies build and deploy AI and machine learning models. It offers focused, hands-on support, which is essential for navigating the complexities of AI development.

The DataRobot platform uses automated machine learning techniques to streamline the model development process and automates various steps, such as data preprocessing, feature engineering, model selection, and hyperparameter optimization. Users appreciate this vendor’s thoughtful approach to AI governance, data governance, and strategic lifecycle management for AI models. The platform is also designed specifically for enterprise requirements, meaning it can handle larger AI workloads without introducing major lag times or performance problems into the mix.

Through DataRobot’s AIaaS approach, users can get started with their Foundational Package, which includes guided onboarding, ongoing access to applied AI support and DataRobot University, and real-time technical support. At an additional cost, customers can also subscribe to Specialty Packages services, which cover everything from additional training and hackathons to model governance and strategic AI roadmapping. Most recently, DataRobot has introduced the DataRobot Generative AI Catalyst Program, a new service program focused on upskilling, building, and developing strategic AI initiatives for your organization.

Pros and Cons

Pricing

• Contact the DataRobot team directly for pricing information

Key Features

• Open ecosystem for data, APIs, and applications
• AI governance with registration, auditing, and documentation features
• Cloud, technology, and services partners, including NVIDIA, Google, Microsoft, and AWS
• Code-first AI accelerators for generative and predictive AI development
• Specialty service packages for training, AI and ML roadmapping, use case delivery, model migration, model governance, and hackathons

H2O.ai

Best for Industry-Specific Service Solutions

H2O.ai is a provider of artificial intelligence and machine learning technologies and services that are mostly powered by AutoML and no-code deep learning engines. In addition to more traditional closed-source, fully-managed models and services, the company also provides open-source software, support, and services for developing complex and custom machine learning models.

To truly differentiate itself as an AIaaS provider, H2O.ai has invested heavily in researching and developing comprehensive service packages for specific industries, including finance, government health, insurance, manufacturing, marketing, retail, and telecommunications. Among its specialized, industry-specific solutions and use cases are features for claims management, hospital capacity simulation, fraud detection, predictive fleet maintenance, lead scoring, and clinical workflow management.

Pros and Cons

Pricing

• H2O.ai doesn’t disclose rates on its website
• Prospective buyers should contact sales for quotes
• Publicly available data shows that H2O.ai Cloud costs $50,000 per unit
• For some third-party vendors, such as AWS, users must pay for at least four AI units to access the platform

Key Features

• H2O AI Cloud with multiple deployment options
• H2O.ai Hospital Occupancy Simulator
• GenAI App Store and Gov GenAI App Store for public-sector-specific solutions
• Enterprise h2oGPTe and H2O LLM Studio
• Generative and predictive AI solutions in both closed-source and open-source formats

Oracle

Best for Custom Business Use Cases

Oracle is a veteran in the enterprise technology business, long offering database and cloud technologies that work for different data types and use cases. It only seems natural, then, that Oracle has been able to jump into this group of top-tier AIaaS providers, especially in its Oracle Cloud Infrastructure (OCI) platform that provides the necessary foundation for extensive, custom AI development.

OCI is the Oracle platform where its most advanced AI software and services are found, including several multimodal generative AI capabilities and services that focus specifically on generating text, speech, image analyses, and document analyses based on user inputs. The diversity of OCI AI tools means that Oracle is a great solution for businesses that want to use AI to contextually analyze multiple data formats in their business. Outside of OCI, there is also Machine Learning in Oracle Database, a solution that adds in-depth AI-driven insights and support to users of various Oracle database products.

Pros and Cons

Pricing

Many Oracle services, especially those offered through OCI, are priced per unit used:

• Units may be training hours, transactions, transcription hours, inference unit hours, or requests, depending on selected products and services
• Several Oracle AI and ML services are also available through Oracle’s free pricing tier
• Contact Oracle’s sales team for more information

Key Features

• OCI Generative AI Agents feature
• OCI Digital Assistant with prebuilt conversational AI and chatbots
• Text analysis, speech-to-text, and text-to-speech services
• OCI Vision for image analysis and OCI Document Understanding for document analysis
• Machine Learning in Oracle Database with machine learning notebooks, monitoring, language-specific solutions, and more

The Future of AIaaS

With several key trends and developments, the future of AI-as-a-Service is promising. Here are some potential directions for the future of AIaaS:

• Increased Adoption and Democratization: As AI technologies continue to mature and demonstrate their value across various industries, the adoption of AIaaS is likely to increase in areas like healthcare, finance, retail, and manufacturing. As AIaaS becomes more accessible and user-friendly, it will empower non-experts to leverage AI technologies effectively. This democratization will enable a broader range of users and businesses to harness AI for various applications.
• Growth of Advanced AI Models: AIaaS providers will continue to develop and deploy more advanced and specialized AI models to tackle complex problems. This may mean AIaaS in the future may be capable of more autonomous taskwork.
• Edge AI and IoT Integration: With the proliferation of Internet of Things (IoT) devices, AIaaS will likely integrate more closely with edge computing capabilities to bring AI processing and inference closer to the data source. This shift will reduce latency, improve response times, and enhance privacy and security.
• More Industry-Specific Offerings and Specialization: While most AIaaS vendors currently offer more general services in generative and predictive AI or machine learning model development, a growing number of these vendors are specializing in specific industries and use cases. Expect this trend to continue and grow, especially as the competition for market share grows fiercer.
• Greater Emphasis on Ethical and Explainable AI: Consumer demand and a growing body of AI regulations–particularly the EU AI Act–are leading AI companies of all backgrounds to work on more transparent, ethical, and explainable AI. This will be especially important for AIaaS vendors now and in the future, as their stakeholders include both the companies subscribing to their services and any end users or consumers who may be impacted by murky or unethical use cases.

How to Choose the Best AI as a Service Company

Choosing the best AIaaS company to partner with can be difficult if you aren’t familiar with the services they offer and the services you actually need.

Industry-Specific Specialization or Experience

Several AIaaS companies specialize in certain industries and use cases, or they can at least demonstrate experience working in those industries. Particularly if you operate in a highly regulated industry, such as healthcare or the public sector, be sure to identify partners with the right experience and strategies for your needs.

Range of Products and Services

As a general rule of thumb, consider a provider that offers a wide range of AI capabilities, such as natural language processing, computer vision, machine learning, or deep learning, depending on your requirements.

Budget and Pricing Approach

Evaluate the pricing model and cost structure of the AIaaS provider. Consider factors such as subscription fees, usage-based pricing, or any additional costs for customization, training, or ongoing support.

Compatibility with Data and Infrastructure Requirements

Depending on the partner you select, you may be self-hosting or hosting solutions in their environments, you may be providing your own data or benefiting from their provided data sources, or making several other decisions about how your chosen services work with your specific data and infrastructure needs. Consider whether your preferred vendor is compatible in this way, and also consider if there will be any additional costs that come with them managing any of these components for you.

Customizability

Many AIaaS offerings can be customized or fine-tuned to your business use case or application requirements. If this is an important feature to you, talk to prospective partners about how they support smooth, user-friendly customizability and co-creation opportunities.

Scalable Platforms and Resources

As your organization’s AI needs grow, you’ll want to partner with an AIaaS provider that can scale with you. Look for providers with elastic pricing and resourcing structures, and be sure to discuss any other scalability advantages with prospective vendors from the outset so you know what to expect and what’s possible.

Ability to Integrate with Existing Tech Stack

Chances are, you’ll already have existing cloud, security, and SaaS applications and platforms that are a crucial part of your tool stack. The best AIaaS provider for your business is likely one that can set up smooth integrations between their products and services and the most important components of your existing tool stack.

Accessible and User-Friendly Customer Support

AIaaS is not just about the technology but about the service delivery. That’s why it’s important to evaluate customer support availability and quality, both by looking at the details of their support pages and reviewing customer reviews about customer support experiences.

Vendor Reputation

Your chosen AIaaS vendor does not need to be the biggest and priciest provider on the market. Often the scrappier and smaller AIaaS vendors will have more of the specialized solutions and hands-on care that you require. But it is important to find a vendor with a solid reputation that consistently meets customer expectations. To determine if the vendors you’re evaluating are high-quality partners, take the time to talk to existing customers, review case studies and testimonials, and look to other third-party sources of information about how that vendor operates.

Bottom Line: AI-as-a-Service Companies Democratize Access to AI

AI as a service companies offer businesses easier access to cloud-based artificial intelligence technologies, a feat that is otherwise nearly impossible for most companies to achieve on their own due to a lack of resources and limited internal expertise. As demand for this type of service grows, AIaaS companies are democratizing access to artificial intelligence and helping businesses discover new insights and automate digital processes without worrying about infrastructure or hardware investments.

As more companies realize the value of investing in AI to keep up with the competition across industries and sectors, expect AI as a service companies and their offering to continue evolving in maturity and capabilities.

To gain a deeper understanding of today’s top large language models, read our guide to Best Large Language Models

The post 12 Top AI-as-a-Service Companies: Powerhouses of Progress appeared first on eWEEK.

Databricks and Snowflake are two of the top data-focused companies on the market today, each offering their customers unique features and functions to store, manage, and use data for various business use cases.

Databricks got its start as a robust tool for configurable data science and machine learning projects, while Snowflake began as a cloud data warehouse solution with business intelligence and reporting capabilities.

The two have continued to roll out new features that have grown their impressive solutions portfolios and transformed them into direct competitors. Knowing how they compare on key features, pricing, ease of use, and other key areas can help your organization determine which might better meet your needs.

Databricks vs. Snowflake Comparison

The follow table shows how Snowflake and Databricks compare across key metrics and categories.

Databricks Overview

Databricks is a data-driven platform-as-a-service (PaaS) vendor with services that focus on data lake and warehouse development as well as AI-driven analytics, automation, complex data processing, and data science. Its flagship lakehouse platform includes unified analytics and artificial intelligence management features, governance capabilities, machine learning, and data warehousing and engineering.

The design of Databricks ensures that all AI, data, and analytics operations and resources are unified within the platform—primarily through Unity Catalog—which means fewer third-party tools are necessary to complete data and AI operations. This is especially effective if you’re working with unstructured, semi-structured, and structured data formats.

Users can access certain platform features through an open-source format. When this feature is combined with its Apache Spark foundation, Databricks offers a highly extensible and customizable solution for developers. It’s also a popular solution for data analysts and scientists who want to incorporate other AI or IDE (integrated development platform) deployments into their setup.

Key Features

Databricks stands out for a number of key features, including the following:

• Data Lakehouses: This unique storage approach was pioneered by Databricks to combine the strengths of data lakes and data warehouses into one infrastructure. With this approach, users can increase data governance and data storage capabilities while also reducing storage costs. In many cases, this infrastructure is also more flexible and compatible with data analytics operations than either a data warehouse or a data lake.
• Unity Catalog: This aspect of the Databricks Data Intelligence Platform provides users with a unified and open governance solution for data and AI. Users frequently select this tool because it allows them to organize, prepare, and operationalize their data—as well as their teams’ permissions—without needing third-party tools to do this work.
• Databricks Solution Accelerators and Notebooks: Prebuilt accelerators provide the notebooks, blueprints, and other resources necessary for teams that want to quickly get started with data analytics and data science projects. Accelerators are organized by industry and cover a lot of ground; Python-based notebooks are a huge favorite.
• Data Intelligence Engine: This feature runs in the background to support complex data operations that are customized to your exact data types and requirements. This engine enables semantic data understanding, easier data search and discovery, and natural language support for coding and troubleshooting.

Pros

• Pioneering data lakehouses and other scalable data stores and structures
• Unified approach to data cataloging, governance, and analytics eliminates tool sprawl

Cons

• Expensive and complex pricing approach with Databricks Units (DBUs)
• Highly technical platforms with steep learning curves

Snowflake Overview

Snowflake is a major cloud and data company that focuses on SaaS-delivered data-as-a-service functions for big data operations. Its core platform is designed to seamlessly integrate data from various business apps and in different formats in a unified data store. Consequently, typical extract, transform, and load (ETL) operations may not be necessary to get the data integration results you need.

The platform is compatible with various types of business workloads, including artificial intelligence and machine learning, data lakes and data warehouses, and cybersecurity workloads. It is ideally designed for organizations that are working with large quantities of data that require precise data governance and management systems in place or on-demand storage.

Compared to Databricks, Snowflake is better set up for users who want to deploy a high performance data warehouse and analytics tool rapidly without bogging down in configurations, data science minutia, or manual setup. But this isn’t to say that Snowflake is a light tool or for beginners. Far from it; it’s a highly advanced platform known for its clear user interface.

Key Features

Snowflake offers a number of key features that help it stand out from competitors, including the following:

• SQL Data Warehousing: Snowflake is a longtime leader in cloud data warehousing, offering a large-scale infrastructure that requires little to no maintenance on the part of the user. Its SQL base makes it particularly accessible to users of varying technical skill levels.
• Snowpark: This newer AI and ML feature is designed to support containerized application development and deployment. It’s also great for data engineering and data pipeline design.
• Marketplace and Partner Network: Snowflake has an extensive marketplace with products that span across categories, business needs, and price points. Its partner network is also impressive, offering dozens of strategic partners across AI data cloud, cloud services, and cloud platform infrastructure.
• Data Clean Rooms: The Data Clean Rooms feature takes role-based access control to more sophisticated and granular levels, making it possible to develop very specific audiences that can overlap or sit separately in whatever ways you choose. The setup makes it very easy to see levels and areas of access for different users.

Pros

• Strong and diverse marketplace for users
• Platform is generally easy to use and set up

Cons

• Less focus and fewer capabilities in advanced data science and analytics
• Limited experience with and maturity in AI and ML use cases

Best for Scalable Pricing and Performance: Depends on Use Case

There is a great deal of difference in how Databricks vs Snowflake are priced. But speaking very generally for the average business user: Databricks typically comes out to around $99 a month, while Snowflake usually works out at about $40 a month.

Again, it isn’t as simple as that, because each tool has different components and plans that have their own pricing variables. It’s especially complicated because each tool is priced per unit or credit used, which can be highly variable from month to month. To add more complexity to this problem, there’s a good chance you’ll also have costs associated with running some of these tools’ processes on AWS, Azure, or GCP.

Here’s a breakdown of what each of these pricing structures looks like:

Databricks Pricing

• Workflows: Starting at $0.15 per DBU
• Delta Live Tables: Starting at $0.20 per DBU
• Databricks SQL: Starting at $0.22 per DBU
• Interactive Workloads: Starting at $0.40 per DBU
• Mosaic AI: Starting at $0.07 per DBU

Snowflake Pricing

• Standard: Starting at $2 per credit
• Enterprise: Starting at $3 per credit
• Business Critical: Starting at $4 per credit
• Virtual Private Snowflake (VPS): Pricing information available upon request
• On-Demand Storage: $23 per TB per month

Snowflake keeps compute and storage separate in its pricing structure, so pricing will vary tremendously depending on the workload and the pricing tier you select. However, if you have pretty consistent storage requirements from month to month, Snowflake may be a more affordable solution.

Compute pricing for Databricks is also tiered and charged per unit of processing. As storage is not included in its pricing, Databricks may work out cheaper for some users. It all depends on the way the storage is used and the frequency of use.

The differences between them make it difficult to do a full apples-to-apples pricing comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements. For some users, Databricks will be cheaper, but for others, Snowflake will come out ahead.

This category is a close competition as it varies from use case to use case.

Best for Data Operations and Capabilities: Databricks

Snowflake is high performing for interactive queries as it optimizes storage at the time of ingestion. It also excels at handling BI workloads and the production of reports and dashboards, and it excels as a data warehouse. Some users note, though, that it struggles when faced with huge data volumes found with streaming workloads. It also has fairly limited data science and processing features built into the solution because of its emphasis on data warehousing and ease of use.

In contrast, Databricks isn’t really a data warehouse at all. Its data platform is wider in scope with better capabilities than Snowflake for ELT, ETL, data science, and machine learning. Users store data in managed object storage of their choice, allowing the platform to focus on data lake infrastructure and complex, high-volume data processing initiatives. It is squarely aimed at data scientists and professional data analysts and offers the complexity of tools necessary to handle a wide variety of their strategic tasks.

In a straight competition on data warehousing capabilities, Snowflake wins, but for virtually all other data operations and capabilities, Databricks is a more mature and capable solution.

Best for Working With Multiple Data Types: Databricks

While both Databricks and Snowflakes technically allow you to work with all data types, the process for getting there is quite different. Databricks is automatically compatible with all data types: structured, semi-structured, and even unstructured data all work in the platform. This is due to its lesser emphasis on data storage and greater infrastructure for data processing and data science. Users can input data in any format into the platform, and built-in ETL and ELT tools are available to make any formatting adjustments if necessary.

In contrast, Snowflake natively offers support only for semi-structured and structured data. It also does not have as much built-in ETL and ELT functionality to support any necessary data transformation work for unstructured data. However, its integration marketplace is incredibly robust and connected to many different solutions that can prepare unstructured data for use in Snowflake. So, if you are already using a separate ETL/ELT tool or are willing to invest in one, you’ll still be able to work with all different data types in Snowflake with relative ease.

While it is possible to work with all different data types in both Databricks and Snowflakes, Databricks takes the win due to its native compatibility with structured, semi-structured, and unstructured data.

Best for Support and Ease of Use: Snowflake

The Snowflake data warehouse configuration is user-friendly, with an intuitive SQL interface that makes it easy to get set up and running. It also has plenty of automation features to facilitate ease of use. Auto-scaling and auto-suspend, for example, help in stopping and starting clusters during idle or peak periods. Clusters can be resized easily.

Databricks, too, has auto-scaling for clusters. The UI is more complex for more arbitrary clusters and tools, but the Databricks SQL Warehouse uses a straightforward “t-shirt sizing approach” for clusters that makes it a user-friendly solution as well. Both tools emphasize ease of use in certain capacities, but Databricks is intended for a more technical audience, so certain steps like updating configurations and switching options may involve a steeper learning curve.

Both Snowflake and Databricks offer online, 24/7 support, and both have received high praise from customers in this area.

Though both are top players in this category, Snowflake wins for its wider range of user-friendly and democratized features.

Best for Security: Snowflake

Snowflake and Databricks both provide role-based access control (RBAC), encryption, and activity monitoring features to protect security and privacy in their platforms. Both data vendors also comply with SOC 2 Type II, ISO 27001, HIPAA, GDPR, and more.

In addition to these more standard security features, Snowflake maintains its own secure cloud infrastructure with continuous monitoring, independent security audits, and unique, more granular role-based access controls like Data Clean Rooms. Snowflake also adds network isolation and other robust security features in tiers, with each higher tier costing more. But on the plus side, you don’t end up paying for security features you don’t need or want.

Databricks, too, includes plenty of valuable security features, but it’s important to note that many of these features require users to do more configuration. Since Databricks is a more complex platform and requires more hands-on user intervention for security to work effectively, that may lead to Databricks having more security misconfigurations and gaps over time compared to Snowflake; obviously this relies on staff resources.

While both platforms offer a range of useful security features that are similar to each other’s solutions, Snowflake wins due to its more automatic and simple security configuration model.

Best for AI Features: Databricks

Both Snowflake and Databricks include a broad range of AI and AI-supported features in their portfolio, and the number only seems to grow as both vendors adopt generative AI and other advanced AI and ML capabilities.

Snowflake supports a range of AI and ML workloads, and in more recent years has added the following three AI-driven solutions to its portfolio: Snowpark, Streamlit, and Arctic. Snowpark offers users several libraries, runtimes, and APIs that are useful for ML and AI training as well as MLOps. Streamlit can be used to build a variety of model types — including ML models — with Snowflake data and Python development best practices. And Arctic offers Snowflake-built enterprise LLM models to users with an emphasis on open design and enterprise-ready infrastructure.

Databricks, in contrast, has more heavily intertwined AI and ML in all of its products and services and for a longer time. The platform includes highly accessible machine learning runtime clusters and frameworks, autoML for code generation, MLflow and a managed version of MLflow, model performance monitoring and AI governance, and tools to develop and manage generative AI and large language models.

Other AI-driven features include feature engineering, vector search, lakehouse monitoring, AI governance, and AI security. AI is intentionally embedded into all corners of Databricks, while Snowflake’s AI solutions essentially sit on top of or come as an add-on for their existing solutions.

While both vendors are making major strides in AI, Databricks takes the win here.

Who Shouldn’t Use Databricks or Snowflake?

Databricks vs Snowflake is an important comparison to make when considering an enterprise-ready data and AI solution for your business, but in some cases, neither solution will offer the features and usability you seek.

The following users might want to consider alternatives to Databricks:

• Users with little experience with or knowledge of Spark and Python
• Less technical users
• Users with predictable, smaller-scale storage requirements
• Users who want a straightforward, easy-to-use, and easy-to-configure solution
• Users who need completely predictable pricing structures
• Users who want prebuilt security features that require little to no implementation

The following users might want to consider alternatives to Snowflake:

• Users who want a completely unified approach to data storage, management, and analytics
• Users who want extensive machine learning functionality
• Users who need support and features for unstructured data
• Users with highly variable or large-scale data processing requirements
• Users looking for a highly customizable Apache Spark back-end
• Users who need completely predictable pricing structures

Best 3 Alternatives to Databricks and Snowflake

If any of the bullet points above felt relevant to your concerns when comparing Databricks vs Snowflake, we recommend considering alternatives such as Yellowfin, Salesforce Data Cloud, and Zoho Analytics.

Yellowfin

Yellowfin is an embedded analytics and BI platform that combines action-based dashboards, AI-powered insight, and data storytelling. With this solution, users can connect to all of their data sources in real time. It’s also possible to configure Yellowfin to allow multiple tenants within a single environment. Additionally, Robust data governance features are incorporated to ensure compliance. Many users select Yellowfin for its flexible pricing model that is simple, predictable, and scalable, as well as for its interactive visualizations that improve decision-making.

Salesforce Data Cloud 

Particularly for users who need advanced data solutions for marketing, sales, or service scenarios, Salesforce’s Data Cloud is a great solution to activate all your customer data across Salesforce applications. This solution empowers teams to engage customers at every touchpoint with relevant insights and contextual data in the flow of daily work. Companies use this solution to connect their data with an AI CRM; this simplifies the process of deriving relevant data and insights from your existing Salesforce processes and applications.

Zoho Analytics 

Zoho Analytics is a software solution that enables users to perform self-service business intelligence and data analytics operations. It is ideal for users that need an easy way to analyze content in various files, apps, and databases. Customers frequently praise the quality and usability of Zoho Analytics visual elements, including its user-friendly reports and dashboards. And, particularly for smaller teams and requirements, Zoho Analytics is an incredibly affordable data analytics solution.

How We Evaluated the Systems

While several other variables impacted research for this comparison guide, the following review categories framed our comparison through the lens of what matters most to Databricks and Snowflake customers.

Mature Data Management Capabilities | 50 percent

Considering both Databricks and Snowflake are enterprise-tier data platforms, I spent significant time researching the data operations and features that are possible with each platform. I looked most specifically at compatibility with different data formats, data storage infrastructure, data management and processing capabilities, data science features, data ownership, data operations scalability, data sharing approach, ETL and other data transformation capabilities, and how data operations integrate with ML and AI operations.

Ease of Use and Support | 25 percent

Because data processes can be complex, especially for less-technical teammates outside of the data analysts’ department, I also reviewed how each vendor made its platform more approachable and user-friendly. This component of my review focused on looking for a clean and accessible interface, natural language configuration capabilities, auto-configuration features, customer support accessibility and resources, customer reviews about ease of use and their general experience with the platform.

Enterprise-Ready Solutions and Growth | 25 percent

Ultimately, both Databricks’ and Snowflake’s existing features—not to mention the new data and AI features their vendors are pursuing—are designed for an enterprise audience with complex use cases and requirements. This is why a large portion of my research process focused on finding unique differentiators that indicated each platform’s scalability and ability to handle big data and complicated working scenarios.

I primarily looked for unique AI and ML features and a growing solutions stack in this area; a robust marketplace and partner network; sophisticated and comprehensive cybersecurity, privacy, and admin features; compatibility with third-party enterprise tools, especially major cloud platforms; customizability; and a unified interface that still plays nicely with other enterprise tools in the customer’s tech stack.

Bottom Line: Databricks vs. Snowflake Depends on Your Overall Data Strategy

Snowflake and Databricks are both excellent solutions for data analytics and management purposes, and each has distinct pros and cons. Choosing the best platform for your business comes down to usage patterns, data volumes, workloads, in-house expertise and ultimately, your company’s overall data strategy.

In summary, Databricks wins for a technical audience with high-level and dynamic requirements, while Snowflake is highly accessible to both a technical and less-technical user base. Databricks provides pretty much every data management feature offered by Snowflake, with several additional features for data science and processing. But it isn’t quite as easy to use, has a steeper learning curve, and requires more maintenance. Snowflake vs. Databricks should be a fairly straightforward decision to make, as their purposes and niches are relatively distinct and uniquely strategic.

For an in-depth look at the leading ML tools for enterprise use cases, see the eWeek guide: Best Machine Learning Platforms

The post Databricks vs. Snowflake (2024): Battle of the Best – Who Wins? appeared first on eWEEK.

fSales managers often use AI-powered sales forecasting to supplement their existing sales forecasting practices with more real-time and far-reaching data, risk management profiles, and recommendations for achieving better results.

When combined with sales team experts and the strategic use of CRM or other sales tools, AI sales forecasting opens up new opportunities for businesses to act on their sales data in future scenarios. In this guide, learn more about AI sales forecasting, how it works, how you can get started with this strategy, and what leading tools you might use.

Understanding AI in Sales Forecasting

AI models that are trained with advanced machine learning algorithms and large sales-specific datasets can provide highly intelligent supplements to marketing and sales forecasts and analytics.

Depending on available data sources and channels, artificial intelligence can identify and incorporate useful finance, chatbot, social media, and customer support conversation data into its analyses to make well-rounded predictions about future revenue, markets, and more.

AI forecasting tools are typically set up to run behind the scenes at all times, collecting new real-time data and updating existing data as conditions change. With this information, sales teams can make well-informed decisions at any point in their sales cycle and for a variety of data points.

To gain a deeper understanding of today’s AI software for sales, read our guide to AI Sales Tools and Software

AI Sales Forecasting vs. Traditional Sales Forecasting

In traditional sales forecasting, past performance data and metrics, various statistical models, and other business intelligence (BI) best practices are leveraged by sales professionals to predict how these same metrics will look in the future.

For example, traditional forecasting may predict a slump in Q2’s sales because the past three years have seen a similar dip. In contrast, AI forecasting takes these existing practices and moves beyond traditional forecasting’s capabilities with large AI/ML algorithms and models, as well as massive large language models.

With the algorithmic training that comes with AI forecasting, AI forecasting tools and solutions can take a more holistic look at all business sales data — including data that is not typically viewed as performance data — and make accurate predictions and recommendations for future outcomes.

For example, an advanced AI forecasting tool may be able to identify negative sentiments in chatbot messages and social media comments that focus on sizing for a particular clothing line; from there, the chatbot may predict a decrease in sales or an increase in returns for this clothing line unless appropriate adjustments are made.

For more information about how these two types of sales forecasting stack up, take a look at our pros and cons comparison:

To learn about AI for customer relationship management, read our guide: Top 8 AI CRM Software 

AI Sales Forecasting Use Cases

Although sales forecasting can be used in highly specific scenarios designed for your business’s sales goals, most AI sales forecasting projects fall into the following use case categories:

• Financial data processing and analysis: Reviewing financial data like revenue, budget spent on past marketing campaigns, cost of deals closed and lost, and more to predict future sales trends.
• Chatbot customer service: Real-time customer feedback and sentiment analysis used to identify fluctuations in product demand, customer acquisition, and customer satisfaction.
• Lead generation and performance scoring: Highly specific analysis and scoring of customers in general and as individuals, looking at different details of their buyer persona, demographics, browsing and purchase history, and social media interactions to determine if they are a worthwhile lead to pursue.
• Customer sentiment analysis: Reviewing the emotions, interactions, and overall behaviors of customers and prospective buyers across different channels, including social media, chatbots, call center transcripts, and customer reviews to determine if product shifts or redesigns are necessary.
• Predictive and prescriptive analytics: Beyond simply predicting performance outcomes based on past data and incoming data, AI sales forecasting tools can often offer recommendations for next steps based on these predictive analytics, whether that’s a possible shift in marketing strategies or an update to a specific webpage.
• Opportunity pipeline and lifecycle management: Instead of looking simply at individual deals and customers, AI forecasting tools can take a look at the sales pipeline as a whole to identify patterns in deal progression and slowdowns, which can help the team make adjustments for the future.
• Risk management: Many AI forecasting tools can look at past events and external data sources — including competitors’ web properties — to identify potential business risks and contingency plans to mitigate these risks.
• Rep-specific performance management and goal-setting: AI can get into the specifics of individual team members’ performance across different metrics, time periods, and channels. Looking not just at straightforward numbers, AI forecasting tools may be able to use sentiment analysis, sales pipeline health, and other murkier or subjective datasets to identify top players and salespeople who could use more training or support.

To learn more about how AI is used in business settings, see our guide: 15 Generative AI Enterprise Use Cases

5 Ways AI Improves Sales Forecasting

With AI as an assistive resource in sales forecasting, businesses can derive new, deeper, and more useful insights at scale. These are some of the benefits that businesses realize almost immediately after incorporating AI into their sales forecasting practices:

• Deeper, more nuanced insights from existing data sources.
• Real-time, accurate updates to predictive analytics and dashboards.
• Multichannel and external data analysis, including on social media.
• Enhanced customer experience focus, with deep reviews of customer interactions and demographics.
• Intelligent performance improvement recommendations, specifically with a shift from predictive analytics to prescriptive analytics and AI-powered recommendations for improvements.

10 Steps to Implement AI Sales Forecasting Solutions

Whether you’re investing in a third-party tool or building your own generative AI model to support sales forecasting work, you’ll need to follow these steps or similar ones to develop an operational and useful tool:

1. Organize and Prepare Data

As with any AI or algorithmic solution, the outcomes are only as good as the training and input data that goes in.

If your organization’s sales data is erroneous, biased, poorly formatted, or incomplete, these problems can severely limit the accuracy and utility of any AI sales forecasts you make. That’s why it’s important to start AI sales forecasting adoption by preparing your data to meet a high data quality standard.

This work may involve cleansing existing data, sourcing new data, finding new data sources, or having multiple team members assess data for biases that may previously have gone unnoticed. This step should take a thorough look at data quality from all angles to achieve the best outcomes.

2. Set Goals and Budget

It will be easiest to measure the success of your AI sales forecasts if you go into this work knowing exactly what you want to measure, how you want to measure it, and how frequently you want to measure it. Specific metrics ensure that AI sales forecasts are focused on the right goals while also helping your team more closely judge the accuracy of the tool’s outputs.

Once measurable, big-picture goals are set, you’ll want to consider what budget is available for AI sales forecasting tools. This will help you decide if it’s time to invest in an entirely new sales platform, work with your existing vendor to embed or integrate AI capabilities, or build or fine-tune your own models for specific use cases.

3. Research AI and Sales Tools

A variety of sales platforms, AI tools, and integrated AI sales solutions are on the market today.

Based on the goals and budget you’ve set as well as your existing tool stack and other preferences, you’ll be able to make an informed decision about the best AI forecasting tool for your business. Most businesses get top value out of investing in an all-in-one sales platform that includes built-in AI capabilities, or even an AI assistant or copilot.

4. Complete Demos or Free Trials

The previous research step should be supported with hands-on testing of your top tool choices. Whether that’s through a customized demo or a free trial, multiple members of your team should test out the tool and its different capabilities.

During this phase, it’s a good idea to not only get familiar with the actual platform but also any community forums, knowledge bases, training, and customer support resources that may be helpful down the road.

5. Adopt or Build a Modifiable Solution

The prep work is done and it’s time to officially implement and customize an AI forecasting solution to your specific needs. Regardless of whether you’ve selected a prebuilt tool or are creating your own sales forecasting AI model, you’ll want to prioritize building a solution that can easily pivot or scale as your teams’ expectations change over time.

6. Start With a Pilot Program

Theoretically, your new AI forecasting sales tool could replace all existing forecast and predictive analytics work, but the problem with this approach is that you won’t be able to easily roll it back if something goes wrong. Instead, start by adopting AI forecasting on a smaller scale for a specific project or use case.

For example, if you work in an e-commerce business, you may want to test the tool first for a specific product’s predicted revenue or a specific sales rep’s expected deal closings for the next two quarters. Focusing a pilot program at a more granular level makes it possible to pinpoint when and where something goes wrong. From there, your team can more effectively make adjustments that will make widespread AI forecasting adoption run more smoothly.

7. Get Buy-In From and Provide Training to Employees

While important stakeholders across teams should already have been involved in the decision-making process, it’s likely that many sales team members are just now learning about this new tool in their stack. Take the time to clearly explain what an AI sales forecasting solution can do, how it can supplement their existing work, and how it can free up their time for the more strategic and interesting work of sales and customer experience management.

From there, take the time to train each team member, going over more general capabilities and then providing role-based training on an individual or team basis. If you take the time to fully communicate and prepare your teams to use these tools well, you’ll not only develop more consistent forecasts but also be working with a team that is more confident in how these tools can support rather than replace them.

8. Develop Monitoring and Review Processes

AI forecasting tools may be prone to error, especially when you’re first getting started. There’s also the possibility of human input errors, data issues, security issues, or customer experience shortcomings. In all of these cases, having a comprehensive performance monitoring and review process will enable you to more quickly identify and address these problems.

For model or AI-specific performance management, human-in-the-loop review workflows are a great way to ensure that each model iteration is closely reviewed and approved by a human expert.

9. Embed or Integrate with Existing Sales Tools

If the AI forecasting tool you’ve selected is not a built-in component of your sales platform or CRM, you’ll likely want to more closely connect it to that platform for smoother workflows and efficiencies. Ideally, your earlier research has already informed you about whether your sales tools natively integrate with your AI forecasting tool.

10. Update Systems and Datasets as Necessary

As your business grows and your sales goals change, you’ll want to update the AI forecasting model’s parameters, data, and other variables to help it keep up with these shifts.

You’ll also want to adjust your tool or even consider moving to a new one if your real-time monitoring results show problems that need to be fixed. Making these updates periodically and regularly will ensure you are handed credible analytical results on a consistent basis.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps

3 Sales Forecasting Tools to Consider

AI sales forecasting tools may be standalone solutions or features built into existing sales platforms. These are some of the leading AI sales and forecasting solutions to consider today:

Salesforce

Salesforce is a leading cloud software suite that includes sales and marketing CRM capabilities as well as solutions to manage customer service experiences and business analytics. With Salesforce Einstein’s Sales AI, users can benefit from AI-powered predictive forecasting and other unique features like deal insights, call insights, a buyer assistant, and relationship graphs and insights.

Pipedrive

Pipedrive is a top CRM software provider that has recently added AI capabilities and features to its platform. Most significant to sales forecasting, the AI Sales Assistant is designed to support revenue forecasting with helpful reminders, suggestions, notifications, and updates to keep your team on track and aware of subtler opportunities.

Zoho CRM

Zoho CRM is a favorite CRM for business users of all backgrounds because of its focus on user experience. Zia, an AI companion, is a recent addition to the tool that helps users manage and optimize the data in their CRM. Some of Zia’s capabilities include conversion predictions, anomaly detection, intelligent automation, competitor alerts, and smart recommendations

7 Best Practices of AI Sales Forecasting

Similar to traditional sales forecasting processes, it’s important to approach your AI sales strategy with strong data management strategies and clear goals in mind. Here are some of the most important best practices for getting started and achieving success with AI sales forecasting:

• Don’t overlook data preparation: Prepare and maintain high-quality, well-rounded, and unbiased data, both for training and inputs.
• Blend AI with existing strategies and strengths: Strategically blend AI with traditional sales forecasting when the situation calls for it; don’t lose the strengths and unique perspectives that your sales experts can provide from years of experience.
• Follow data and AI ethics best practices: Source and use data ethically, especially when operating in industries or regions with strict compliance requirements.
• Focus on employee experience alongside customer experience and sales outcomes: Train team members on how to leverage new insights to make their jobs easier.
• Constantly monitor AI forecasting performance: A real-time monitoring strategy will help your team identify anomalies, performance aberrations, and emerging trends.
• Focus on outcomes and next steps: Don’t let the shine and excitement of a new piece of technology distract you from the goals it should help you achieve in the sales cycle.
• Improve and iterate on AI forecasting models and methods over time: Start small to make it easier to see what does and doesn’t work in short sessions of analysis.

Bottom Line: AI Boosts Sales Forecasting – With Human Help

With its speed, scalability, accuracy, and ability to look at sales data in-depth and from new angles, AI sales forecasting has become a top trend in the sales technology sphere for good reason. But is it “the future” of sales forecasting, or is it nothing more than a trend of the moment?

Right now, the buzz surrounding AI sales forecasting often discounts or downplays the manual effort that makes these tools run. Without sales and data experts preparing the right data, setting relevant goals and parameters, and reviewing performance regularly, AI sales forecasting tools would provide little value to the modern business. In short, salespeople should not worry about being replaced with AI; rather, they should prepare to upskill as their roles shift — AI will certainly take on some of the more tedious work that used to fill their daily schedules.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies

The post AI Sales Forecasting: Benefits and How-To Guide appeared first on eWEEK.

In this guide, learn more about how AI can be used in CRMs and how your organization can create AI-powered workflows that make sense for your customer relationship management goals and expectations.

What is AI in Customer Relationship Management?

AI in customer relationship management is the use of artificial intelligence software, algorithms, services, and best practices to optimize customer data analytics and lifecycle actions, typically within a CRM platform.

AI in customer relationship management is often used to improve and update customer data in real time, create more engaging customer experiences, expand data-driven knowledge for decision-makers, and automate different aspects of nurturing and working with the customer.

With AI in CRM, marketers, salespeople, and service representatives alike can better handle their day-to-day workflows with automations, integrations, and recommendations that are “smart” and based on up-to-date performance metrics and recent customer interactions.

To learn about AI for customer relationship management, read our guide: Top 8 AI CRM Software 

How CRMs Use AI to Improve Customer Experience

CRMS use data to improve everything from customer data to customer communications and outreach plans. These are some of the most common AI CRM use cases that focus on improved customer experience:

• Reporting and predictive analytics: AI technology is used to collect better data and derive more in-depth information about customer sentiment, performance problems, and other issues that may impact customer experience. From there, many AI analytics solutions can also offer AI-powered recommendations for how to address these shortcomings most effectively.
• Tailored content creation: Generative AI tools can be used to create dynamic web and ad content, personalized emails, and other types of outreach that focus on everything from a customer’s global region to their past buying history to reviews they’ve left on third-party sites. When content is created specifically for individuals, they’re more likely to feel connected and loyal to the brand that’s creating this content.
• Workflow automation: Particularly in the nurture stage of the customer lifecycle, AI can set up complex, always-operational workflow automations so these customers receive regular personalized communications that keep them engaged and satisfied with the service they’re receiving.
• Audience segmentation: A task that has often been done manually (and tediously) in the past, audience segmentation is now frequently handled by AI bots built into the CRM. Based on the data that’s available for each customer profile, AIs can expertly divide these customers into the funnels and outreach groups that make the most sense for their interests and buying history.
• Sentiment analysis: Instead of having a human scour third-party review sites, social media messages, and dozens of chat logs, AI technology can quickly scan through all of these data sources to identify overall sentiments and the sentiments of individual customers in your database. From there, it can make recommendations on any larger changes that need to be made or how to take a better approach based on a specific customer’s frustrations.
• Personalized product recommendations: With the data you’ve collected and that’s been identified over time, AI CRMs have enough knowledge of individual customers to make ultra-personalized product recommendations. This could include upselling and cross-selling based on recent purchases or activity. The likelihood of closing these kinds of deals is all about the relevance and timeliness of recommendations, which are two areas in which AI excels.
• Chatbots and virtual assistants: AI assistants can be used to support both internal employee work and external customer interactions. Especially because most chatbots and copilots are available 24/7, users can complete their work and customers can ask questions whenever it’s convenient for them.

How to Implement AI in Customer Relationship Management

Use CRMs with Native AI

While it is certainly possible to integrate or embed third-party AI technology into your chosen CRM platform, AI will run your workflows and initiatives more seamlessly if it is natively offered as part of the CRM software.

Many of the top CRMs today have built-in AI automation, workflow management, data analytics, and content management features, so take the time to research your own CRM — or prospective CRMs, if you’re on the market — to determine what capabilities are already built into the system. Using a CRM with inbuilt and proven AI functionality will make the adoption and implementation process go more quickly and smoothly.

Map Out Strategic Goals & Outcomes

Before getting started with AI and exploring its capabilities, set your sights on the customer relationship management goals you’re hoping to accomplish with the help of AI. These goals can be far-reaching and generic, but it helps to also set some more specific goals with tasks and initiatives that will help you achieve those outcomes.

Start with highly measurable goals that make the most impact on your business’s customers — and your bottom line — such as the following examples:

• Improve overall customer satisfaction by increasing the organization’s Net Promoter Score (NPS) by 10 points over the next eight months.
• Use AI chatbots to resolve 25% of customer service inquiries and concerns during the customer’s first interaction; make this possible within the first year of AI chatbot adoption.
• Increase customer retention rate by 5% within the year by using AI to improve lead scoring, identify churn risks, and develop targeted customer retention email campaigns.

Other strategic goals for AI in CRM may focus on AI analytics adoption, customer lifecycle management, task automation, and other areas of the CRM workflow where new efficiencies can quickly be realized.

To gain a deeper understanding of today’s AI software for sales, read our guide to AI Sales Tools and Software

Improve Data Quality Prior to AI Adoption

AI does its best work when its training and sourcing data is high quality. Artificial intelligence in CRM technology is a particularly unique enterprise use case of AI, as much of the work that it does focuses solely on your organization’s collected and stored data. This makes it all the more important for your team to prioritize data quality management: cleansing, deduplicating, fact-checking, and updating data are all important parts of this step in the process.

At this time, you’ll also want to consider if your data sources are updated and relevant. When working with customer and customer-service-focused data, it’s important to rely on data from all kinds of customer service channels, including websites, product pages, social media channels, and customer service phone calls and chats. You may want to get even more granular, looking at specific responses to outreach campaign emails, the details of past customer service tickets, and responses to customer surveys and third-party review sites.

Adopt AI Use Cases & Workflows Iteratively

Even if you’re working with a highly sophisticated CRM like Salesforce or HubSpot that offers AI bells and whistles across its feature set, it’s important to not get caught up in the excitement and instead approach AI adoption with cautious logic. Start with your most important goals that you set out before, and take the time to lay out a clear project plan for implementing and adopting this AI use case across your team(s).

The lessons you learn from initial rounds of AI adoption and change management will prepare you to tackle other areas of your AI CRM more efficiently and effectively. Iterative adoption takes time, but it ultimately saves most companies time, as they don’t have to go back to fix repeated errors too often.

Test & Monitor Performance

After initial adoption and implementation, you’ll want to monitor AI technology performance in your CRM to make sure it’s meeting your needs and not creating new errors or problems in your workflows.

While actual performance data may be measurable with usage data in your CRM’s settings, you’ll also want to get creative and look at how customers feel about their AI-based interactions. Make reviewing customer feedback — especially comments on the quality of AI bots — part of your process so you can immediately identify and address any issues customers are having with “nonhuman” support staff or communications.

Develop a Handoff Process for AI Assistants & Human Support Staff

If you’ve decided to use AI as part of your customer service chatbot workflows, don’t forget that AI technology still has its limitations, especially when AI chatbots are in the early days of training and learning your organizational data. Human support staff should remain available at least during regular business hours, as they will occasionally need to take over and handle customer service queries directly.

But even in after-hours scenarios, develop a system where unresolved AI customer service conversations can be passed to the next-available human support staff. Customers will quickly become frustrated if they feel like there’s no way for them to get in touch with an actual company representative for their more complicated questions, especially if the AI chatbot is giving canned or irrelevant responses to their questions.

6 Benefits of Integrating AI into CRM

Integrating artificial intelligence into your CRM can lead to benefits for the business as a whole as well as for key players on the team and the customers themselves. Here are a few of the ways in which both customers and businesses benefit from AI in CRM:

Smart & Timely Data Analytics

AI can support better data collection and cleansing methods while also getting more useful insights out of this data. In essence, AI in a CRM can help manage data and data analytics throughout the data lifecycle.

Most important, AI can operate in the background of all customer service channels at all times, meaning this data can be updated in real time. Having up-to-date, accurate, and diverse customer data at all times leads to more data analytics possibilities, particularly for businesses that want to identify and address any customer churn risks.

Time-Savings Through Workflow & Task Automation

Artificial intelligence can write emails and blog posts, handle customer service queries, organize tasks and action items, and set up strategic outreach campaigns that reach the right people at the right times.

All of these tasks would typically require human intervention on a daily or weekly basis, which takes significant time out of their work schedules. As AI is used to automate and take over employees’ most tedious tasks, they can spend more time working on high-level strategy and customer nurturing campaigns that lead to greater company growth and customer retention potential.

Revenue Growth Potential

Customers who are happier with the outreach and support they receive are more likely to become loyal, repeat customers or even brand advocates. When your brand develops this reputation among loyal customers, you’ll not only earn more of their business but will likely attract more customers who are interested in receiving the same levels of attention and care.

More Personalized Shopping Experiences

With the ability to connect multichannel customer data in nearly any format, AI-powered CRMs have all the data necessary to better personalize shopping experiences for customers. They are more likely to see relevant targeted ads, receive email campaigns on sales or deals that interest them, and get customer support resources that addresses the real struggles they’re having with your products or services.

In sum, AI enables a personal touch that is difficult to replicate on such a large scale with limited human workforces.

Less Spam

AI is frequently used to target CRM outreach campaigns at prospective buyers and current customers who are most likely to be interested in that specific message and buying suggestion. This means customers receive fewer unwanted, spam messages from your organization; they’ll be happier with the communications they do receive from you, and you’ll likely get more engagement from those messages as well.

Real-time Support Services

AI chatbots aren’t limited to a 9-to-5 job schedule or the hours a business can afford to staff. In most cases, AI chatbots can stay up and running at all hours, leading to no additional costs for businesses.

However, customers certainly benefit, as they can get answers to many of their most pressing questions at any time of the day. This is an especially effective feature for global businesses that support customers in varying time zones.

3 Leading CRM Solutions Using AI

HubSpot

HubSpot is a leading CRM platform that divides its system into core hubs: the Marketing Hub, Sales Hub, Service Hub, Content Hub, Operations Hub, and Commerce Hub can all be used together or independently.

Within the Service Hub section, AI focuses primarily on offering real-time customer support and smart insights to business users. HubSpot’s AI features include different generative AI tools to create content for web, email, and social media; AI summarization; brand voice management; chatbot building; and unified customer record management.

Pipedrive

Pipedrive is a sales-focused CRM platform that combines some of its own AI technologies with OpenAI products to create AI-driven solutions and experiences. While Pipedrive’s AI solutions mostly support sales workflows and use cases, Pipedrive’s AI email generator and email summarization can both be used to create and manage content for customer service scenarios.

Freshsales

Freshsales is a sales CRM that also includes robust customer service and relationship management capabilities. With its Freddy AI collection of AI features, business customers can benefit from intelligent self-service chatbots, the Freddy Copilot for operational support and task management, and Freddy Insights for data-driven decision-making and risk management.

Bottom Line: AI is Improving Customer Relationships

For many years, customers have dreaded the possibility of needing to interact with a “robot” to get the answers they want when online shopping. While this dread still exists at some level, the development of AI and generative AI are offering drastic improvements, making it increasingly difficult for customers to tell the difference between a human-driven and an AI-driven interaction; AI tends to give them the answers they need with minimal human intervention.

Moreover, AI has reached the point where it can surpass human customer service representatives’ abilities in certain key areas, including collecting and applying real-time customer data and automating various outreach and lead management tasks. Expect AI to continue its rapid evolution in this sector and within CRM software, especially as businesses continue to realize how more tailored customer data and experiences lead to more revenue.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies

The post AI In CRM: How AI is Reshaping Customer Experiences appeared first on eWEEK.

Businesses from all different industries are incorporating new enterprise AI use cases in their workflows to improve products and disrupt their respective industries. To keep up with the competition, business leaders need to develop an AI business strategy that addresses their unique business model while helping them keep pace with industry-wide digital transformations.

In this guide, we’ll walk you through eight key steps of crafting a top AI business strategy. We’ll also cover some of the greatest benefits and biggest challenges that come with adopting AI as part of your business’s operational framework.

1. Identify Current Performance and Technology Gaps

Artificial intelligence (AI) tools and strategies can be infused throughout your business operations, but chances are, there are a few key areas where AI will make the highest-value impact in your organization.

To determine where these gaps are, start by looking at your legacy tools and applications, as well as any performance data or support tickets that indicate recurring problems with those systems. Additionally, assess the size and quality of different departments and teams, paying close attention to any resources they’re missing that would make their work more efficient.

Finally, look at things from the investors’ or customers’ perspective and ask this question: What current performance gaps are impacting their experience or the bottom line?

Asking these questions, completing a deep audit of your current resources and processes, and documenting your most crucial gaps is an important first step toward determining which AI solutions, partners, and investments are most strategic for your business.

To develop a plan to improve performance, it’s critical to create metrics. The chart above illustrates the various metrics that can be used to estimate the ROI of an AI investment. Source: Gartner

2. Research the AI Technology and Services Landscape

Once you’ve decided which areas of your business could most benefit from AI tooling and strategy, it’s time to look at the AI technology landscape and what it has to offer.

Depending on your internal skill sets and budget, you may choose to invest in:

• A free AI tool, an open-source solution, or a fine-tuned existing AI model
• A managed large language model
• Or even build your own generative AI model (though this is a particularly resource-intensive and expensive approach)

There’s also a wide range of prebuilt AI tools that won’t require you to adjust any deep learning algorithms or training data. Instead, you’ll simply work with the vendor or their platform to adjust the AI to your specific needs. You’ll also need to determine if you want to invest in more generic AI solutions — such as chatbots, copilots, and AI automation tools — or if you’re interested in a more specialized solution that is designed for your industry or a enterprise specific use case.

All of these approaches are valid but may not be the best route for your business. To make the best possible investment, spend some time researching leading AI vendors, big and small, assessing their individual products, longterm roadmaps and goals, and the additional resources they provide to support their customers.

It’s also valuable to look at customer reviews on third-party review sites, ratings from technology research firms, and the investors that are currently backing some of these technologies. Through each of these portions of your research, return to this question: Does what I’ve learned about this vendor, product, or service align with our business’s goals and way of doing things?

3. Set SMART Goals for AI Adoption

You’ll likely have a basic idea of your AI adoption goals and desired outcomes at this point, but you’ll stand the best chance of reaching those goals if you spell them out. There are several different ways to do this, but SMART goals provide a straightforward and highly objective way to measure how well you’re staying on track.

SMART goals are:

• Specific: The goal should very clearly state what you want to accomplish and on what timeline. It’s important to include numerical measures of progress and deadlines so everyone understands what the goal is. For example, “increase online conversion rate for sales of personalized AI product recommendations by 10% in the next eight months” is a highly specific SMART goal.
• Measurable: Again, numbers are important for these types of goals, because you’ll only know if you’re on track if there’s a way to measure your progress toward milestones. In the example we’ve listed above, the numbers are all there, and the team can easily track which conversions are happening due to AI-powered recommendations.
• Achievable: AI adoption will fall apart if you don’t set realistic goals for your team to act on. In the case listed here, this goal should be achievable if enough team members are aware of and committed to resourcing it.
• Relevant: Just because AI can do a certain task doesn’t mean that it’s the most efficient way to use it in your business. Be sure to set goals that are relevant to your business operations and longterm revenue goals, or else you’ll end up wasting time on a less-valuable project. This SMART goal is only valuable for businesses that are hoping to increase their product revenue in this way.
• Time-bound: SMART goals should include a realistic timeline for completion. In the details or subtasks that roll up into this goal, consider including monthly or weekly check-ins so progress and bottlenecks can all be assessed.

Your team may have one or several SMART goals like this one, depending on how many AI projects you’re working on at once. A helpful way to organize and visualize all of these goals as part of a bigger picture is through a planning roadmap. And while these goals may look a little different, it’s worthwhile to spell out any AI ethics or compliance goals too, so your team won’t forget about them in the middle of implementation.

4. Partner With Strategic AI Vendors

AI vendors come in all shapes and sizes. There are massive enterprises that were some of the earliest pioneers of AI technology, there are small AI startups that focus on a specific product or use case, and there’s companies that do a little bit of everything for AI products and services.

While it isn’t realistic to research every AI company out there, it’s smart to look at a variety of players to see who would be a strategic partner for your business. In many cases, the biggest name isn’t the most aligned or experienced with what you want to do.

The best way to ensure your AI investments work across all departments and functions is to bring key leaders, managers, and stakeholders from each group into the decision-making process. Consider having them complete a demo or trial period, share their perspective on what is and isn’t working with their current tech stack, and gain more operational data to secure a well-informed partnership or a well-rounded purchase.

5. Develop and Follow an AI Implementation Plan

Realistically, you’ve already completed several steps in creating your AI implementation plan, especially if you were thoughtful while writing out your AI adoption goals. Now, it’s time to figure out the exact details for executing a successful AI rollout.

You’ll want to first prep all key aspects of your internal operations — tools, data, and teams — for AI adoption. With your existing applications and data, this step may involve cleaning up or reformatting data so it works with new tools. With your teams, you may need to take some time to share your tooling and timeline decisions with them so they know where they fit into the AI implementation schedule.

Several examples of AI action or implementation plans can be found online, but ultimately, you know what makes the most sense for your team. Source feedback from your employees, talk with your vendors about what’s possible, and don’t be afraid to adjust your plan if needed along the way.

6. Create Cross-Functional AI Training and Change Management Programs

AI business strategies, no matter how strategic, will fall apart if your teams are unaware of or uninvested in your hoped-for outcomes. That’s why it’s important to train all employees on how AI will impact their role and how they can best use it for success. This can be a particularly sensitive step in an AI business strategy, as some employees may fear they are being replaced by AI.

To address and mitigate these fears, make sure that your change management program offers retraining and professional development resources that can help these employees feel confident if they need to up-skill. Fortunately, many AI vendors provide customers with extensive knowledge bases, learning resources, and even training academies and certifications that can help. These resources are available at all times, so when new employees come aboard or existing employees need a refresher, go back to these resources to keep things running smoothly on all fronts.

7. Track AI Performance Metrics

During and after AI implementation, your business should regularly track AI performance through the metrics that matter most to you. For example, if you’ve adopted an AI healthcare assistant or agent, your metrics may look something like this:

• Patient satisfaction with AI agent interactions
• AI agent’s response timeliness
• AI agent’s response accuracy
• AI agent’s adherence to HIPAA and privacy standards
• Number of patient interactions with AI agents over time

However, if you’ve invested in an AI data analytics platform to support your marketing and sales teams, your metrics may be more like this:

• Quality of predictive insights
• Quantity of predictive insights
• Relevance and actionability of AI recommendations
• User satisfaction with AI and data explainability
• Performance speed and accuracy with larger datasets

As you can see, these metric sets are quite different from each other. But they’re similar in that they each focus on both quantitative and qualitative measures of success. Regardless of what type of AI tool you use, be sure to select a wide variety of useful metrics and measure often; these measurements will help you determine if an AI app needs updating or a team needs retraining for better outcomes.

8. Adjust AI Solutions and Plans Periodically

What AI looks like today is not what it will look like tomorrow. And what your business looks like today is not necessarily what it will look like tomorrow or a month from now. Additionally, the AI tooling and regulatory landscape is changing at a rapid and constant rate, so it’s important to keep your AI implementation and adoption plans iterative and agile.

If you adopt and test AI solutions on an iterative basis while also keeping up with how AI and industry-specific regulations are evolving — as well as how your customers and the general public’s views on AI ethics and usage change over time — you’ll be prepared to shift your approach quickly and keep your company aligned with the best possible outcomes.

For a deeper understanding of AI compliance issues, read our guide: AI Policy and Governance: What You Need to Know

7 Benefits of AI Business Strategy Planning

The potential benefits of AI grow significantly when AI is accompanied by an effective business strategy. These are just a handful of the benefits that come with comprehensive AI business strategy planning:

• More strategic AI adoption and usage: An AI business strategy plan provides clear details about who should use what AI tools, when and why they should use them, and how they should use them most effectively. This comprehensive plan helps your whole team adopt the right solutions and ensure they actually get used in a way that consistently benefits the business.
• AI risk management and disaster recovery planning: An AI strategy doesn’t just cover what tools you’ll be using but also delineates what structures and safeguards will need to be established for success. The preplanning that comes with AI business strategy leads many business leaders to prepare a more effective risk management and disaster recovery plan, which may even extend beyond your AI tools to better protect the rest of your business applications and operations.
• Responsible and ethical approach to AI adoption: AI business strategies help you to think about all of the ways AI can impact your business, both good and bad. Proactive planning and ideating for AI is the best way to ensure you make responsible and ethical decisions that consider the needs of your employees and customers alike.
• Cross-functional and cross-disciplinary AI adoption: Without an AI business strategy, individuals or individual departments may adopt AI tools without much thought given to who else could benefit from these technologies. An overarching AI strategy helps the entire business identify useful tools and how they can be used effectively in different roles and divisions of the company.
• Competitive edge: Businesses that flesh out their AI strategies will have a clear picture of what they want to accomplish and what steps they need to take to get there. While other competitors may simply start using AI and run into performance issues or bottlenecks due to poor planning, businesses with an AI strategy will avoid many of these pitfalls and pass their competitors quickly.
• Automation and productivity support: An AI strategy assists leaders in identifying where current performance gaps or challenges are hindering productivity in the business. From there, they can select the AI software that is most likely to solve these issues, automate complex workflows, and otherwise improve the day-to-day operations of the business.
• Enhanced customer experience and customer insights: Businesses typically start their AI journey with internal solutions to help their employees’ productivity, but with an AI business strategy, they may more quickly identify how AI can improve customer experiences.

7 Common Challenges of AI Business Strategy Planning

AI business strategy planning is a difficult process, especially if you don’t get the right people and solutions in place from the outset. These are some of the most common mistakes and challenges that businesses face when working on AI business strategy plans:

• Making smart cross-functional AI investments: Technology investment decisions are often made from the top down. But with AI technology that is designed to be embedded and incorporated into multiple parts of business operations, this approach may mean you invest in a tool that is not a good fit for certain employees’ day-to-day responsibilities. To avoid this, you should create a cross-functional decision-making team for AI business strategy planning.
• Preparing internal data and operations for AI: Many organizations spend all of their time researching and selecting the right AI tool for their business but never consider all of the work that should go into preparing their data, workflows, and other business assets for AI. Businesses that fail to prepare their data for AI may end up with poorly-trained or ineffective AIs, or worse, an AI that has been exposed to PII or PHI in a noncompliant manner.
• Setting and sticking to reasonable AI goals: AI is an exciting business prospect, and many business leaders are tempted to implement AI solutions across their operations all at once. However, this method can lead to implementation errors and limited utility, as you’re giving your teams minimal time to edit and fact-check AI additions. It’s a good idea to set iterative AI implementation goals, giving your team a chance to learn how these solutions work and optimize them before moving on to the next big thing.
• Considering AI from important ethical and privacy angles: Whether you’re in a highly regulated industry or not, working with AI can be risky because of how some of these models train with and otherwise expose private data and business processes. Businesses should steer clear of AI companies that are unable or unwilling to explain their data collection and training processes; they should also work closely with these vendors to determine what additional AI privacy security safeguards need to be added to protect their data when using AI tools.
• Identifying and enforcing change management best practices: Businesses often make the mistake of subscribing to an AI tool and then letting it collect dust while employees continue to manage their workflows as usual. To make sure AI investments are threaded through your operations effectively, you must first determine what AI should support and why and how this support should be offered. From there, it’s important to train and retrain all impacted workers on how to work with AI in their roles.
• Addressing new security challenges: Because of how AI technology works with data and complex training algorithms, these models can expose your data and operational infrastructure to new security risks. Many businesses fail to prepare for these new attack surfaces, which can lead to severe breaches and data theft or loss.
• Staying up-to-date on changes in AI technology and regulations: The AI technology landscape — and the regulations that are trailing just behind — seems to change on a minute-by-minute basis. If your organization doesn’t keep up with these changes, you may find yourself in a position where you’re using a tool or working with a company that is operating unethically or is noncompliant with regulations that impact your organization. It is your responsibility to stay abreast of regulatory changes and confirm that your AI vendors and tools are in alignment.

Frequently Asked Questions (FAQs)

Why Do You Need an AI Strategy?

Businesses need an AI strategy because investing in AI technologies can be an expensive, risky, and time-consuming practice. Going in with clear objectives and a strategic framework for AI investment will help your team identify the best solutions and get them operational with minimal hassle. This strategy will also help you determine if AI companies, technologies, and methodologies align with your organization’s culture, long-term goals, and ethical and legal expectations.

What Is an Example of an AI Strategy?

An example of an AI strategy is the structured framework, objectives, and measured steps that go into an AI implementation project like deploying a customer-facing AI chatbot on your e-commerce site.

To do this effectively, your organization should follow an AI strategy to get the right stakeholders involved, establish a clear overarching goal, set up objectives and deadlines, determine steps and initiatives to move in that direction, and define metrics to measure the overall success of this strategic implementation.

In the case of the example listed here, this will involve actions like:

• Involving marketing, sales, IT, and product team stakeholders in decision-making
• Setting a goal for how you want the AI chatbot to interact with customers and pass off conversations to human agents
• Developing the steps and investments your organization needs to reach this goal
• Measuring the AI solution’s success with metrics like response accuracy, response speediness, and customer satisfaction

How Do You Implement AI into Your Business?

You should implement AI into your business through an iterative and ongoing strategic process. As business priorities, budgets, stakeholders, and the AI landscape change over time, it will be important to watch for these shifts and make changes to your AI tooling strategy accordingly. Taking measurable, distinct steps in your AI adoption journey will make it easier to pivot.

Bottom Line: Developing an AI Business Strategy That Works for You

AI business strategies should be custom-fitted to your organization, though the steps covered above provide a useful framework for getting started. Ultimately, you know what your business’s weaknesses are and what areas can most benefit from AI adoption. If you don’t know where these weaknesses are now, it’s time to start the internal discovery process and speak directly with internal stakeholders so you can identify where AI support is most needed.

When you begin to develop your AI business strategy, start by reflecting on what’s happening in your particular organization, industry, talent pool, and tool stack. All of these variables should influence the AI partnerships and tools you select, especially as many AI vendors are beginning to specialize in highly specific niches and use cases. If your workforce has limited AI experience or technical knowledge, it may be wise to research and partner with an AI-as-a-service or AI consulting company that has experience with your industry and the goals you are trying to accomplish.

To learn where the next generation of AI companies are headed, see our extensive overview: Top 75 Generative AI Startups

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