The CMA had been investigating whether Microsoft’s growing involvement with OpenAI amounted to a takeover, which could have raised competition concerns. However, after extensive analysis, the regulator announced on Wednesday that Microsoft holds “material influence” over OpenAI but does not have “de facto control.”

No change in control, says CMA

The investigation was triggered by OpenAI’s leadership shake-up in November 2023, when CEO Sam Altman was briefly ousted and later reinstated. This raised concerns about Microsoft’s influence, given its financial backing and deep integration with OpenAI’s AI models in its products.

After reviewing extensive documentation and consulting both companies, the CMA concluded that while Microsoft plays a significant role in OpenAI’s operations — especially in terms of funding, technology, and cloud computing power — it does not outright dictate the company’s policies or direction.

“Looking at the evidence in the round (including the recent changes), we have found that there has not been a change of control by Microsoft from material influence to de facto control over OpenAI,” said Joel Bamford, executive director of mergers at the CMA. “Because this change of control has not happened, the partnership in its current form does not qualify for review under the UK’s merger control regime.”

A win for Microsoft, but not a ‘clean bill of health’

Despite the CMA’s ruling, the regulator was quick to clarify that the decision does not mean Microsoft’s AI dealings are free from competition concerns. Instead, the agency emphasized the need for ongoing vigilance in monitoring the fast developing AI sector.

Bamford wrote, “The CMA’s findings on jurisdiction should not be read as the partnership being given a clean bill of health on potential competition concerns; but the UK merger control regime must of course operate within the remit set down by Parliament.”

The U.K. authority acknowledged that the prolonged nature of the review — stretching over 14 months — was due to the complex and evolving nature of the Microsoft-OpenAI relationship. As recently as January 2025, Microsoft adjusted its contractual agreements to lessen OpenAI’s reliance on its computing infrastructure, a move that likely played a role in securing the CMA’s approval.

The CMA’s clearance marks a regulatory win for Microsoft, which has been facing increasing scrutiny over its AI ambitions. In the U.S., the Federal Trade Commission (FTC) has raised concerns that Microsoft’s partnership with OpenAI could reinforce its dominance in cloud computing and give it an unfair edge in the AI race.

Regulators still watching AI deals closely

The CMA’s ruling reflects the broader regulatory debate around big tech’s growing influence in AI. The watchdog has been keeping a close eye on major AI investments, recently clearing Google’s and Amazon’s partnerships with AI startup Anthropic.

Despite the clearance, regulators worldwide remain cautious about how tech giants are shaping the future of artificial intelligence.

The post $13B Microsoft-OpenAI Deal Finally Gets UK Regulators’ Approval appeared first on eWEEK.

AI agents are designed to automatically complete tasks without users having to prompt them. Basically, agentic AI can execute actions without a user having to perform a specific triggering event first. Agentic AIs are a particular subtype of AI tools that focus on automating routine tasks.

New AWS group for agentic AI

According to the email, the new group for agentic artificial intelligence will be led by executive Swami Sivasubramanian, whose title on LinkedIn is VP of AWS Agentic AI. Sivasubramanian will report directly to AWS CEO Matt Garman, who wrote the internal email sent to AWS staff.

“Agentic AI has the potential to be the next multi-billion business for AWS,” Garman allegedly wrote in the email. He went on to say that, “We have the opportunity to help our customers innovate even faster and unlock more possibilities, and I firmly believe that AI agents are core to this next wave of innovation.”

Amazon’s recent push for agentic AI includes Alexa+

This new AWS group isn’t Amazon’s first foray into the world of agentic AI. Last week, the company announced that new agentic AI capabilities would soon be coming to Alexa, the company’s voice assistant AI tool.

The newly upgraded version will be called Alexa+, to distinguish it from the earlier versions. Alexa+ will reportedly be able to perform certain actions on its own even if users don’t issue a specific voice command or ask a question.

The Alexa+ service is free for Prime members but will cost $19.99 per month for non-members. Alexa+ will become available to U.S. households soon, starting with certain models of the HD smart display Echo Show devices. The first Echo Show models to get access to the agentic AI Alexa are 8, 10, 15, and 21.

More AWS reorganization in the works

The creation of the agentic AI group won’t be the only change at AWS. In another internal email sent on Tuesday, AWS senior vice president Peter DeSantis revealed several additional reorganizations are in the works. Three groups – hardware engineering, the Bedrock AI group, and the SageMaker AI group – will be moved underneath the compute organization. A new group will also be formed by combining customer experience and commerce.

The post ‘Next Multi-Billion Business for AWS’ Could Be Agentic AI appeared first on eWEEK.

Although it requires thousands of workers to build these facilities, “The reality is data centers just don’t employ that many people,’’ wrote Tom Dotan, a reporter for The Wall Street Journal, wrote on LinkedIn about his related WSJ article.

Fact vs. fiction about full-time data center jobs

Data centers have been touted by politicians and business leaders as a new avenue for employment. When President Trump discussed OpenAI’s Stargate AI venture during a recent press conference, he said that more than 100,000 new jobs would be created “almost immediately.” OpenAI echoed that in a blog post, saying Stargate would “create hundreds of thousands of American jobs.”

However, Dotan cited a one million square foot facility in Abilene, TX that OpenAI is planning to use for its Stargate AI venture that will employ 1,500 people to build it but is projected to employ only 100 people full time. “That’s one-fifth the number of people who will be working in a nearby cheese packing plant that is a fraction of the size,’’ Dotan wrote.

“Data centers are very labor-intensive to build, but not as labor-intensive to operate,” according to Jim Grice, a real estate and project finance attorney who focuses on data centers, as quoted in the WSJ article.

Synergy Research Group Chief Analyst John Dinsdale said in Dotan’s WSJ article that, while data centers can employ more than 1,000 people in the several months or years it takes to build them, it is rare for them to need more than 100-200 once they open.

Regional economic impact of these data centers is limited 

These sentiments are echoed by Good Jobs First, a Washington, D.C.-based nonprofit that tracks the use of state and local economic development subsidies. “Data centers require large amounts of capital investment, but, unlike manufacturing projects, they create few jobs,’’ the organization wrote in a blog post.

In addition, “Besides electricity and water, data centers buy very little from local communities, so their economic impact on a region is limited,’’ Good Jobs First said.

OpenAI’s estimate of the total jobs resulting from Stargate includes ones created indirectly by company and employee spending in communities, according to a company spokeswoman.

The post WSJ Debunks AI Data Centers Jobs Myth appeared first on eWEEK.

Hunyuan Turbo S is capable of replying to user inputs and queries within one second, which is even faster than DeepSeek-R1, according to the company and as reported by Reuters. We haven’t found any speed benchmarks that confirm Tencent’s claim.

How Hunyuan Turbo S compares to competitors in the benchmarks

According to benchmarks provided by Tencent as reported by WinBuzzer, Hunyuan Turbo S leads many competitors in a variety of areas. The following benchmarks are commonly used to evaluate the functionality, efficiency, and accuracy of large language models (LLMs).

• Chinese: Hunyuan Turbo S ranks the highest in Chinese language benchmarks performed by CMMLU, but DeepSeek-R1-Zero leads in C-Eval’s benchmarks.
• Alignment: Although Hunyuan Turbo S outperforms GPT-4o, Claude 3.5, Llama 3.1, and DeepSeek-V3 in benchmarks from LiveBench, it lags slightly behind Claude 3.5 in benchmarks from IF-Eval.

Some of Hunyuan Turbo S’s weaknesses include:

• Math: Hunyuan Turbo S outperforms GPT-4o, Claude 3.5, Llama 3.1, and DeepSeek-V3 in some benchmarks, but DeepSeek-R1-Zero leads them all as scored by AIME 2024 and MATH.
• Knowledge: Hunyuan Turbo S ranks fairly high on most knowledge benchmarks, but it doesn’t quite match up to DeepSeek-R1-Zero in the benchmarks from MMLU, MMLU-Pro, and SimpleQA.
• Reasoning: Hunyuan Turbo S only ranks third-highest, behind GPT-4o and Claude 3.5, on BBH’s reasoning benchmarks.
• Code: While HumanEval has Hunyuan Turbo S sitting right behind Claude for coding capabilities, it’s a bit further behind DeepSeek-V3, DeepSeek-R1-Zero, and GPT-4o on LiveCodeBench’s results.

While Hunyuan Turbo S is the clear winner in certain cases, it still falls behind DeepSeek-R1-Zero in several instances.

A strong competitor in the AI race

Tencent’s new Hunyuan Turbo S platform solidifies the Chinese tech giant’s position in the race to develop the fastest and most powerful AI platform. Although it’s not Tencent’s first foray into the world of generative AI tools, it is the company’s most noteworthy entry to date – and it’s certainly one to watch over the coming weeks, months, and years.

The post Tencent’s New DeepSeek Competitor Looks Promising Based on Key AI Benchmarks  appeared first on eWEEK.

iPhone users publicly document the mistake

Reports of the mistaken transcription first surfaced on Tuesday. iPhone users recorded videos of themselves saying the word “racist” aloud while the dictation feature was enabled. Apple’s AI model would initially write “Trump” before quickly correcting to “racist,” the actual word that was said.

After more and more iPhone users said they were also experiencing the issue, publications including The New York Times were able to replicate the error.

Apple blames swap on phonetic overlap

The issues appeared to begin after an update to Apple’s servers earlier in the week. Apple attributes the transcription error on a phonetic overlap between words that contain the r consonant, saying that it’s difficult for the dictation AI tool to distinguish between them.

“We are aware of an issue with the speech recognition model that powers Dictation and we are rolling out a fix today,” Apple told The Associated Press on Wednesday.

Apple said that other words that contain the r consonant could also cause the same bug. eWeek was not able to replicate the transcription error on Thursday, which suggests that the fix has indeed been rolled out to iPhones.

Former Siri team member casts doubt on Apple’s explanation

Not everyone believes Apple’s official statement, however. John Burkey, a former member of Apple Siri’s team who is still in “regular contact” with his former colleagues, told The New York Times that the bug seemed more like a deliberate prank than an accidental error, in his opinion.

Burkey said the fact that the word corrected itself indicates that a change was made to either the code or the data that powered the speech recognition AI tool. “This smells like a serious prank,” Burkey said. “The only question is: Did someone slip this into the data or slip into the code?”

The transcription error comes on the heels of Apple’s announcement earlier this week to invest more than $500 billion and hire more than 20,000 people in the United States. The tech company also announced plans to build a new factory in Texas, likely in response to Trump’s tariff on Chinese imports, which include iPhones and other Apple products.

The post Apple Blames AI Glitch After Dictation Replaces the Word ‘Racist’ With ‘Trump’ appeared first on eWEEK.

The last of the non-reasoning models

GPT-4.5 does not feature advanced reasoning capabilities like OpenAI’s o1 or o3, meaning its responses may lack the depth of its counterparts. It is also not GPT-5, which OpenAI CEO Sam Altman described in early February as a “system that integrates a lot of our technology.” Altman further said that GPT-4.5 will be OpenAI’s final model before transitioning to reasoning-based AI.

Starting Feb. 27, ChatGPT Pro users can access GPT-4.5 through the model picker. Plus and Team users will gain access the week of March 3, followed by Enterprise and Edu users the week of March 10. The model is also available through OpenAI’s Chat Completions API, Assistants API, and Batch API. However, OpenAI cautions that GPT-4.5 may not remain in APIs for long due to its high cost and uncertain role in the company’s GPT-5 roadmap. User feedback will help determine its future availability.

OpenAi emphasized that GPT-4.5 is not a replacement for GPT 4.0.

What is supervised learning?

Supervised learning trains AI models using labeled datasets to improve accuracy and responsiveness. OpenAI describes it as one of two “axes of intelligence,” alongside reasoning. Compared to a reasoning model, a supervised learning model excels at handling general, consumer-oriented queries with more natural responses.

In supervised learning, the AI model is exposed to labeled datasets of inputs and outputs that have been classified according to which output might be a correct answer to the input. Typically, the datasets are labeled in such a way to steer the model toward a specific goal.

Compared to GPT-4, GPT-4.5 might produce “warm and intuitive conversations,” OpenAI said. For example, when asked, “What was the first language?” the model’s response reads like a history documentary:

“Today, linguists study existing languages to understand how they evolved over thousands of years, but the exact identity of humanity’s very first language remains — and will likely always remain — a mystery.”

OpenAI said the improvements achieve that ineffable goal of making it such that interaction with the AI chats “ feels more natural.” GPT-4.5 has an improved “emotional quotient,” or the ability to infer and respond appropriately to human emotions.

However, as generative AI has worked its way into the mainstream, one fundamental problem persists: misinformation.

OpenAI said GPT-4.5 should “hallucinate less” than previous models, but AI-generated content remains vulnerable. Additionally, more naturalistic conversation could be exploited by scammers, especially since people are already using AI to create false rapport with victims.

The post OpenAI Releases GPT-4.5, a “Warm” Generative AI Model, for Paid Plans and APIs appeared first on eWEEK.

We will explore these key AI trends fuelling the most impactful tech transformations, giving IT executives the insights to adapt, innovate, and safely navigate the changes.

1. Agentic AI is reshaping IT jobs

Agentic AI isn’t just assisting IT teams anymore — it’s making decisions independently. If you work in IT, this shift will impact you directly. Agentic AI is evolving from a tool to an autonomous system, meaning your role could change significantly.

By 2028, Gartner predicts AI agents will be embedded in 33 percent of enterprise applications, up from less than 1% in 2024. Some IT jobs will disappear, while new opportunities will emerge.

You will see more of these IT jobs

• AI Workflow Engineers: AI needs customization. According to Gartner, companies will need specialists to train and fine-tune AI workflows to match business goals.
• AI Governance Specialists: As AI takes over decision-making, someone has to set the rules. According to Deloitte, AI Governance Specialist roles will be critical in compliance and risk management.
• Prompt Engineers and AI Interaction Designers: AI’s output is only as good as the input it gets. If you understand how to structure AI prompts and refine interactions, this skill set will be in high demand.
• AI Ethics Officers: AI is increasingly used in hiring, finance, and compliance. Someone needs to ensure AI makes fair, unbiased decisions, and that could be you.

You will see fewer of these IT jobs

• Entry-level IT Support: AI chatbots and self-healing IT systems handle troubleshooting, password resets, and help desk tasks, reducing demand for IT support staff.
• Network Monitoring Specialists: AI can now analyze and fix network issues in real time, eliminating the need for manual monitoring.
• Essential System Administrators: AI is automating systems updates, optimizing cloud resources, and detecting issues before humans notice them.
• Entry-level IT Compliance Analysts: AI is already tracking regulatory changes and automating compliance updates, reducing the need for junior compliance analysts.
• Basic QA Testers: AI-driven testing tools can detect bugs and correct errors automatically, making manual testing less necessary.

How to prepare for the AI workforce shift

If your job falls into one of these categories, it may be time to upskill and adapt; focusing on AI auditing, workflow automation, and security risk assessment will keep you relevant. Even though AI takes on more responsibilities, human oversight is critical, especially in compliance, ethics, and risk management.

Rather than viewing AI as a threat to your career, think of it as a collaborator. Companies that successfully integrate AI alongside their workforce, not as a replacement, will see the biggest benefits. If you invest in learning how to work with AI, you’ll be ahead of the curve. The key isn’t just adopting AI, but making sure you’re equipped to work with it.

2. Edge AI benefits real-time decision-making across industries

Edge AI processes data locally on devices, sensors, or industrial systems, unlike traditional AI models that rely on cloud computing; this eliminates delays, reduces bandwidth use, and improves efficiency. McKinsey highlights edge AI is essential for industries that depend on real-time decision-making, where waiting for cloud processing isn’t an option.

How edge AI is changing the game in different sectors

• Manufacturing is cutting downtime by using edge-AI-powered sensors to detect defects, optimize production, and predict equipment failures in real-time. With AI-driven maintenance, factories can reduce costly disruptions.
• Healthcare is becoming more responsive as edge AI enables real-time patient monitoring, emergency response, and faster diagnostics. AI-embedded medical devices detect anomalies instantly, reducing reliance on cloud-based systems.
• Autonomous vehicles rely on edge AI for safety. Self-driving cars process road conditions, obstacles, and traffic changes instantly using AI on board instead of depending on cloud servers. This minimizes the risk of accidents caused by network delays.
• Retailers are enhancing customer experiences with edge AI-powered inventory racking, cashier-less checkout, and personalized shopping. Retailers can cut costs and improve supply chain efficiency by processing data locally.
• Cybersecurity is becoming more proactive as edge AI detects threats instantly at the device or network level, preventing breaches before they spread. This real-time analysis reduces reliance on cloud-based security systems and strengthens data protection.

Edge AI is all about real-time, local processing; agentic AI takes it a step further by acting autonomously. While edge AI enables fast, on-device processing, agentic AI focuses on autonomy and long-term decision-making, making them complementary rather than interchangeable. As AI evolves, IT leaders must understand where each fits into their technology strategy to maximize efficiency and automation.

3. Retrieval-augmented generation enhances IT leaders’ decision-making

AI agents are everywhere, analyzing data, predicting outcomes, and making decisions without human intervention. According to a recent poll at The Wall Street Journal’s CIO Network Summit, 61 percent of IT executives are experimenting with AI agents, but 21% haven’t adopted them yet. One of the biggest reasons? Trust. About 29 percent of IT leaders cite cybersecurity and data privacy as primary concerns, and 75 percent feel AI currently delivers minimal value compared to its investment.

Despite these challenges, AI agents play key roles in various industries.

• Johnson & Johnson uses AI agents in drug discovery to optimize chemical synthesis.
• Moody’s applies multi-agent systems for financial analysis.
• eBay has AI agents assisting in coding and marketing, adapting to employee preferences over time.
• Deutsche Telekom and Cosentino have AI-powered digital assistants handling internal employee inquiries and customer orders.

How RAG can help

For AI agents to be effective, they need to make accurate, reliable, and up-to-date decisions, which is where retrieval-augmented generation (RAG) comes in.

Most AI models operate on preexisting knowledge, meaning they rely only on information they were trained on; that data can become outdated, leading to inaccurate predictions and poor decision-making. This is where RAG provides a critical advantage. Instead of making guesses based on stale data, RAG enables AI agents to retrieve and process real-time, relevant information from external sources.

For IT leaders looking to enhance AI-driven decision-making, RAG offers significant advantages in key areas.

• IT operations: AI agents with RAG can monitor system dialogues, network health, and security updates in real-time, adjusting proactively to prevent failures.
• Cybersecurity: RAG-enhanced AI can detect and analyze emerging threats as they appear, helping cybersecurity teams anticipate risks.
• Customer service: AI chatbots using RAG can provide accurate responses by retrieving the latest product details, company policies, and troubleshooting steps.
• Legal and compliance: AI systems with RAG can track regulatory changes and assess risks in real-time, reducing chances of non-compliance.

AI agents are still evolving, and skepticism around their reliability remains valid. But with RAG, AI has the potential to be not just faster but smarter and more trustworthy, and that’s what will ultimately drive real value in enterprise IT.

4. Multimodal AI improves contextual understanding

AI is no longer about processing one type of data at a time. Multimodal AI is gaining traction because it can analyze text, images, audio, and video all at once, making AI systems more context-aware and responsive. The simple idea is AI is now learning to process different types of data together, just like how you use sight, sound, and language to understand the world.

Drawing from various research papers and expert analyses, here’s how multimodal AI is making an impact in IT.

• IT security and operations: If your role involves cybersecurity, instead of just relying on security tools and network alerts, AI can now analyze traffic patterns, detect unusual activity, and even process system audio alerts to spot threats more effectively. Your security team can catch risks earlier with greater accuracy, reducing potential cyber threats that might otherwise slip through.
• IT support and automation: Slow resolutions are frustrating, especially when dealing with IT help desk issues. Multimodal AI is changing that by combining voice recognition, ticket analysis, and system diagnosis to troubleshoot problems before they escalate. This could mean faster responses, less downtime, and a seamless IT experience for your team and end users. Plus, AI-driven platforms can now detect patterns in reported issues, helping you fix recurring problems before they cause more significant disruptions.
• Enterprise applications: Whether you’re managing IT infrastructure or supporting business intelligence efforts, multimodal AI makes it easier to extract insights from structured data such as reports and databases and unstructured data such as handwritten notes, images, and voice recordings. However, implementing this isn’t as simple as flipping a switch; you will need more substantial computing power, optimized cloud strategies, and AI-ready infrastructure to handle the increased data demands. You might look into edge computing and AI-specific hardware to make this work.

Adopting multimodal AI isn’t without its challenges; data integration, privacy concerns, and high computational costs are barriers you’ll need to navigate. The question to answer is: How quickly can you build the right infrastructure to support multimodal AI?

5. AI frameworks for IT leaders and managers

Here are some AI frameworks that can help you streamline processes and optimize workflows.

• LangGraph helps you manage complex workflows with built-in moderation, ensuring system reliability and enabling seamless human-agent collaboration.
• CrewAI allows you to organize AI-driven teams, facilitating dynamic decision-making and autonomous task delegation to improve IT operations.
• AutoGen simplifies the development of scalable, event-driven AI agent systems, making it easier for you to enable collaboration and asynchronous communication in automated IT processes.
• HayStack enhances research and retrieval capabilities, improving AI-driven natural language processing (NLP) and knowledge management for IT support and troubleshooting.
• LlamaIndex helps you efficiently index and retrieve structured and unstructured data, making AI-powered insights more accessible for better decision-making.

6. AI tech stacks for developers, engineers, and technical teams

AI tech stacks are the foundation for integrating AI into enterprise IT, combining machine learning models, AI frameworks, and cloud computing to improve scalability, automation, and performance. These tech stacks typically consist of four layers:

• Application layer: The front-facing software, APIs, and AI-driven applications that automate tasks and enhance decision-making.
• Model layer: The backbone of AI systems, featuring pre-trained and custom AI/ML models that handle automation, analytics, and complex data processing.
• Data layer: Pipelines, storage solutions, and data management frameworks that fuel AI applications.
• Infrastructure layer: Cloud, on-premise, and edge computing environments that support AI workloads and optimize performance.

Understanding the differences between AI frameworks and AI tech stacks

In short, AI frameworks guide IT leaders’ AI strategy and implementation, while tech stacks power the IT team’s execution and scalability.

AI frameworks provide a structured methodology for managing AI systems, focusing on governance, workflow automation, and process optimization. These frameworks help IT leaders and managers establish AI-driven strategies, ensuring compliance, scalability, and efficiency in enterprise environments.

AI tech stacks refer to the combination of tools, models, and infrastructure that enable AI applications. Tech stacks are essential for developers and engineers building AI-driven software, integrating machine learning, and managing data pipelines.

7. Hybrid cloud positions IT for AI-focused growth

AI-driven applications demand high computational power, dynamic storage, and flexibility, and hybrid cloud architectures are now essential for balancing performance with security compliance.

Integrating on-premise systems, private clouds, and public cloud services allows you to seamlessly manage AI workloads across multiple environments while maintaining cost efficiency, agility, and control over sensitive data. This hybrid approach optimizes workload distribution, allowing high-compute AI tasks to run in public clouds while keeping critical and regulated data on-premise.

With AI continuing to evolve, hybrid cloud strategies ensure real-time adaptability to AI processing demands. You must enforce solid data governance, AI model security, and compliance standards, all while ensuring that AI workloads remain operational, efficient, and cost-effective.

For industries such as finance, healthcare, and e-commerce where AI workloads are complex and high-volume, hybrid cloud models offer greater agility without compromising security. To remain competitive, you must develop a cloud-native AI infrastructure, optimize multi-cloud strategies, and adopt AI-driven automation tools to allocate resources and reduce operational risks dynamically.

Hybrid cloud solutions are expected to become the backbone of enterprise IT infrastructure, allowing IT professionals to deploy, scale, and manage AI workloads more efficiently. Assess your hybrid cloud strategy, ensure seamless integration, and position your IT operations for AI-driven growth.

8. AI for DevSecOps strengthens IT security

AI-driven cybersecurity solutions are transforming DevSecOps, helping you automate threat detection, vulnerability management, and compliance monitoring throughout the software development lifecycle. With machine learning and deep learning algorithms, you can detect threats in real time, identify system weaknesses, and respond to cyber risks automatically.

However, AI-driven security isn’t without challenges. AI-generated cyberattacks are on the rise, making it critical for you to integrate AI responsibly. Striking the right balance between innovation and strong security ensures that your data and IT infrastructure remain protected.

While automation boosts security efficiency, human oversight is still necessary. AI models can generate false positives or miss nuanced threats, so your security team must validate alerts and prevent alert fatigue. A manual verification process for critical vulnerabilities ensures a more balanced security approach.

Resource constraints can also be a challenge. AI-driven security tools require high-performance computing for real-time monitoring and data analysis, which can strain infrastructure. Cloud-based solutions offer scalability, but you must carefully manage resource allocation to optimize cost and performance.

With predictive threat intelligence, automated incident response, and adaptive threat modeling, you can stay ahead of cyber threats while keeping your DevSecOps pipelines agile and secure.

9. Quantum computing and AI convergence

Quantum computing converging with AI could be one of the decades’ most significant shifts in computing power.

AI is pushing the limits of traditional computing, especially in machine learning, data processing, and optimization problems. Quantum computing changes the game by handling complex calculations at speeds that traditional systems can’t match. Gartner and McKinsey predict AI-powered by quantum computing will drive innovation in fields including cybersecurity, supply chain management, and scientific research.

What does this mean for IT decision-makers? AI models that struggle with massive datasets will be able to process information exponentially faster, leading to better predictions, deeper insights, and real-time decision-making. Think faster fraud detection in finance, next-level drug discovery in healthcare, and fully optimized logistics operations.

Tech giants including IBM and Google are already investing in AI-quantum integrations. IBM’s Quantum Roadmap is focused on bringing practical quantum-enhanced AI to enterprise IT, while Google’s Quantum AI team is exploring how quantum algorithms can optimize machine learning models. If these advancements continue at their current pace, AI could soon handle tasks that were previously impossible due to computing limitations.

The convergence of quantum computing and AI isn’t just about futuristic technology — it’s about preparing for a shift in AI capabilities. As quantum computing evolves, companies leveraging AI for cybersecurity, logistics, and final modeling will gain a competitive edge.

10. Generative AI transforms content creation for IT marketing

Content marketing is essential for engaging IT buyers, explaining complex solutions, and building thought leadership. But keeping up with content demands isn’t easy.

Generative AI automates content creation, transforming how IT brands communicate, from blogs and white papers to videos, graphics, and interactive experiences. AI-powered tools make content production faster, more efficient, and highly personalized. AI streamlines content workflows while maintaining brand consistency and quality.

To be clear, AI isn’t supposed to replace your marketing team — AI should make them more efficient. If you’re not using generative AI to scale content, personalize messaging, and streamline workflows, your competitors probably are. In 2025, AI-powered content creation isn’t just an advantage — it’s a necessity.

Personalization with AI-driven content creation and automated workflows

Your customers expect clear, well-researched content that explains complex IT solutions. Tools like ChatGPT, Jasper, and Claude will help you create blogs, white papers, and technical case studies quickly and at scale.

These AI tools also support your marketing team in customizing messages for different buying personas, whether you’re targeting CIOs, IT managers, or procurement teams. AI-powered chatbots, email automation, and content engines adapt language and tone to match the needs of each audience.

Instead of spending hours writing blog posts, social media updates, or product descriptions, your team can use AI to generate content drafts, repurpose existing materials, and streamline approvals. AI is even helping marketers turn blogs into videos automatically.

AI-powered graphics and videos

Your marketing team can use AI for infographics, social media graphics, and ad creatives. Tools like Adobe Firefly, Dall-E, and Canvas’ AI assistants allow teams to create branded graphics instantly without a designer.

Video marketing is popular; however, video production costs are high. AI tools like Syntheisa and Runway ML make creating explainer videos, product demos, and customer testimonials easier.

Future of AI: Ethical considerations and human interaction

AI will continue to evolve because it constantly learns from the data fed by its users; and, the more it is exposed to new data, the more sophisticated future AI models and tools will be. Since AI will be the main focus of the future’s technological advancement, there are ethical considerations users need to be aware of as well as its human interaction.

Human-in-the-Loop (HITL) design

HITL design incorporates human monitoring at different levels of AI development and decision-making; this technique makes sure AI systems adhere to human values and ethical standards. HITL requires ongoing human monitoring, feedback, and intervention to help prevent biases, errors, and unintended consequences.

This year, HITL will be considered standard practice in AI application development, guaranteeing AI behaviors are guided by human judgement and ethical considerations.

Ethical AI development and deployment

Ethical AI development entails establishing systems that are transparent, accountable, and equitable; it involves resolving common issues including bias, discrimination, and confidentiality. Developers must guarantee AI systems do not reproduce or worsen existing societal conditions.

Ethical AI deployment should take into account the broader societal implications of AI, such as potential job displacement and economic shifts.

Balancing AI autonomy and human oversight

As AI systems become more autonomous, it is critical to establish a balance between autonomy and human supervision. While AI can improve efficiency and decision-making, it should not be used without proper human management and responsibility.

To keep this balance between human and AI, specific boundaries for AI actions must be established, and the same goes for human involvement when necessary. This balance will be important in applications such as healthcare, finance, and public safety, where AI judgments can have serious ramifications for people’s lives.

FAQs

What are the next big trends in AI?

The next big trend in AI will likely be the advancement of self-supervised learning and more efficient AI architectures, such as those based on transformers and neuromorphic computing. Self-supervised learning allows models to learn from unlabeled data, reducing the need for extensive data annotation and enhancing the adaptability of AI systems.

Additionally, integrating AI with edge computing and federated learning is expected to improve real-time processing and privacy by enabling models to learn and infer locally on devices without centralizing data.

What is the next AI breakthrough?

One of the anticipated breakthroughs in AI is the development of more advanced generative AI models that can create realistic content such as text, images, and even synthetic data with minimal human intervention. Advances in large language models (LLMs) and multimodal models that can understand and generate text, images, and audio are also on the horizon. These breakthroughs could lead to significant improvements in AI’s ability to handle complex tasks, create content, and interact more naturally with humans.

What fields will AI transform?

AI is expected to transform a variety of fields, with automation potentially displacing certain roles in industries like manufacturing, logistics, and customer service. Tasks that involve routine and repetitive activities are particularly susceptible to automation. However, rather than outright replacement, AI is more likely to optimize human roles by handling repetitive tasks and enabling workers to focus on more complex and creative aspects of their jobs.

Fields such as healthcare and finance may experience particularly significant changes, with AI supporting decision-making and enhancing efficiency rather than fully replacing human roles.

Bottom line: AI trends can offer significant benefits, though keep ethics top of mind

Advances in AI architectures that are driving innovative applications across diverse fields, from climate action to transportation to media, will transform how we live and work. This progress means it’s crucial to address concerns about bias and fairness in AI to ensure these technologies benefit everyone; AI must be monitored as it grows more powerful. By focusing on ethical practices, you can make the most of AI’s potential while navigating its challenges.

The post 2025 AI Trends Driving the Biggest Tech Transformations Today appeared first on eWEEK.

AI is changing the world around us and it shows no signs of slowing down. With this technology becoming such a prominent part of our future, knowing how to start a career in AI is a smart move for anyone looking to stay ahead. Industries like technology, finance, and healthcare are relying more heavily on AI, and launching your career in the field means safeguarding your future. To help you get started. I’ve built this guide with the important steps to begin your AI career, including the career paths you can explore, the skills you need, and certifications that can set you on the right track.

Understand the AI Basics

The first step in building a successful career in artificial intelligence (AI) is truly understanding what it is. Grasping its main concepts will let you see its impact on industries and everyday life, from customer service to entertainment. This knowledge will guide your decisions as you move forward and give you a solid foundation to identify the skills you need, the different AI jobs, and finally choose the direction that aligns with your goals.

Choose Your AI Career Path

AI offers a wide range of career opportunities, each with its own set of required skills, tasks and responsibilities, and job salaries. It’s imperative to familiarize yourself with various AI roles early on. If you’re just getting started, consider exploring entry-level AI jobs to get hands-on experience and serve as stepping stones to more advanced roles later on. Knowing the demands of each career gives you clarity on what skills you need to focus on. By choosing the career you want, you can narrow down your learning path and start working on the specific skills for that role.

Build Core Skills

Like most jobs, starting AI careers requires a combination of technical and soft skills. Whether you’re just starting out or aiming for an advanced role, having a solid foundation is a must. Invest in AI certifications to boost your knowledge about AI as a whole. Core skills not only prepare you for entry-level jobs but also make you adapt more easily with the field as it evolves.

Technical Skills for Different AI Careers

Technical skills are non-negotiable when it comes to kicking off your AI career. You must enrich your knowledge in AI-related topics, such as machine learning (ML) and natural language processing (NLP). These skills equip you to work with data, algorithms, and systems, which are the primary components of AI. Learning them may feel daunting at first, but it will guide you to long-term success. There are many ways you can learn AI for free, but completing paid courses usually gives you a certificate that can raise your chances of getting a good AI job.

• Computer Vision: Mastering computer vision techniques enables you to work on systems that analyze and interpret images or videos. This is a primary skill for computer vision technicians, so they can make systems for facial recognition, self-driving cars, and medical imaging.
• Data Analysis: The ability to clean, organize, and interpret data is beneficial for an AI career. It is especially useful for data engineers and data scientists, who need to extract meaningful data insights and transform them into usable formats for AI systems.
• Data Engineering: Understanding how to design and maintain data pipelines is making sure AI systems have access to clean, structured data. This skill is fundamental for data engineers because they lay the groundwork for AI development by preparing datasets for analysis and modeling.
• Machine Learning Basics: Familiarity with machine learning algorithms and concepts is necessary for ML engineers and data scientists. It allows you to create systems that learn from data and fine-tune their performance over time.
• Natural Language Processing: Learning how AI systems generate human language is needed for NLP assistant and prompt engineers. It opens opportunities to make AI models for translation, sentiment analysis, and prompt engineering.
• Programming: Developing strong programming skills will supercharge any AI career. Languages like Python are widely used by support engineers and data scientists to implement algorithms, process data, and craft AI applications.
• Prompt Optimization: Crafting precise instructions for generative AI tools is a useful skill for prompt engineers, as they work to steer these systems to produce accurate and targeted outputs.
• Research Techniques: You must be able to explore new methods, test algorithms, and evaluate results to advance in AI. AI researchers and AI research assistants need research skills to uncover new approaches to enhance existing AI technologies.

Soft Skills Required for an AI Career

No matter your chosen career path in AI, technical expertise alone is not enough to succeed. Soft skills play a significant role in supporting teamwork, resolving problems creatively, and bridging the gap between AI solutions and real-world applications.

• Adaptability: Being open to learning new tools and methods lets you stay relevant and updated on the latest about AI.
• Collaboration: Working well with others is important because most AI projects require input from multiple people with different skills and expertise.
• Communication: Clearly explaining technical concepts to non-technical audiences helps make sure everyone understands your work and its value.
• Critical Thinking: The ability to assess situations carefully, spot problems, and make decisions is needed for solving challenges effectively.
• Problem-Solving: Tackling difficult situations and coming up with workable solutions is key to making your projects succeed.

Practice Your Skills and Create Your Portfolio

To jumpstart your AI career, actively apply what you’ve learned through practice and work on real projects to truly enrich your skills. You could begin with personal projects, contributing to open-source initiatives, or using publicly available datasets to experiment with AI models. You can even try using some free AI tools to get a firsthand experience without spending anything. Practicing allows you to see how AI works in real-world settings, so you can understand the challenges that can’t be taught in a classroom.

In addition, while practicing, you can build a portfolio of work to demonstrate your abilities to future employers. As you continue practicing, you’ll sharpen your problem-solving skills, boost your technical knowledge, and gain the experience necessary to move forward in your AI career.

Network and Develop Professional Relationships

Expanding your professional network plays a substantial role in thriving in the AI field. By connecting with more professionals, you get more job opportunities, insights, and collaborations. Attending AI conferences and joining online communities keeps you informed and enhances your skills. Networking can also provide mentorship and guidance, accelerating your career growth.

Top AI Certifications for Beginners

Getting certified can be a powerful way to demonstrate your skills and knowledge as you start your AI career. AI certifications for beginners will increase your credibility, combining structured learning with practical experience. I’ve selected a few courses that establish a solid grounding in AI, each handpicked so you can strengthen your skillset and set yourself up for success.

Artificial Intelligence for Beginners: Understand the Basics on Udemy

This Udemy course offers a comprehensive, beginner-friendly introduction to AI concepts and requires no programming background. Priced at $69.99, it features 20 lectures, assignments, and on-demand video content, ensuring a well-rounded learning experience. This introductory course covers AI history, topics such as regression and clustering, and practical AI applications. You will use a no-code approach to create AI models for image pose and audio detection to gain hands-on customization experience. With just minimal prerequisites of computer knowledge, the course is accessible and can be completed in about five hours.

Artificial Intelligence for Beginners: Python and AI Basics on Udemy

Udemy’s course is great if you’re looking to explore the principles of AI and Python programming, even without prior experience. The course is 10 hours and 14 minutes long and includes 26 lectures on topics like regression, classification, and neural networks. While programming knowledge isn’t required, an understanding of programming concepts can be advantageous. Available for $79.99, the course includes on-demand videos, hands-on coding exercises, and quizzes to test your knowledge. By the end, you’ll be able to create AI models and an interactive game using Python, along with a certificate of completion.

Introduction to Artificial Intelligence on Coursera

IBM’s course on Coursera is a great starting point if you want to learn more about AI. The course explores AI’s central concepts, its real-world applications, and examples you encounter daily. It encompasses machine learning, deep learning, and neural networks, as well as how AI is used in various areas, like robotics and computer vision. Ethical issues and the potential impact of AI on society are also discussed. The course is self-paced, taking approximately 13 hours to complete, with flexible deadlines. Organized into four modules, it includes 12 assignments. After finishing, you’ll receive a shareable certificate. A monthly subscription of $59 to Coursera Plus is needed to access this course.

FAQs

Bottom Line: The Value of Knowing How to Start A Career in AI

Embarking in careers in artificial intelligence molds you to be a part of a field that redefines our lives today. Keep in mind that establishing an AI career is just like launching any other career, you have to start from the ground up. This guide was built so you can prepare your AI career with confidence, no matter your background. By learning the core AI skills and gaining practical experience, you equip yourself with future-proof skills and open doors to innovative and impactful opportunities.

Find out more about how AI is impacting the job market by reading our articles on will AI replace humans, AI-proof jobs, and jobs AI will replace.

The post How to Jumpstart Your Career in AI appeared first on eWEEK.

What is artificial general intelligence (AGI), and why does it matter? As one of the most talked-about topics in technology today, it has sparked a race among top companies like OpenAI and Google to turn this cutting-edge concept into reality. Understanding AGI is important because it has the potential to revamp industries, affect our society in profound ways, and change the way we interact with technology. Here’s what you need to know about what it might be able to do, how it might transform industries and fields, and the significant challenges facing its development.

What is Artificial General Intelligence (AGI): A Clear Definition

Artificial general intelligence, or AGI, refers to a type of artificial intelligence (AI) that can interpret, learn, and perform any cognitive task that a human can do. Unlike today’s AI, which is built to handle specific tasks like recommending products or processing data, AGI would be able to adapt to new challenges and apply knowledge across various fields. In other words, this advanced type of AI would think and reason like a human. While AGI holds great potential, it’s worth noting that it is still a concept today, with no fully developed systems available yet.

Key Capabilities of Artificial General Intelligence

AGI would have a range of capabilities that mimic human intellectual functions, so it can perform tasks beyond the narrow focus of the current AI tools in the market. Some key capabilities include the following:

• Human-Like Reasoning: The technology would be able to understand and make decisions the way humans do. It would think critically, solve problems, and come up with solutions based on its own experiences and past interactions, similar to how we apply past knowledge to new situations.
• Solving Unfamiliar Problems: One of AGI’s strengths is its potential to tackle new problems. Unlike traditional AI, which is trained to perform specific tasks, AGI would have the capacity to handle problems it hasn’t been directly trained to solve. It could figure out how to approach a completely new challenge, just like humans do when faced with something we’ve never encountered before.
• Self-Learning and Adapting: AGI could fine-tune its skills and learn from experience, without the need to be manually updated every time. It would observe and analyze data, learn from mistakes, and find better ways to complete tasks over time. This means AGI could adapt to new situations and get better at tasks on its own.
• Using Knowledge Across Different Areas: AGI would be able to take what it learns in one area and apply it to other tasks. For example, if it learned how to solve math problems, it could use that knowledge to address challenges in other fields, like science or business. The ability to transfer skills across different areas is something humans do naturally and would make the technology versatile in diverse sectors.
• Understanding and Responding to Emotions: Recognizing and reacting to human emotions would also be within AGI’s capabilities. This would be important in settings where understanding people’s feelings matters, such as healthcare, customer service, or social situations. By responding to emotions appropriately, AGI would be better equipped to work with humans in an effective way.

Understanding AGI vs Traditional AI

The table below provides a snapshot of the major differences between AI and traditional or narrow AI by underscoring their capabilities, adaptability, and current status.

AGI would have the ability to think, learn autonomously, and adapt to new challenges like humans. However, it is still theoretical and has not been realized yet. On the other hand, traditional AI is built for particular tasks and operates within a fixed scope. It cannot adjust to new tasks without human input.

For example, an AGI could learn to diagnose medical conditions, then use that knowledge to develop personalized treatment plans—and even adjust its approach based on the patient’s progress. Additionally, it could apply this problem-solving ability to tasks in completely different fields, such as creating business strategies or advising on environmental conservation. In contrast, traditional AI, like a diagnostic tool, can only analyze medical data for specific conditions. It cannot adapt to other areas or improve on its own.

Potential Applications of Artificial General Intelligence

While AGI isn’t here yet, its potential applications span numerous fields and hold great promise of drastic advancements in many sectors. Without being limited to specific tasks like narrow AI, AGI would be highly versatile and could apply its capabilities to solve multi-disciplinary problems. It could overcome challenges currently beyond the capabilities of existing AI applications.

Transforming Healthcare

AGI would change the game in healthcare by diagnosing complex and rare diseases with greater accuracy, even in cases where symptoms are ambiguous or overlap with multiple conditions. It could create highly personalized treatment plans by studying patient history, genetic information, and real-time health data. In addition, AGI could accelerate drug discovery, identifying possible treatments in weeks rather than years by processing massive datasets and running predictive simulations.

Advancing Scientific Research

In scientific research, AGI would be able to simulate experiments, analyze intricate datasets, and generate hypotheses. It could expedite breakthroughs in quantum physics, genomics, and climate science. By integrating knowledge from various domains, the technology could uncover connections and solutions that might otherwise go unnoticed by traditional AI.

Improving Industry

Organizations in the industrial field could use AGI to boost efficiency in real-time by managing entire supply chains. It would predict and resolve disruptions before they occur. In manufacturing, it could oversee autonomous factories, optimizing production processes while maintaining safety and quality standards. Its ability to adjust to changing circumstances would make it an invaluable tool in industrial environments.

Enhancing Business Strategy

AGI could improve business decision-making by evaluating market trends, customer behavior, and operational data to find opportunities and risks. In contrast to narrow AI systems, AGI would innovate solutions to challenging business problems, such as dealing with economic uncertainty or forecasting long-term market shifts. Its ability to learn from diverse sources would empower businesses to remain competitive.

Redefining Finance

In the financial sector, AGI could increase forecasting precision by detecting patterns in vast amounts of financial data, so investors and institutions can make informed decisions. It would also be able to spot fraud in real-time by recognizing subtle anomalies that traditional AI systems might miss. Additionally, AGI could build more robust financial models, factoring in complicated variables and scenarios to mitigate risks.

Challenges in Artificial General Intelligence Research

Developing AGI is one of the most ambitious goals in technology, but it comes with many difficulties. These challenges include technical, ethical, and societal areas, making AGI development an intricate and multi-faceted process. Overcoming the following challenges is tantamount to ensuring safety, upholding ethical standards, and carefully planning how AGI’s introduction and use will affect people, industries, and society as a whole:

• Making AGI Truly Flexible: AGI would need to handle a wide range of problems and adapt to new situations, just like humans. Building a system of flexibility is incredibly hard because current AI tools are not designed to think or learn at this level of sophistication.
• Massive Computing Needs: To replicate human intelligence, AGI would require enormous amounts of computing power to process information from diverse sources quickly. Figuring out how to make such systems powerful and efficient enough for real-world use is a substantial challenge.
• Understanding Human Intelligence: We don’t fully understand how human thinking works, especially complicated aspects like intuition or consciousness. Without this understanding, it’s challenging to build machines that can emulate human-like thinking.
• Making AGI Safe and Ethical: AGI could potentially be misused, like to create biased systems or harmful tools like autonomous weapons. Researchers must make sure that AG is built responsibly and follows strict ethical guidelines. This is a tricky task that necessitates global collaboration.
• Keeping It Under Control: There’s a risk AGI could act in ways we don’t expect, especially since it would have the capacity to learn and change over time. Ensuring that these systems remain aligned with human values and are safe to use is one of the biggest challenges in AGI research.
• Impact on Jobs and Society: If AGI becomes a reality, it could replace jobs or cause economic inequality by benefitting some groups more than others. Preparing for these social impacts is just as important as building the technology itself.
• High Costs and Resources: Researching AGI necessitates a lot of money, time, and expert knowledge. Not all organizations have these resources, slowing down progress and leaving smaller businesses out of the race.

3 Introductory AGI Courses to Consider

Familiarizing yourself with AGI can give you a competitive edge, whether you wish to advance your career in AI or simply want to stay informed about emerging technologies. The following introductory courses can help you gain a deeper understanding of what artificial general intelligence is, so you can solidify your knowledge about this promising AI advancement.

Artificial General Intelligence (AGI): An Introductory Course on Udemy

This Udemy course provides a fundamental understanding of AGI, suitable for beginners with no prior experience. The course covers relevant topics, including the foundations of AI, the basics of AGI, and the latest trends in the field. It also explores the benefits, risks, and challenges associated with AGI, equipping you with insights into what the advanced technology can achieve. The entire course consists of 15 lectures and can be completed in approximately 45 minutes. Upon completion, you will receive a certificate to bolster your credentials in the job market. This introductory course costs $24.99.

Intro to Artificial General Intelligence (AGI): Future of AI on Udemy

Udemy’s introductory course offers a comprehensive overview of AGI for learners with no technical background. It discusses the historical context and foundation of AGI, the distinctions between narrow AI and AGI, and ethical considerations surrounding its development. In addition, it addresses future trends in AI and AGI, shedding light on the challenges and opportunities that lie ahead. Spanning one hour and 46 minutes, the course includes 39 lectures, on-demand video, and downloadable resources. It also has a practical test at the end to reinforce your understanding. You will be awarded a certificate once you complete the course. It is available as part of Udemy’s premium plans, starting at $20 per month, or as a separate purchase of $49.99.

Artificial General Intelligence (AGI) on Udemy

This Udemy course brings a clear and concise introduction to the topic, with on-demand videos and 22 lectures. It elaborates on major AGI concepts and the role of robotics in AGI development. It also examines the ethical, software, and hardware challenges in creating AGI. The course provides quizzes to test your knowledge and a certificate of completion. Priced at $44.99, it is made for learners at any level, making it accessible and valuable for anyone who wants to learn more about AGI.

Frequently Asked Questions (FAQs)

Bottom Line: Why Knowing What Is Artificial General Intelligence Matters

Understanding artificial general intelligence is imperative because this technology could change industries, solve difficult problems, and transform how we use AI. But as we begin to develop AGI, we must carefully address several challenges, including technical issues, ethical concerns, and its overall impact on society. By learning about AGI’s potential and risks, we can work toward making sure it is created responsibly and used in ways that would benefit everyone.

Learn more about the big names racing toward AI development in our Top AI Companies article.

The post What Is Artificial General Intelligence: A 2025 Beginner’s Guide appeared first on eWEEK.

Healthcare organizations of all types are adopting generative AI in hopes of optimizing patient diagnoses, improving doctor-patient relationships, and providing administrative and clerical support in clinical settings. GenAI’s ability to mine vast medical datasets and use complex algorithms to produce biomedical insight is revolutionizing healthcare. Knowing GenAI’s potential in the healthcare industry can give you a better understanding of the transformational changes this dynamic technology is creating along with the challenges it poses.

Generative AI and Healthcare: Potential and Challenges

The two goals that drive the healthcare industry often appear to be in conflict. On one hand, healthcare organizations are passionate about improving patient care. On the other, these same organizations struggle with the need to contain rising costs. Balancing these priorities is a constant challenge. The continued shortage of healthcare professionals results in consequences ranging from medical team burnout to inefficiencies in patient care. To face these challenges, many healthcare organizations are strategizing to implement generative AI.

GenAI can offer virtual assistance in a growing range of tasks, which frees medical professionals to focus on tasks that offer more real value to patients. It can also lower labor costs, lessening the need for organizations to squeeze budgets. Yet generative AI is an emerging technology that sometimes prompts more excitement than real understanding. Attempting to deploy a new technology like generative AI in a field as complex (and prone to litigation) as healthcare requires a steep learning curve. Even tech-savvy professionals in the field can’t fully predict how it will reshape patient care.

How Generative AI is Helping Relieve Healthcare’s Big Burdens

Generative AI is equipped to address some of healthcare’s most pressing concerns by automating low-level repetitive tasks, freeing up clinical resources, and allowing healthcare practitioners to concentrate on higher-value activities. This can result in more efficient operations and improved patient care in the following ways:

• Addressing Chronic Conditions and Complex Diseases: GenAI can help manage chronic conditions and complicated diseases by analyzing large volumes of medical data to detect trends and forecast results. This can assist with early detection, personalized treatment strategies, and continuous monitoring, which will ultimately improve patient outcomes and quality of life.
• Improving Patient Outcomes and Quality of Life: GenAI has the potential to greatly improve patient outcomes by offering more accurate diagnoses, personalized treatment plans, and real-time monitoring. It also can offer healthcare workers the data to make better decisions, which leads to enhanced patient care and a higher quality of life.
• Reducing Healthcare Costs and Administrative Burden: GenAI can help cut healthcare expenses by improving administrative operations, including documentation, invoicing, and scheduling. Healthcare organizations may save time and costs by automating these procedures, lowering the total administrative burden, and allowing for more effective financial management.

Improving Doctor-Patient Relationships with Generative AI

Generative AI transforms the doctor-patient relationship, reducing administrative efforts and improving interaction quality. This not only increases the efficiency of healthcare delivery but also promotes a more trusting and empathic relationship between physicians and patients.

Enhanced Personalized Care

GenAI makes customized healthcare possible, creating full health profiles for each patient by combining datasets such as patient medical records, genetic information, and lifestyle variables. This allows for the development of highly personalized healthcare treatment regimes that take into consideration each individual’s specific health demands. For example, AI can identify potential health issues early on and recommend preventative steps specific to each patient.

Also, AI-powered virtual health assistants can offer ongoing assistance by monitoring patient progress and adjusting recommendations in real time based on new data. This level of customization ensures that each patient receives care specific to their unique medical need and caring preferences that can help improve their overall health results.

Efficient Communication and Data Analysis

Generative AI improves communication between doctors and patients by providing correct and quick replies to patient requests. AI may be integrated into patient portals and healthcare applications to offer rapid answers to frequent inquiries, provide prescription reminders, and track progress on treatment programs.

In terms of data analysis, AI can handle massive amounts of information from a wide variety of sources, including electronic health records, medical literature, and real-time health monitoring equipment. By recognizing patterns and connections in this data, AI can provide insights that human analysts may miss, assisting in diagnosis, treatment planning, and illness management.

Patient Engagement and Education

Using generative AI to help patients understand their medical condition lifts a burden on medical personnel who need to explain technical terms to a patient. AI-powered chatbots and virtual health assistants can communicate with patients, offering information and answering questions in real time. These technologies can also help patients navigate difficult medical procedures, offer advice on managing chronic diseases, and provide motivational support for maintaining healthy habits.

By making health information more accessible and understandable, generative AI healthcare encourages individuals to take an active part in their healthcare. As a result, this will likely lead to greater adherence to treatment programs and better health outcomes.

Challenges and Solutions in Implementing Generative AI

Generative AI has the potential to alter the healthcare business. However, its deployment faces considerable obstacles, including data errors, possible bias, and a need for more effective AI governance.

Lack of Governance and Knowledge

Effective AI governance is important even if it is still underdeveloped in many organizations. Without clear standards, AI tools risk being exploited, resulting in negative outcomes for patients and medical teams. Also, the complexity of AI technology requires trained personnel for effective development, testing, and implementation. Ironically, while AI may automate some tasks, a lack of AI knowledge prevents its efficient application.

Organizations should:

• Invest in educating and employing AI specialists with healthcare experience
• Create governance structures to promote responsible AI deployment

Data Protection and Regulatory Compliance

Patient privacy is a critical component of the healthcare business, governed by federal rules such as HIPAA (Health Insurance Portability and Accountability Act). These requirements compel healthcare institutions to preserve sensitive patient data, such as social security numbers and personal health records. Generative AI complicates these criteria since the technology relies on acquiring and analyzing medical data to deliver insights.

Organizations should:

• Never use patient data without their informed consent
• Ensure that any AI technology used meets high regulatory criteria

Technical Challenges and Data Quality Issues

Healthcare decisions rely on accurate information, making data mistakes a serious problem. Generative AI models, known as large language models (LLMs), can generate erroneous results, or even “hallucinations”—false information that seems plausible. For example, technologies such as ChatGPT have been shown to generate incorrect data. This necessitates healthcare personnel to manually validate AI-generated results, lowering the productivity gains that AI offers.

To fix this, healthcare organizations should:

• Require robust validation tools for detecting and correcting problems in AI outputs
• Encourage close collaboration between AI engineers and healthcare specialists to improve algorithm accuracy for industry-specific use cases

Ethical Considerations and Regulatory Barriers

Generative AI poses ethical concerns, including bias in outputs and the possible misuse of AI systems. Bias arises from LLMs trained on datasets that may reflect social preconceptions, resulting in discriminating outcomes for specific races or genders. In healthcare, such biases can have a direct influence on the quality of care for certain patient populations.

Organizations should:

• Regularly audit AI systems for fairness and inclusion
• Establish ethical criteria for AI use to prevent inadvertent damage

Strategies for Safe and Effective Integration

The numerous issues around the safe integration of AI in healthcare systems workflow must be addressed proactively if generative AI is to achieve its promise in healthcare. Important strategies include:

• Enhancing collaboration among AI developers, healthcare experts, and regulators
• Implementing extensive training programs to address the knowledge gap among staff
• Prioritizing patient safety and ethical issues throughout the AI integration process

The healthcare business is critical to worldwide health and well-being. While generative AI has tremendous potential, it must be used thoughtfully and ethically to guarantee that the advantages exceed the hazards.

Opportunities and Benefits of Generative AI

The future of generative AI offers enormous promise, from personalized healthcare to predictive maintenance to streamlined administration.

Enhanced Diagnostic Accuracy and Efficiency

Every patient is different and so each patient’s care treatment needs to be tailored to fit their unique healthcare needs for the best outcomes. However, personalized care plan development requires teams to get to know patients on a deeper level by analyzing complex health data such as medical histories and genetics. Technology such as generative AI and machine learning can simplify the data analytics involved with this process of customized healthcare. For example, generative AI can be used to find patterns in patient health data that point to the potential development of chronic diseases. Providers can develop care plans to help prevent these diseases.

Personalized Medicine and Treatment Planning

Patient care requires the use of a wide range of medical devices, from critical defibrillators to complex MRI imaging devices. Predictive maintenance can help prevent operating issues with this equipment by alerting medical teams to potential future failures before they occur. Generative AI can be used to quickly find patterns in large data sets that point to equipment failures. As a result, medical teams can keep their equipment maintained so it’s available for medical intervention at all times, improving overall patient care.

Streamlining Administrative Processes

By automating repetitive processes like creating comprehensive reports, compiling medical records, and accurately producing important documents such as medical letters of recommendation, generative AI holds the potential to completely transform administrative operations in the healthcare industry. AI can easily search the large database used by hospitals to extract relevant information and produce clear and accessible summaries of patient records, treatment histories, diagnostic results, and physician notes.

Medical Imaging Analysis and Diagnostics

Medical imaging methods such as MRI, CT, and PET scans are key components of patient care. They’re used to diagnose diseases and pinpoint critical injuries quickly. Generative AI can simplify the imaging process to help healthcare teams deliver faster results to patients. AI is already seeing significant adoption in the field of medical imaging. For example, generative AI solutions already exist to reduce image noise for clearer scans. Other solutions can also use machine learning to reduce overall scan time. Another potential use case is using artificial intelligence and machine learning to automatically detect common abnormalities in patient images.

Drug Discovery and Development

GenAI is transforming drug research and development by dramatically speeding processes, lowering costs, and increasing healthcare outcomes. It analyzes massive datasets to forecast patient acceptance of medication, create novel compounds, and find disease pathways, while an AI healthcare tool like AlphaFold increases our understanding of protein structures.

Treatments are being customized with the help of generative AI, which analyzes genetic data and repurposes current medications. It also improves preclinical testing by modeling biological systems to anticipate efficacy and toxicity. In clinical trials, AI improves patient recruitment and trial design, enhancing success and efficiency. GenAI facilitates innovation even in low-resource settings, treating neglected diseases and providing medicines quickly amid health emergencies. While issues such as data quality and regulatory constraints persist, GenAI has enormous potential to alter the healthcare business and improve global health outcomes.

Clinical Decision Support Systems

Beyond patient care, healthcare organizations require administrative support. For example, hospitals and clinics require key players such as medical invoicing specialists and office administrators. Generative AI applications can support administrative tasks to improve efficiency. For example, generative AI can complete tedious, manual tasks that take time away from more important projects. For example, AI can perform data entry, take patient payments, communicate to teams which patients are due for exams, and much more.

Generative AI can also be used to complete the administrative tasks that physicians and other patient-facing individuals must complete. Of all the potential uses of AI in healthcare, supporting administrative tasks is seeing a high degree of interest and investment. For example, physicians are already using AI to document the details covered during patient visits in electronic medical records. As a result, doctors and nurses alike can spend more time with their patients and less time on manual tasks.

Best Practices for Implementing Generative AI in Healthcare

Implementing generative AI in healthcare will help medical professionals care for their patients more efficiently. With this in mind, medical professionals need to stay up-to-date with data security trends and be knowledgeable about ethical data usage, AI tools related to the healthcare industry, and AI-based clinical testing and validation as follows:

• Ensure Data Security and Ethical Use: Since patient information is sensitive, data security and ethical use are critical in the healthcare industry. To guarantee data privacy, organizations must adhere to laws such as the General Data Protection Regulation (GDPR) in Europe and the Health Insurance and Portability and Accountability Act  (HIPAA) in the United States. This includes updating security procedures and performing routine audits.
• Train Healthcare Professionals on AI Tools: The foundations of AI, the specific instruments used in healthcare, and their many uses should all be covered in thorough training programs that combine practical instructions with continuing education. It helps to select AI technologies with intuitive user interfaces so that healthcare professionals can use them without any technical expertise.
• Conduct Clinical Testing and Validation: Thorough clinical testing and validation are important before implementing AI tools in a clinical setting; this is essential to guarantee their efficacy and safety. After deployment, continuous monitoring is needed to make sure the tools function as intended, with modifications made in response to user feedback.
• Implementing Data Quality and Transparency: The foundation of successful AI systems in healthcare is high-quality data. To guarantee accuracy and consistency, it is important to set up explicit data-gathering procedures, which include standardized data sources and formats. Frequent preprocessing and data cleaning are important for removing mistakes and inconsistencies and preserving the accuracy of AI predictions.

3 Popular Generative AI Tools in Healthcare

The healthcare industry greatly benefits from generative AI tools—these tools can make diagnosing patients easier, allow medical professionals better patient management, and help patients to have a deeper understanding of their condition. Tools such as Hippocratic AI, PaigeFull Focus, and Kahun are a few of the many genAI tools that can streamline healthcare processes.

Hippocratic AI

Hippocratic AI is a generative AI tool known for its patient administration and appointment follow-up functionality. It improves overall patient care and operational efficiency by helping healthcare providers with appointment setting, patient record management, and on-time patient follow-ups. Hippocratic AI uses AI agents to interact with patients in a manner similar to a healthcare assistant. It will ask a few questions to verify their identity and help the AI to gather information that its doctor or administrator programmed it to do. This AI telehealth administration and follow-up tool costs nine dollars an hour.

PaigeFull Focus

PaigeFull Focus is a professional education and cancer diagnosis generative AI tool that was created to help with cancer detection and offer professional training. Using AI to evaluate pathology slides, it helps pathologists identify cancer faster and more accurately. It also provides educational materials to help doctors stay up-to-date on the most recent developments in oncology. PaigeFull Focus doesn’t post its pricing information on its website, but you can have a three-day free trial through Microsoft’s Azure Marketplace.

Kahun

Kahun is a generative AI tool designed for patient follow-up and diagnosis. By analyzing patient data and medical literature, it employs AI to help physicians diagnose a range of ailments. To make sure that patients receive ongoing treatment and supervision, Kahun also assists in overseeing patient follow-ups. Kahun’s pricing is not posted on its website, but you can request a demo account.

Bottom Line: The Future of Healthcare with Generative AI

Generative AI is widely seen as offering enormous potential for the healthcare industry. It can provide physicians with the tools they need to deliver personalized care and also ensure medical equipment is available for intervention at all times. However, the lack of governance and the possibility of bias in AI models, which could result in inaccuracies and privacy challenges, should give pause to healthcare industry leaders who are considering major investments in generative AI. Healthcare organizations should tread carefully to protect their patients, staff, and the industry as a whole.

Read our guide to generative AI to learn more about the technology behind it, the risks associated with it, and the wide range of use cases it provides across other fields beyond healthcare.

The post Generative AI in Healthcare: Understanding the Fundamentals appeared first on eWEEK.