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.

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Matt Hillary, VP of Security and CISO at Drata, details problems and solutions as AI plays an expanding role in governance, risk, and compliance (GRC).

Watch the video:

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Nick Heudecker, Sr. Director, Market Strategy and Competitive Intelligence at Cribl, discussed how to address cyber risks in LLMs and data harvesting, and also made predictions about the future of cybersecurity in the age of AI. Watch the video:

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Join eWeek at 2 PM Eastern/11 AM Pacific on Tuesday, January 28, for a lively, in-depth discussion of the emerging technology of agentic AI as eWeek Senior Editor James Maguire moderates our next monthly TweetChat on the X platform (formerly Twitter).

A panel of industry experts will discuss and debate all aspects of agentic AI, a technology that enables AI bots to perform a wide array of tasks. Our aim is to offer thought leadership that enables companies to more effectively strategize as agentic AI plays an ever greater role in the workplace.

Here’s what you need to know to participate in the eWeek TweetChat.

Expert Panelists: Agentic AI

The list of experts for this month’s TweetChat currently includes the following:

• Evan Kirstel, Chief Digital Evangelist, eVIRa Health
• Zeus Kerravala, Principle Analyst, ZKast Research
• Bernard Golden, Executive Technical Advisor, VMware
• Andi Mann, Global CTO and Founder, Sageable
• James Maguire, Senior Editor, eWeek [moderator]

Please check back for additional expert guests.

TweetChat Questions

The questions we’ll tweet about will include the following:

1. What’s your sense of the rate of agentic AI’s adoption in the workplace: is it still in the future, nascent but growing, or already seeing rapid adoption?
2. Do you think executives fully understand the potential of agentic AI? What advice would you give them?
3. What trends do you see affecting agentic AI in 2025?
4. For companies that want to use agentic AI, what best practices do you recommend? 
5. What are typical challenges that companies encounter with agentic AI, and how do you advise companies to address them?
6. What should executives know about the costs of agentic AI? Is the ROI usually relatively fast, or is this a long-term process?
7. What are the security concerns around agentic AI?
8. For companies seeking a vendor to provide agentic AI, how should they go about selecting the best provider for their needs?
9. Last question: what other highly important considerations should companies be aware of as they grow their agentic AI deployment?

How to Participate in the TweetChat

The chat begins promptly at 2 PM Eastern/11 AM Pacific on January 28. To participate:

1. Open X in your browser. You’ll use this browser to post your replies to the moderator’s questions.

2. Open X in a second browser. On the menu to the left, click on Explore. In the search box at the top, type in #eweekchat. This will open a column that displays all the questions and all the panelists’ replies.

Remember: You must manually include the hashtag #eweekchat for your replies to be seen by the TweetChat panel of experts.

That’s it—you’re ready to go. Be ready at 2 PM Eastern/11 AM Pacific to take part. Note that there is sometimes a few seconds of delay between when you tweet and when your tweet shows up in the #eweekchat column.

TweetChat Schedule for 2025*

January 28: Agentic AI Trends and Best Practices
February 25: Data in 2025: Optimizing Data for Competitive Edge
March 25: How to Select an AI Vendor
April 29: Future Cloud: New Directions in Cloud Computing
May 27: AI and Cybersecurity
June 24: The Costs of Tech: Data, Cloud, AI

*All topics subject to change

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Ayman Sayed: Waves of Innovation

As the CEO of BMC, Ayman Sayed speaks with a wide range of enterprise customers about their plans and challenges. An issue for many customers is that they’ve invested deeply in their existing tech stack yet they’re also adding new tech at a rapid pace. According to Sayed, this creates an issue: “How do you drive a degree of standardization or uniformity without having to do a forklift upgrade, where you have to rip everything up and start from scratch?”

Even as they handle this issue, many businesses’ overriding concern is creating a strategy that “is not focused on technology for technology’s sake, but rather toward the business outcome.” This is where BMC can be the biggest help to customers, Sayed said.

BMC works to “reconcile their past investments and their heterogeneous environments, and at the same time equip them not to just to ride the current wave of technology and shifts, but future-proof their investments in technology.” The goal is to always translate customer needs into a concrete business outcome that supports the bottom line. This work, of course, is constant, as enterprise customers navigate new advances in AI, cloud, data, DevOps, edge computing, and other emerging tech trends.

Looking ahead to the future, Sayed sees enormous potential. “I think we’re barely scratching the surface in terms of AI applicability to IT workflows,” he said. “Every day almost, we come up with a new use case or a new workflow that we can reinvent.”

To that end, BMC has invested deeply in its product portfolio, including mainframe modernization, intelligent automation, and digital services and operation management. Sayed’s message to customers: “I think now is the time to jump on these waves of innovation.”

“Now is the time to jump on these waves of innovation,” according to BMC CEO Ayman Sayed.  

Ram Chakravarti: Key Themes in Enterprise Tech

Enterprise businesses are inundated with technological changes, said BMC CTO Ram Chakravarti. “The shiny new toy of yesterday becomes obsolete today, and then the next new thing comes up,” he said. “But rarely is enough weight given to operationalization considerations, and how you get value from your tech investments.”

Fully understanding how to derive business value from technology requires leaders to understand key themes in AI, data and risk management.

The first theme, of course, is AI and generative AI. He noted that AI has earned a halo effect as so many users tout its enormous potential. “It would be silly if I didn’t mention that the halo effect has diminished somewhat because of the high cost of implementation, potentially questionable return on value in certain use cases, and a host of risks that we need to solve for,” he said. To counter this, BMC has focused on a pragmatic, business-focused application of AI.

A second overarching theme is data. “In the data space, organizations are stymied by their inability to exploit every single facet of their data ecosystem,” Chakravarti said. “It’s not just about the business data, it’s also the data about the data—otherwise known as metadata.”

To address this, BMC has listened to customer needs and uses generative AI to connect the disparate puzzle pieces of metadata sprawled across the data management ecosystem.

And then there’s perhaps the most essential theme of all: pragmatism and risk management. BMC is always striving to move customers forward with their tech infrastructure, a constant process that must be tempered with pragmatism, Chakravarti said.

“On one side, AI done right is a force for good and it adds value,” he said. “On the other side, there’s a whole host of uncertainties.” Getting ahead of these risks requires a proactive approach to AI risk mitigation, “because the extent to which you’re able to actively manage and mitigate the risks associated with AI, the greater the value from your AI investments.”

“So what I suggest is that organizations get their hands around active risk management and risk mitigation,” Chakravarti said. “That’s going to be the pathway to success.”

“The extent to which you’re able to actively manage and mitigate the risks associated with AI, the greater the value from your AI investments,” BMC CTO Ram Chakravarti said.

Margaret Lee: Managing Multi-Generational Technology

Throughout her extensive career in technology, there have always been two conflicting forces for enterprise clients to balance, said BMC’s GM & SVP of Digital Service and Operations Management Margaret Lee. On one hand is the desire to stay current with the latest technology.

“Every company wants to push out new capabilities, new applications, new releases, to try to get every digital experience to be more consumer-like for their customers and employees,” Lee said.

On the other hand, BMC clients are comprised of Fortune 100 and Fortune 500 companies and large governmental entities. These organizations have complex IT infrastructures built over many years of business growth. As they combine today’s next-gen tech with their legacy tech, “it’s a recipe for a lot of heartburn,” Lee explained. “I’m going to approve a release. Which system does it touch? What if something goes wrong? What kind of operations team do you need to be able to understand the true impact of a particular change?”

Large enterprise managers want to move fast, but “they have a certain risk profile,” Lee said. Large enterprise requires both innovation and stability, and she provides the guidance to help these major players navigate a tricky balancing process.

“Inevitably,” she said, “the request takes the form of, ‘Can you help me understand the risk I’m taking? How do I think about the risk profile of a particular release, a particular change? How do I make sure that I don’t shoot myself in the foot with some of the things I’m trying to do?’”

Among the new technologies that are particularly exciting to customers is agentic AI, and BMC has just released new products and features with upgraded agentic AI. Companies see great potential in AI apps whose functionality enables AI to be a true digital colleague, Lee said.

“What we believe is that the form it ideally takes is deeply embedded in your platform, in your application, in your product,” she said. This embedded approach allows users to speed workflow without needing to pivot to a separate app for AI assistance. In particular, this embedded AI offers a major productivity boost to the many staffers who handle documents.

“Whether it’s paper or digital, that’s a lot of intense work,” Lee said. “If some gen AI can help update those articles so that your knowledge article library is up to date, it’s hugely valuable. That’s why [agentic AI] is so hot in the industry—because more and more people realize AI becomes an agent and helper throughout the experience within the application.”

“More and more people realize AI becomes an agent and helper throughout the experience within the application,” said Margaret Lee, BMC’s GM & SVP, Digital Service and Operations Management.

Gur Steif: Multiple Axis of Innovation

BMC’s President of Digital Business Automation Gur Steif refers to the “multiple axes of innovation” that have transformed IT as technology has evolved across eras. One axis reflects the rapid evolution of IT infrastructure, as it has grown from mainframes to virtual systems and the cloud. Another axis revolves around data, as information management has shifted from simple files to databases to unstructured data. The third axis relates to how businesses build applications, which has advanced from monolithic applications to agile iteration and DevOps.

The difficulty presented by this constant innovation is that businesses now have different eras of technology all working together—or trying to work together—in a single IT infrastructure.

“One of the valuable things that BMC does for customers is that we allow them to manage all those different environments in one way,” Steif said. “A lot of customers, when they first come to BMC, they say, ‘Well, we have this thing we’re doing on the mainframe, and this thing we’re doing on these systems, and we have three or 300 different things we’re doing in the cloud—but they don’t talk to one another.’”

To unify systems, BMC develops a single cohesive automation and orchestration approach for these customers. “We allow them to have one way of managing across all those environments, which makes it easier to share data, to troubleshoot, and to understand what’s going on,” he said. “It gives you one language to speak rather than 27 different languages.”

One major benefit: this automation framework enables customers to focus on driving business value rather than getting bogged down in tech complexity.

BMC’s tool for this automation and orchestration is Control-M, which has recently been named a leader in the Garter Magic Quadrant for Service Orchestration and Automation Platforms (SOAP).

“It’s a tool that thousands of customers are using,” Steif said, “and it’s driving tremendous value in their automation journey.”

“One of the valuable things that BMC does for customers is that we allow them to manage all those different environments in one way,” according to Gur Steif, President, Digital Business Automation at BMC.

John McKenny: Planning for Constant Change

Finding optimal ways to handle change and strategize for a better outcome are topics that are close to John McKenny, SVP and GM, mainframe software business at BMC.

“One of the things that I spend quite a bit of time talking about is: what does change look like within an organization?” he said. “So when I’m asked for recommendations, I suggest: Always be very clear about the outcome that you want. What’s the destination? How can you define success?”

It’s essential to be specific. He recommends simplifying the process down to two or three essential criteria. Next, create a plan that goes from inception to destination, a plan that details expected progress over a specific timeframe. Then decide what your successful outcome will be—in detail.

“That sounds simple, but it’s amazing how many times people rush to implement technology and haven’t fully defined the benefits they want to achieve,” McKenny said. When leaders plan properly, “you keep people aligned and really empower them to make that happen.” Best of all, he said, “when you do it well, you’re surprised by the kind of outcomes that you achieve.”

McKenny acknowledges that planning is now harder than ever because rapid tech changes are happening even as planning takes place. How can business leaders plan when the basic functionality of their toolsets is evolving as they build their blueprint?

While this presents a major challenge, there are solutions that can help. For example, in a recent blog post, McKenny addresses how BMC pushes the boundaries of hybrid cloud solutions for mainframe, offering enterprise cloud-native capabilities optimized for modern data functionality.

“That’s why it’s so important to decide the destination first,” he said. “If you’re very clear on the destination and how you define success, then as things change along the way you can keep yourself on track despite the shifts. And you can only do that effectively when you’re clear about where you want to go.”

“If you’re very clear on the destination and how you define success, then as things change along the way you can keep yourself on track despite the shifts,” according to John McKenny, SVP and GM, mainframe software business at BMC.

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• Lori Rosano, MD & SVP, North American Public Cloud, SAP
• Beena Ammanath, Global Head of Deloitte AI Institute
• Ryan Manning, VP, Product Management, BMC Software
• Chad Dunn, VP, Product Management, AI and Data Management, Dell
• Nabil Bukhari, Chief Technology and Product Officer, Extreme Networks

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