Q1: Redefining What's Possible in Healthcare Through AI

You've said that your vision is for Northwell to "not just compete, but to redefine what's possible" through digital technology. What does that actually look like in practice?

Can you share a specific example where AI has unlocked possibilities for patient care that simply weren't achievable three years ago?

KM: When I say we want to “not just compete, but redefine what’s possible,” I mean using technology to fundamentally change the experience of healthcare and not just simply digitizing the way we have always worked.

Ambient AI is a great example. Three years ago, it would have been difficult to imagine technology listening to a natural conversation between a physician and patient, understanding the clinical context, and helping create the documentation in real time.

Today, that is becoming a reality and truly a foundational capability that we need to provide all physicians. The real value isn't just creating a note faster; it's giving physicians their time and attention back. Instead of focusing on a screen or documentation, they can be more present and focused on the patient. This is a win-win for both the patient and the physician.

That’s what redefining what’s possible means to me: using technology to make healthcare more efficient, but also more human.

Q2: From Strategy to Execution: Building Enterprise AI at Healthcare Scale

Northwell Health is one of the largest health systems in the US.

What are the unique challenges of implementing enterprise AI strategy at this scale, and how do you prioritise which AI initiatives get resourced first?

How do you balance innovation with the operational realities of running a complex healthcare organisation?

KM: At our scale, one of the biggest challenges with AI isn’t the technology, it’s the people and culture transformation around it. We have to prepare a large and diverse workforce to work differently, build new skills, and become comfortable with technology that is evolving incredibly quickly. That requires investment in AI fluency, education, and leaders who model new ways of working.

At the same time, there are hundreds of potential AI opportunities, so prioritization is critical. We focus resources where AI can have the greatest impact on patient experience, clinical outcomes, workforce experience, and operational performance, while also considering feasibility, scalability, and risk.

Balancing innovation with operational reality means being deliberate about where we move fast. We can experiment and iterate quickly in lower-risk areas, while AI that touches clinical care requires a much higher bar for safety, quality, privacy, security, and ethics.

Ultimately, AI transformation is as much about people and leadership as it is about technology.

Q3: Proving AI Value in Healthcare: Beyond the Hype

Healthcare leaders are being asked to invest significantly in AI whilst demonstrating clear ROI.

How do you measure AI's business value at Northwell?

What metrics matter most, and how have you built the business case for continued AI investment?

KM: We take a very disciplined approach to measuring AI value, but we also recognize that ROI in healthcare isn’t always purely financial. We look across several dimensions: financial and operational performance, workforce productivity and experience, patient experience, and ultimately clinical quality and outcomes.

The metrics depend on the use case. For automation, it might be hours returned to the workforce, reduced cost, or improved throughput. For patient access, it could be reduced wait times, improved conversion, or a better patient experience. For clinicians, it might be reduced administrative burden and time spent documenting outside of work.

We also measure adoption because an AI solution that people don’t use creates no value. That means looking at utilization, engagement, and whether AI is actually changing how work gets done.

The business case for continued investment comes from demonstrating those outcomes at scale and proving that AI is creating measurable value for our patients, workforce, and organization.

Q4: Clinical vs Administrative AI: Where's the Bigger Impact?

You're accelerating AI adoption across both clinical and administrative functions.

Where are you seeing the most compelling ROI right now?

Are there surprising use cases that have delivered more value than expected, or high-profile applications that haven't lived up to the hype?

KM: We’re seeing some of the most compelling ROI where AI removes administrative burden from high-volume, repetitive work. Ambient clinical documentation is a great example as it can give meaningful time back to physicians while improving their experience and allowing them to focus more fully on patients.

We’re also seeing significant potential across back-office functions like revenue cycle, finance, supply chain, HR, and other administrative workflows where AI can automate tasks, improve throughput, and allow our workforce to focus on higher-value work. Sometimes these less visible use cases can deliver some of the most immediate and measurable value.

Where I think we need to be careful is assuming every highly visible AI application is ready to transform healthcare today. For example, fully autonomous clinical decision-making, gets significant attention, but the technology, evidence, and governance need to mature further.

For us, some of the greatest value comes when AI fundamentally improves the experience in healthcare, both for our patients we serve and for our workforce to deliver.

Q5: Building Trust: Your Approach to Responsible and Ethical AI

Healthcare AI decisions can directly impact patient outcomes, making responsible AI implementation absolutely critical.

What does your governance framework look like at Northwell?

How do you ensure AI systems are not only effective but also ethical, equitable, and trustworthy?

KM: In healthcare, responsible AI is non-negotiable to how we adopt and scale AI. At Northwell, our governance starts with an AI Executive Committee made up of C-suite leaders who provide enterprise-level strategy, prioritization, and oversight.

We also have a dedicated AI Risk, Security and Ethics Committee that brings together clinical, technology, cybersecurity, privacy, legal, ethics, and other perspectives to evaluate AI through a risk-based lens. We assess areas such as patient safety, effectiveness, privacy, security, transparency, bias and equity, with the level of rigor increasing based on the potential impact of the AI.

Importantly, AI isn’t governed as a separate technology track. All AI requests are integrated into our broader enterprise technology intake process, giving us visibility and consistent governance from idea through implementation and ongoing monitoring.

Q6: Navigating the Regulatory Landscape

Healthcare is one of the most heavily regulated industries, and AI is introducing new compliance challenges.

How are you navigating the evolving regulatory environment whilst maintaining innovation at speed?

KM: The regulatory environment for AI is evolving quickly, so we have to build an approach that can evolve with it. We stay closely connected to emerging federal and state requirements, industry standards, and best practices, and continuously adapt our governance and controls as the landscape changes.

The key is embedding regulatory, legal, privacy, security, and ethical considerations early in the AI lifecycle rather than treating compliance as a final checkpoint. That allows us to identify risks sooner and avoid slowing innovation later.

We also take a risk-based approach to speed. In lower-risk administrative use cases, we can experiment, learn, and iterate quickly. When AI touches clinical decision-making, sensitive patient data, or patient safety, we deliberately apply greater scrutiny and oversight.

Q7: The Foundation: Data, Cloud, and Security

Your strategic focus includes modernising data infrastructure and migrating to the cloud as part of Northwell's digital transformation.

Why are these foundational elements so critical to AI success?

KM: AI is only as powerful as the data and technology foundation underneath it. In a large health system, data exists across clinical, operational, financial, and consumer systems, and bringing that information together in a trusted and usable way is essential to scaling AI across the enterprise.

That’s why modernizing our data infrastructure and moving to the cloud are foundational parts of our strategy. Cloud gives us the scalability, computing power, and flexibility to take advantage of rapidly evolving AI capabilities, while a modern data platform helps ensure AI is working from high-quality, governed, and accessible data.

It’s also about speed. A strong foundation allows us to move from individual AI pilots to enterprise capabilities that can be deployed and scaled much more quickly.

Q8: AI Adoption: Winning Hearts and Minds

Technology transformation isn't just about systems, it's about people.

How are you cultivating a culture of innovation and AI adoption amongst clinicians and staff?

Have you encountered any resistance and if so, how have you overcome it?

KM: Technology transformation is ultimately people transformation.  We cannot achieve meaningful adoption unless our clinicians and team members understand, trust, and see how it improves their work.

We’re focused on building AI fluency across the organization—from education and hands-on learning to giving people opportunities to experiment with AI in their everyday work. Leadership is critical to building AI fluency. We expect our leaders to use AI themselves, demonstrate curiosity, and model new ways of working so adoption comes from the top.

There is naturally some anxiety around AI, particularly around how AI may change roles and ways of working. We address that through transparency, education, and engagement—bringing clinicians and staff into the process rather than imposing technology on them.

Our goal is to create a culture where AI is embedded in everyday work and people feel empowered to identify new ways it can improve their work and the experience of our team members and patients.

Q9: The Next Frontier: Where Healthcare AI Is Heading

Looking ahead to 2027 and beyond, what emerging AI capabilities are you most excited about for healthcare?

Where do you see the biggest opportunities for AI to transformation?

KM: I’ve been very excited about the evolution from generative AI that primarily provides information or assists with individual tasks to agentic AI that can increasingly take action and orchestrate complex workflows.

In healthcare, that creates tremendous possibilities. AI can be so powerful when it doesn’t just tell a patient what they need to do next but helps navigate their entire journey—finding the right provider, scheduling care, coordinating follow-up, answering questions, and proactively helping them stay on track.

There is equally significant opportunity for our workforce. AI agents can take on many of the repetitive administrative tasks surrounding clinical and operational work, allowing people to focus more of their time on patients, problem-solving, and higher-value activities.

The biggest transformation will come when AI moves beyond individual use cases and becomes embedded across the healthcare experience.  It’s about connecting people, data, and workflows to make care more proactive, personalized, and seamless.

Q10: Your Message for AI Leaders at The AI Summit New York

As organisations grapple with AI strategy and implementation, what's the one piece of advice you'd give to fellow executives?

What should they be thinking about as they plan their 2027 AI roadmap?

KM: My biggest advice is to think about AI as much more than a technology strategy as it will impact the entire organization. It is a business, workforce, and transformation strategy and an opportunity to reimagine how your organization operates and delivers value.

As executives build their 2027 roadmaps, I would focus on where AI can have the greatest impact and fundamentally improve how work gets done. Identify high-value opportunities, build the data and technology foundation to scale them, and invest just as deliberately in your people, culture, and governance.

Leadership is also incredibly important. Executives have an opportunity to become AI fluent themselves, embrace the technology, and model the curiosity and adaptability they want to see across their organizations.

AI will continue to evolve incredibly quickly. The organizations that thrive will be those that create the foundation, culture, and capabilities to continuously learn, adapt, and take advantage of what becomes possible.

Conclusion:

Kristin Myers proves that healthcare AI transformation is as much about people and culture as it is about technology. Her disciplined approach to ROI, responsible governance, and workforce adoption offers a blueprint for leaders navigating AI at enterprise scale. Want to learn how to balance innovation speed with clinical safety? Ready to understand what AI metrics actually matter? 

Join Kristin's session at The AI Summit New York 'The Trust Gap: When AI Makes Decisions, Who Is Accountable?' taking place on Thursday, December 10, 1:35pm on the Headliners stage and discover how to turn AI investment into real business impact.