Sunil Dadlani is an award-winning global executive serving as Chief Information, Digital, Cybersecurity, and Innovation Officer. A pioneer in human-centered design, he champions the transition from AI-enabled to AI-native ecosystems, implementing advanced Agentic and Generative AI technologies to create frictionless clinical experiences. Recognized as a multi-year Global CIO 100 honoree and one of the Top 50 Global Icons, Sunil drives enterprise-wide cloud governance, data strategy, and robust zero-trust risk frameworks. Educated at institutions including Harvard, Wharton, MIT, Cornell and Columbia, he possesses deep expertise in orchestrating secure, large-scale digital architectures that transform modern healthcare delivery. His career spans Fortune 100 companies across five continents — from Sony Motorola, Siemens and Novell to the New York State Department of Health.
Recently, in an exclusive interview with CIO Magazine, Sunil shared insights into why he left consumer tech for regulated, mission-driven sectors. On talent shortages, he said healthcare loses if it competes with Big Tech on salary alone. Systems must build, not buy, talent through reskilling, embed data scientists in clinical domains, and train clinicians in AI literacy. To a 22-year-old wanting to transform healthcare, he’d say spend time where care happens, observe workflows, pick one specific problem, build something small that helps, learn AI with clinical context, understand healthcare finance, and stay patient and curious. The following excerpts are taken from the interview.
Hi Sunil. Fortune 100 to government to healthcare is an unusual arc. What drew you from consumer tech into regulated, mission-driven environments like healthcare and public sector?
“Honestly, I didn’t leave consumer tech — I followed the hardest problems where impact would be most. And the hardest problems migrated into regulated industries.”
I started in Fortune 100 environments where the focus was scale, efficiency, and shareholder value. That experience taught me how to build systems that perform, optimize cost structures, and operate with discipline. But over time, I realized that the same digital and AI capabilities driving margin expansion in consumer tech could have far greater impact in sectors where outcomes are measured in human terms.
The transition to government and then healthcare was driven by that realization. These are some of the most complex, regulated, and operationally constrained environments in the world. Yet they are also the ones where the gap between what is possible and what is delivered is the widest.
Healthcare, in particular, operates with structural inefficiencies that would not be tolerated in other industries. We see fragmented data ecosystems, manual workflows, and administrative burdens that can account for nearly 25 to 30 percent of total healthcare spend. That is not just a cost problem. It is patient access, safety, and experience problem.
What drew me in was the opportunity to apply the rigor of consumer tech and enterprise discipline to a mission-driven system. Not to “digitize” healthcare at the surface, but to fundamentally re-architect it.
What do you love the most about your current role?
What I value most about my current role is the ability to operate at the intersection of mission, scale, and transformation.
Healthcare is one of the few industries where every decision carries both operational and human consequence. The work is not abstract. It is deeply tangible. When we improve access, reduce friction, or enable clinicians with better tools, the impact is immediate and visible in-patient care.
At the same time, the complexity is what makes it intellectually compelling. You are navigating clinical workflows, regulatory constraints, financial pressures, and rapidly evolving technology all at once. It forces you to think beyond point solutions and build systems that work end to end.
“The best technology leaders in healthcare aren’t the ones who understand the tech the deepest. They’re the ones who can sit in a room with a CMO, a CFO, and a frontline nurse — and make all three-feel heard, aligned, and confident in the same decision.”
You champion the transition from AI-enabled to AI-native ecosystems. What will be the clearest signal by 2030 that a health system has truly become “AI-native”?
By 2030, the clearest signal that a health system is truly AI native will not be the number of models deployed. It will be whether intelligence is embedded into every decision without friction, and trusted by the people making those decisions.
In an AI-enabled organization, AI sits on the side. It generates insights, dashboards, or recommendations that still require translation and manual action. In an AI native system, intelligence is part of the workflow itself. It is invisible, continuous, and operational.
When a health system becomes truly AI-native, the technology recedes entirely into the background. It becomes the foundational fabric of the organization.
Here is what that actually looks like on the ground:
- The friction just disappears. Ambient technology will listen to a patient-physician interaction, update the electronic health record, order the correct labs, trigger the pre-authorization with the insurance company, and queue up the billing codes—all seamlessly in the background without the doctor ever typing a word.
- The shift from reactive to autonomous operations. In the revenue cycle or supply chain, you won’t have teams logging in to manually fix billing errors or predict inventory shortages. You will have autonomous, agentic workflows that self-correct, manage denials, and optimize workflows in real-time, escalating only the most complex anomalies to a human expert.
- The metric shifts from “adoption” to “capacity.” Today, we measure success by how many people use a tool. In 2030, an AI-native system will measure success by how much time has been given back to the bedside.
The ultimate proof of an AI-native health system is that clinicians will feel like they can finally just be doctors and nurses again, and patients will feel like they are interacting with a human-centered ecosystem, not a bureaucratic machine.
Zero trust is evolving beyond network perimeters. In five years, what will “identity” mean in a hospital where humans, devices, and agents all request access?
We are moving from static identity to dynamic, contextual trust.
Today, identity is largely human centric and role based. A physician logs in, a nurse badges in, a device is registered. Access is granted based on predefined roles and network boundaries.
That model does not hold in a world of autonomous agents, connected medical devices, and continuous data exchange.
In the near future, identity will become multi-dimensional and continuously evaluated across three domains:
- Human identity
Not just credentials, but behavior, context, and intent. Access will adapt in real time based on location, activity patterns, risk signals, and clinical context. - Device identity
Every device, from infusion pumps to imaging systems, will carry a verifiable, cryptographic identity. Trust will depend on device integrity, software state, and real time posture, not just network presence. - Agent identity
This is the biggest shift. AI agents will not just assist. They will act. Each agent will require its own identity, permissions, accountability trail, and governance boundaries. You will need to know not just that an action was taken, but which agent took it, on whose behalf, and under what constraints.
What brings this together is a move toward continuous authentication and authorization, where trust is not granted once but recalibrated constantly.
You’ve been honored as a multi-year Global CIO 100 and Top 50 Global Icon. Our readers would love to know the secret mantra behind your success.
Honestly, there is no secret formula or magic bullet. If I look back at my career, from the Fortune 100 to government and now healthcare, my “mantra” comes down to a few simple, non-negotiable principles I live by every day.
First, leadership is about people, not protocols. You can have the most brilliant technology strategy in the world, but if your team doesn’t trust you, or if you don’t care about their growth, it’s just ink on paper. I’ve always focused on building high-performance teams where transparency is absolute and human-centered design is the default. My job isn’t to be the smartest person in the room; it’s to clear the runway so the brilliant people around me can do their best work.
Second, fall in love with the problem, not the technology. It’s easy to get blinded by the latest shiny object—whether that’s quantum computing or the newest AI model. But true enterprise value happens when you use technology to solve real, painful, human problems. In my current role, that means focusing on things like reducing clinician burnout or pulling millions of dollars out of operational waste so it can go right back to patient care. When your focus is entirely on solving the problem, the accolades tend to take care of themselves.
Finally, empathy and fiscal discipline must coexist. You can’t be a successful modern leader by just being a visionary, and you can’t do it by just being a ruthless cost-cutter. You have to balance both. You need empathy to understand how your decisions affect a nurse on the floor or a citizen using a government service, combined with the financial grit to negotiate hard, structure smart enterprise agreements, and protect the organization’s capital.
At the end of the day, success isn’t about the titles or the awards. It’s about building a legacy of trust, resilience, and meaningful impact in the lives of the people you serve.
What book — technical, philosophical, or historical — has the most notes in your margins, and which idea guides your weekly decisions?
One book that has shaped my leadership thinking is Good to Great by Jim Collins.
What stands out is the idea that enduring success is not driven by a single breakthrough or bold move. It is built through disciplined people, disciplined thought, and disciplined action, compounded over time.
That principle translates directly into how I operate week to week.
In healthcare, the temptation is to chase transformation through big, visible initiatives. But the reality is that sustainable impact comes from consistency. Putting the right leaders in place, being brutally honest about where you stand, and then executing with focus and discipline.
Two concepts from the book guide my decisions:
The Hedgehog Concept
Clarity on what you can be best at, what drives your economic engine, and what you are deeply passionate about. For me, that means focusing on building an AI native, digitally integrated health system that improves outcomes while remaining financially sustainable.
The Flywheel Effect
Transformation is not a single moment. It is a series of consistent pushes in the same direction. Each improvement in access, throughput, cost structure, or clinician experience builds momentum for the next.
So on a weekly basis, the question is not “What is the next big idea?”
It is “Are we pushing the flywheel in a consistent direction?”
Because over time, discipline compounds into transformation.
Talent shortages in cyber and AI are acute. How will health systems compete for and grow the next generation of “AI-native” engineers and clinicians?
Competing with Big Tech and Silicon Valley for AI and cyber talent on salary alone is a losing battle for healthcare. If we try to play that game, we lose before we even start.
To win the talent war, health systems have to change the rules of engagement. We shouldn’t just try to recruit tech talent; we have to build an ecosystem that grows it from within, and we have to pitch something Big Tech can’t offer: unmatched purpose and high-leverage impact.
There are three shifts that will define how leading systems compete and grow AI native talent.
- Build, not buy, the workforce
The supply of fully formed AI and cyber talent will remain constrained. The winning strategy is to create talent internally. That means structured reskilling programs for engineers, data scientists embedded in clinical domains, and clinicians trained in AI literacy and workflow design. The future is not pure technologists or pure clinicians. It is hybrid talent that understands both. - Create AI native operating environments
Top talent wants to work where they can build and deploy at scale. That requires modern data platforms, access to high quality data, responsible governance, and the ability to move from idea to production without friction. If your environment slows innovation, talent will leave. If it accelerates learning and impact, talent will stay. - Make purpose a competitive advantage
Few industries offer the ability to directly improve human lives at scale. But purpose alone is not enough. It must be paired with visible outcomes and empowerment. Engineers should see how their models improve patient care. Clinicians should have agency in shaping the tools they use.
If you could host dinner with three innovators from any era, who would be at the table and what would you ask them about building for humanity?
If I could bring together a dinner table to explore what it truly means to build for humanity, I would invite Steve Jobs, Alan Turing, and Jensen Huang.
Each represents a different dimension of innovation that continues to shape our world.
With Steve Jobs, the conversation would center on the human experience. He understood something many technology leaders still miss: technology has little value if it does not resonate with the human spirit. He did not build from engineering checklists. He built from intuition about how people feel and interact.
I would ask: As we move into a world of autonomous, agentic AI systems that can think and act on behalf of humans, how do we design these invisible yet powerful systems, so they deepen human connection rather than distance us from it?
With Alan Turing, the discussion would turn to the ethics and boundaries of intelligence. He laid the foundation for modern computing yet lived before society could fully grasp its implications.
I would ask: As machines begin to reason and act with increasing autonomy, where should we draw the boundaries to ensure intelligence remains aligned with human values?
With Jensen Huang, the focus would be on infrastructure as destiny. He has fundamentally reshaped the compute layer that powers the AI era.
I would ask: How do we ensure that the infrastructure we are building today remains accessible, sustainable, and broadly enabling, rather than concentrating innovation in the hands of a few?
Across all three, the underlying question is the same:
How do we scale intelligence in a way that strengthens humanity, rather than outpaces it?
What is your biggest goal? Where do you see yourself in 5 years from now?
My biggest goal right now is to successfully transition a major, integrated health system from being merely “AI-enabled” to truly “AI-native.”
Right now, the entire industry is caught up in the noise of pilots, point solutions, and chatbots that require extra clicks from our clinicians. My immediate focus is to engineer that out. I want to build a foundational, autonomous architecture—especially in areas like revenue cycle management and ambient clinical documentation—where the technology recedes into the background and simply does the heavy lifting. The metric of success isn’t technology adoption; it’s the number of hours we return to a doctor or nurse to spend at the bedside.
Where I See Myself in 5 Years
Five years from now, I see myself continuing to lead at the bleeding edge of where complex global technology, strategic business architecture, and societal mission intersect.
Whether that is expanding my scope to drive digital-first transformations across broader,
Specifically, over the next five years, I intend to focus on three things:
- Pioneering Agentic Governance: As autonomous AI agents begin executing workflows independently, the role of the technology leader shifts from deployment to governance. In five years, I want to have established the definitive enterprise playbook for how large-scale organizations safely orchestrate, audit, and de-risk autonomous AI systems.
- Securing Next-Gen Infrastructure: We are rapidly heading toward a world where quantum computing will threaten current cryptographic standards. I want to ensure that the critical infrastructure I oversee is completely migrated to post-quantum, zero-trust security frameworks well ahead of the curve.
- Mentoring the Next Generation: By then, the lines between a “technologist” and a “business leader” will have completely vanished. I want to spend a significant portion of my time coaching and building a world-class pipeline of “bilingual” executives who can seamlessly speak the language of deep data science, hard-nosed financial discipline, and human-centered leadership.
If a 22-year-old told you, “I want to transform healthcare with technology,” what would you tell them to do this year?
If a 22-year-old told me they want to transform healthcare with technology, I would probably say this first:
That is a great ambition. Now go spend time where care actually happens.
Healthcare looks very different from the inside. It is complex, fast paced, and often messy in ways you cannot fully understand from the outside. So the most important thing you can do this year is get close to the frontline. Sit with clinicians, observe workflows, talk to patients, and pay attention to where things break down or slow down.
Then, instead of trying to solve everything, pick one problem. Something real and specific. Maybe it is documentation burden, scheduling delays, or gaps in patient communication. Try to understand it deeply and build something, even if it is small, that actually helps.
At the same time, build your AI and data skills. But do it with context. Technology in healthcare is not just about what is possible. It is about what is safe, practical, and usable in real environments.
I would also encourage them to learn how healthcare works financially. It may not sound exciting at first, but it is critical. If you understand how value flows through the system, your solutions are much more likely to scale.
And most importantly, stay patient and stay curious.