𝗘𝘅𝗰𝗶𝘁𝗲𝗱 𝘁𝗼 𝘀𝗵𝗮𝗿𝗲 𝗺𝘆 𝗳𝗶𝗿𝘀𝘁 𝗹𝗼𝗼𝗸 𝗮𝘁 𝘁𝗵𝗲 𝗰𝗼𝘃𝗲𝗿 𝗮𝗿𝘁 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘀𝗲𝗰𝗼𝗻𝗱 𝗲𝗱𝗶𝘁𝗶𝗼𝗻 𝗼𝗳 𝘼𝙄 𝙞𝙣 𝙃𝙚𝙖𝙡𝙩𝙝 - 𝘼 𝙂𝙪𝙞𝙙𝙚 𝙩𝙤 𝙒𝙞𝙣𝙣𝙞𝙣𝙜 𝙞𝙣 𝙩𝙝𝙚 𝙉𝙚𝙬 𝘼𝙜𝙚 𝙤𝙛 𝙄𝙣𝙩𝙚𝙡𝙡𝙞𝙜𝙚𝙣𝙩 𝙃𝙚𝙖𝙡𝙩𝙝. When HIMSS and CRC Press published the first edition in 2020, most health leaders were still asking whether AI would ever truly arrive. That question has been answered. The transformation of health and medicine is underway. The question that matters now is what skills, mindsets, and strategies will separate health leaders who shape this transformation from those who get swept along by it? That's what this new edition is built to answer. Look for it this fall.

If your AI initiatives are failing to drive measurable value, it’s not because you picked the wrong technology. It's failing because of people – And it’s usually not their fault. A 2026 Harvard Business Review survey says 93.2% of executives point to cultural challenges and change management as the #1 barrier to AI adoption, which drives value. Technology challenges? 6.8%. This has been true every single year since 2021. Here's the hard truth: You've invested in the tools. You've hired great tech talent. But many leaders who get caught up in the vision of AI-driven innovation miss something that is Critical-to-Success: Innovation is a voluntary act. You cannot force people to embrace something new. You can only create the conditions that make them want to. And so, are you investing as much in upskilling your workforce as you are in your tech investments? One of the first questions I ask leaders in my advisory work is a simple but important starting point: Is AI part of your HR plan?

𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗶𝘀 𝗲𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝗶𝘁𝘀 𝗻𝗲𝘅𝘁 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗰𝘆𝗰𝗹𝗲—𝗮𝗻𝗱 𝗔𝗜 𝗶𝘀 𝗮𝘁 𝘁𝗵𝗲 𝗰𝗲𝗻𝘁𝗲𝗿 𝗼𝗳 𝗶𝘁.  I joined Wilmington Trust on their Capital Considerations podcast to unpack what AI really means for the healthcare investment landscape—and why we’re still in the early stages of a long transformation cycle. Done right, AI will lead to the creation of Intelligent Health Systems which will augment humans in our ability to improve the provision of health and medical services across all touchpoints, experiences and channels. Appreciate the conversation with Tony Roth , Chief Investment Officer, for a thoughtful discussion on where this is all heading. Listen to the podcast here. T.

𝗪𝗵𝗲𝗻 𝗔𝗜 𝗶𝘀 𝗱𝗼𝗻𝗲 𝗿𝗶𝗴𝗵𝘁, 𝗲𝘃𝗲𝗻 𝗼𝘂𝗿 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝗳𝘂𝗲𝗹 𝗳𝗼𝗿 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀, 𝗶𝘀 𝘁𝗵𝗲 𝘁𝗵𝗲𝗺𝗲 𝗼𝗳 𝗺𝘆 𝗹𝗮𝘁𝗲𝘀𝘁 𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗳𝗼𝗿 𝙄𝙣𝙨𝙞𝙙𝙚 𝙋𝙧𝙚𝙘𝙞𝙨𝙞𝙤𝙣 𝙈𝙚𝙙𝙞𝙘𝙞𝙣𝙚. AI isn’t just a tool. It’s a catalyst for becoming a master adaptive learner—helping each of us personalize how we grow, think, and lead in an AI-enabled world. If you’re a leader, clinician, or knowledge worker trying to make sense of AI’s impact on your role… this article is for you . T.

𝗜 𝗺𝗶𝘀𝘀𝗲𝗱 𝗛𝗜𝗠𝗦𝗦 𝗯𝘂𝘁 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝘁𝗵𝗮𝘁 𝗺𝘆 𝗯𝗼𝗼𝗸 𝘼𝙄 𝙞𝙣 𝙃𝙚𝙖𝙡𝙩𝙝 𝘄𝗮𝘀 𝘁𝗵𝗲 #𝟭 𝗯𝗲𝘀𝘁𝘀𝗲𝗹𝗹𝗲𝗿 𝗮𝘁 𝘁𝗵𝗲 𝗛𝗜𝗠𝗦𝗦 𝗯𝗼𝗼𝗸𝘀𝘁𝗼𝗿𝗲 𝗶𝗻 𝗟𝗮𝘀 𝗩𝗲𝗴𝗮𝘀 𝗹𝗮𝘀𝘁 𝘄𝗲𝗲𝗸. That surprised me. Because the book was published six years ago — which in AI years feels like ancient history. Since then, healthcare has moved from the age of AI curiosity to the age of AI accountability. Which makes the timing of this news even better. I’ve just signed an agreement with Taylor & Francis / CRC Press to publish a second edition of AI in Health coming this fall. Thank you to everyone who has read, shared, and applied the ideas from the first edition of AI in Health (and my other books). Stay tuned. T.

Looking forward to the DHAI Summit. I'll be keynoting and learning alongside so many talented leaders who are pushing the boundaries of what's possible at the intersection of AI and healthcare. My keynote will explore how health organizations can move beyond hype and experimentation to achieve real-world, scalable AI impact — and what it truly means to put the Human + AI equation to work in ways that are responsible, practical, and value-driven. If you're working in digital health, life sciences, or healthcare AI, this is a great place to be. 📅 June 8–9, 2026 📍 Omni Boston Hotel at the Seaport

We've reached a strange paradox in healthcare: AI adoption is accelerating, investment is growing, and vendor solutions are multiplying — yet most organizations are not seeing AI value actually scale across clinical outcomes, financial performance, or operations. I see this pattern constantly. Healthcare is good at starting AI. We are far less good at turning it into something that measurably and consistently changes results for the people we serve. Here are the five reasons I see most often, and one overlooked leadership skill that, when present, dramatically increases the odds of success. Five Reasons AI Value Stays Locked 1. No clear definition of value — before you start In my experience, many organizations deploy AI without first agreeing on what success looks like in measurable terms. Metrics are vague, retrospective, or disconnected from clinical or financial performance. Without a pre-defined baseline and agreed-upon KPIs, there's no way to demonstrate ROI — and initiatives drift without accountability. Projects get declared "complete" at go-live, not at value realization. 2. Pilots that go nowhere Healthcare is highly adept at piloting AI. Industrializing it is another story. I've watched impressive pilots stall out more times than I can count — and the data backs this up: only about 30% of AI healthcare pilots make it to full production. Most are siloed experiments with no integration into operational workflows, no change management plan, and no budget for enterprise rollout. The result is a graveyard of promising ideas that never escape proof-of-concept. Value locked in pilots cannot move system-level metrics.  3. Data that isn't ready AI performs only as well as the data it consumes. In healthcare, that data is fragmented across EHR systems, legacy platforms, and departmental silos — with inconsistent coding, missing fields, and poor interoperability. Where data does exist, governance structures around quality, access, and lineage are often immature. The result is models that underperform, eroding the clinical trust that adoption depends on. 4. Change management treated as an afterthought This one frustrates me most. AI implementation is too often treated as a technology project rather than a people and process transformation. Insufficient time and resources go toward training end users, redesigning workflows, and building the clinical and operational champions needed to sustain adoption. Without behavioral change, technology plateaus well below its potential — or simply goes unused. 5. AI chasing the wrong priorities Many AI investments I see are driven by vendor marketing, peer benchmarking, or departmental enthusiasm rather than enterprise strategic need. The result is a portfolio of capabilities that don't address what leadership is actually accountable for: cost reduction, quality metrics, patient access, margin improvement. Optimizing the wrong things produces activity, not outcomes. The Real Problem Isn't the Technology Here's what I've come to believe after working through these challenges with many organizations: the AI tools available today are genuinely capable of driving meaningful outcomes. The failure points are structural, organizational, and strategic — rooted not in how algorithms work, but in how leaders plan, deploy, govern, and evaluate. The single most important thing I've learned about scaling AI innovation is this: innovation is a voluntary act. You cannot force people to embrace something new. You can only create the conditions that make them want to. The Hidden Leadership Skill: Become a Bridger This is where a framework I recently encountered has genuinely shifted how I think about the problem. In their new book Genius at Scale, Linda A. Hill, Emily Tedards, and colleagues introduce what I consider one of the most useful concepts for scaling innovation I've come across. They call it "Bridging" — and the leaders who do it well, they call "Bridgers." Bridgers are not the loudest voices in the room. They are not necessarily the most technical. What makes them disproportionately effective is their ability to work at the intersection of people, trust, and collaboration across organizational boundaries. In practice, I see this play out in three ways: Bridgers curate the right partners. They are deliberate about who needs to be at the table — not just the technology team or the clinical champions, but operational leaders, finance partners, and the frontline staff whose workflows will actually change. And they bring these people in early, before decisions are made. Bridgers build mutual trust. They listen more than they advocate. They communicate upward, downward, and laterally. What I find most powerful here is their understanding that persuading someone to embrace a new idea requires understanding not just what they say — but what they value, what they fear, and what's already weighing on them when they show up to work. Bridgers create mutual commitment. They don't hand off and move on. They stay engaged through implementation, through the hard middle, through the moments when adoption stalls and the metrics aren't moving yet. They hold coalitions together when momentum fades. When I step back and look at the organizations that are actually scaling AI value, this is what I see in common. It's not the most sophisticated technology stack. It's not the biggest budget. It's leaders who understand that done right, AI in healthcare isn't fundamentally about technology — it's about empowerment. Helping clinicians be better at what they care about. Giving operational leaders clearer signal through the noise. Helping mission-driven organizations learn faster than their problems are growing. Leaders who embrace this won't just deploy AI. They'll scale it into something that genuinely changes what their organizations can do. The question I keep coming back to is a simple one: Who in your organization is playing the Bridger role right now?

Excited to share FINN Partners’ new eBook: Human-First Health Information: How AI, Data, and Innovation Are Rewriting the Future of Care. As healthcare enters a new era of AI-enabled decision-making and data-driven transformation, one principle remains essential: human-first health information. Trusted, accessible, actionable insights that improve outcomes and strengthen patient experience. I contributed a chapter focused on mastering your AI learning journey.  Click on this link to download a free copy. T.

Had bumper stickers existed in the 1850s, Dr. John Snow might have had one on the back of his carriage that later became popular in the 1960s with the counterculture crowd that read: Subvert the Dominant Paradigm It was 1854, and a deadly cholera outbreak was tearing through London. At the time, the medical establishment believed cholera spread through miasma—a poisonous cloud of bad air. John Snow, an unknown physician who lived in the affected neighborhood, saw something different. As he watched neighbors die, he became convinced the disease wasn’t airborne—it was waterborne. When Snow presented his theory to London’s medical leaders, he was dismissed. But he persisted. Through interviews, careful observation, data tables, and his now-famous map, Snow traced the outbreak to a contaminated water pump. His work helped stop the epidemic—and gave rise to what we now call epidemiology. Healthcare has been here before. Fast-forward to the 1970s. Even with mounting evidence, endoscopic surgery faced strong resistance. Leading surgeons believed “large problems required large incisions.” Minimally invasive “keyhole” surgery was dismissed. Today, endoscopy is recognized as one of the most important breakthroughs in modern medicine. Change is hard—especially in healthcare.  Since medicine emerged as a data-driven scientific discipline, progress has depended on leaders willing to challenge prevailing assumptions. Vaccines. Antibiotics. Sanitation. Clean water. Preventive care. None of these advances came from doing more of the same. Standing behind every major leap forward were leaders who shared two traits: They saw problems through a different lens. They were willing to challenge the status quo to make healthcare better. To be clear: thinking differently does not mean ignoring evidence or freestyling in the operating room. Medicine depends on rigor, standards, and proven best practices. But progress happens when we apply that science in new ways—more inclusive, more efficient, and more effective ways. The art and science of thinking differently Steve Jobs famously made “Think Different” a rallying cry, reminding us that the people who change the world are often the ones who see it differently. True innovators connect the unconnected. They combine ideas across disciplines. They don’t just play the game better—they change the game. Yet not all leaders are equal when it comes to innovation. Research shows that successful innovators spend significantly more time deliberately trying to think differently. For many people, this doesn’t come naturally—and it can feel uncomfortable or exhausting. The good news? Thinking differently is a skill, not a gift. Most of our innovation capacity is shaped by environment and practice, not genetics. With repetition, what once felt uncomfortable becomes energizing—and that’s when the best ideas emerge. History is full of reminders that even breakthrough ideas take time to find their true purpose. Early visions for the telephone included using it merely to notify people that a telegraph message had arrived. And now, here we are—with AI. AI has exploded into healthcare and society, driving change at a pace few organizations are prepared for. What works today will feel outdated tomorrow. Leaders who are complacent with the current state of healthcare will be eclipsed by those who think differently, plan creatively, and act with intent. While many health leaders talk about innovating with AI, I’m looking for the misfits—the ones whose ideas make traditionalists uneasy, but who ultimately move health and medicine forward. They’re the ones who’ve always changed the world. Could that be you? T.