Leading Healthcare AI

Temitayo Bewaji

Healthcare AI strategy for hospital executives, health-system leaders and chief AI officers. Each episode starts with the enterprise challenge, not the tool, and shows how leaders turn AI possibilities into decisions, investment and measurable value.  Most health systems now have an AI list: use cases, pilots, vendor demos, governance committees. What many lack is a strategic centre. They haven't decided which challenge deserves priority, where scarce clinical and technical capacity should go, or what should stop. Leading Healthcare AI works through that gap.  Hosted by Dr. Temitayo Bewaji of Bewaji Healthcare Solutions, the show follows one line of executive reasoning. Enterprise strategy sets direction. AI strategy interprets those choices and turns them into priorities, capabilities, investment, operating models and results. A recurring health-system case runs across episodes so you can see how each decision shapes the next.  You'll hear about:  - Translating enterprise strategy into a healthcare AI strategy  - Diagnosing the constraint that actually governs performance before choosing an AI solution  - Building AI investment cases that compete for capital and pass the CFO test  - AI governance, decision rights and the operating model for the CAIO and C-suite  - Implementation, adoption, and how to tell whether AI is creating value (and when to stop)  Most episodes close with a question or exercise your leadership team can use right away.  More from Bewaji Healthcare Solutions: https://bewajihealth.com  Talk through your AI strategy or governance approach in a 20-minute conversation: https://bit.ly/BookatBHS 

Episodes

  1. 5d ago

    Seeing the Inflection Point Before It Changes the Game

    How do healthcare leaders recognize a strategic inflection point before it shows up in market share and margins? In this episode of Leading Healthcare AI, we look at what happens when separate changes in technology, workforce, patient behavior, payment, regulation and competition start reinforcing one another. We also look at how to tell a genuine shift from temporary disruption. We start with Intel in 1985. Andy Grove asked Gordon Moore what a new CEO would do about the struggling memory business, and Intel left the market it had helped create. The pressure had been building for years. What made the decision hard was that it challenged what Intel believed it was. Healthcare organizations face the same risk: strategies built on assumptions that were entirely reasonable once and may now be quietly losing their footing. Drawing on Grove’s original definition and Rita McGrath’s work on seeing around corners, we cover: What a strategic inflection point is, and why a new technology isn’t one on its ownFour reasons successful organizations miss emerging shiftsWhy Kodak’s story is more complicated than “it ignored digital,” and what that means for choosing a responseMedicare telehealth’s 63-fold jump in 2020 as a healthcare precedent, and why adoption during a shock isn’t proof of a new normalSix forces converging around healthcare AI, including KFF’s finding that 32% of US adults now use AI chatbots for health informationHow leading, current and lagging indicators buy leadership timeFive responses: monitor, investigate, prepare, reconsider and adaptYou’ll leave with the Inflection Point Assessment, an eight-part test that includes the question most teams skip: what evidence would prove us wrong? You’ll also get an executive exercise built around one question: what could be changing today that we’ll wish, three years from now, we had started preparing for? For healthcare CEOs, boards, C-suite and clinical executives, chief AI officers, and anyone responsible for healthcare AI strategy, technology adoption or long-range planning. References Andrew Grove, Only the Paranoid Survive (1996)Andrew Grove, Academy of Management address (1998): https://www.intel.com/pressroom/archive/speeches/ag080998.htmRita McGrath, Seeing Around Corners (2019)Natalya Vinokurova and Rahul Kapoor, “The Problem of Sustaining a Successful Enterprise,” Business History Review (2025): https://www.cambridge.org/core/journals/business-history-review/article/problem-of-sustaining-a-successful-enterprise-kodaks-multiple-takes-at-strategic-renewal-that-culminated-in-failure/BE8CCCEC173E0D91B1E7F0BBE2BF2744KFF, Use of AI for Health Information and Advice (March 2026): https://www.kff.org/public-opinion/kff-tracking-poll-on-health-information-and-trust-use-of-ai-for-health-information-and-advice/FDA, Artificial Intelligence-Enabled Medical Devices: https://www.fda.gov/medical-devices/digital-health-center-excellence/artificial-intelligence-enabled-medical-devices

    Seeing the Inflection Point Before It Changes the Game

  2. Oct 20

    Signals, Not Headlines: What Changes Our Strategy?

    How do healthcare leaders know when something outside the organization has changed enough to revisit their AI strategy? In this episode of Leading Healthcare AI, we set out a practical method for strategic sensing: turning a constant flow of news into the few developments that should actually change a decision. We start with the WHO’s public health intelligence teams. In one week in June 2026, they scanned around 666,000 raw signals and escalated 23 for action. Healthcare strategy needs the same discipline. Most AI announcements, vendor claims and policy proposals don’t deserve a strategy meeting. The ones that matter touch an assumption your strategy depends on. Drawing on Rita McGrath’s work on strategic inflection points and George Day and Paul Schoemaker’s research on peripheral vision, we cover: The difference between information, a signal, a strategic implication and a decision triggerWhy sensing should start from your strategy’s assumptions, not from news alertsSix places to look: patients, workforce, financing, technology, regulation and competitorsThe Strategic Signal Test, six questions to ask before escalating any developmentHow to balance false alarms against missed signalsUsing signposts and triggers so leaders neither react to everything nor wait until change is undeniableWe also interpret three current signals: FDA authorization of more than 1,600 AI-enabled medical devices, the FDA’s 2026 discussion paper on generative AI medical devices, and the WHO’s projected shortfall of 11 million health workers by 2030. The last is a reminder that the signal that matters most for your AI strategy may have nothing to do with AI. You’ll leave with the Strategic Signal Card and an assumption-watchlist exercise your executive team can run in an afternoon. For healthcare CEOs, boards, C-suite and clinical executives, chief AI officers, and anyone responsible for healthcare AI strategy, governance or technology adoption. Chapters 00:00 666,000 signals 02:00 Information is not intelligence 04:05 Start with strategy, not news alerts 06:55 Snow melts from the edges 10:25 Detection is not interpretation 13:25 False alarms and misses 14:50 Three current signals 18:20 From signal to decision trigger 20:50 What strategic sensing is not 22:15 The assumption watchlist Resources Previous episode: Episode 4 — The Choices an AI Strategy Must MakeNext episode: Seeing the Inflection Point Before It Changes the GameReferences WHO Weekly Epidemiological Record, vol. 101, no. 25 (2026): https://www.who.int/publications/journals/weekly-epidemiological-record/wer101-25Rita McGrath, Seeing Around Corners (2019)George Day and Paul Schoemaker, “Scanning the Periphery,” Harvard Business Review (2005): https://hbr.org/2005/11/scanning-the-peripheryFDA, Artificial Intelligence-Enabled Medical Devices: https://www.fda.gov/medical-devices/digital-health-center-excellence/artificial-intelligence-enabled-medical-devicesFDA, “FDA Seeks Public Feedback to Inform Regulatory Approach for Generative AI-Enabled Medical Devices” (August 2026): https://www.fda.gov/news-events/press-announcements/fda-seeks-public-feedback-inform-regulatory-approach-generative-ai-enabled-medical-devices

    Signals, Not Headlines: What Changes Our Strategy?

  3. Oct 16

    The Choices an AI Strategy Must Make

    Every Formula 1 team works under a cost cap. Every upgrade, every hour of engineering and every dollar spent on this year’s car is taken from somewhere else. No team can say yes to every promising idea. Healthcare organizations face the same problem with AI. There will always be another model, another vendor, another pilot. The hard part isn’t finding possibilities. It’s choosing among them. This episode completes the first stage of the series by moving from translation to commitment. We set out nine choices a coherent AI strategy has to make: Where will we concentrate?What role will AI play?Which capabilities must we own?What will we build, buy, partner for, or defer?What principles will govern investment?What operating conditions must exist?What boundaries will we impose?How will we measure value?What will we explicitly not do?Along the way we draw on Porter, Playing to Win, Barney’s resource-based view and the NIST AI Risk Management Framework. We look at BCG’s July 2026 survey of CEOs, in which only 26% said AI was embedded in a broader business transformation. And we use the June 2026 Mayo Clinic–Microsoft collaboration to show a sourcing choice in practice: partnering for capability while keeping ownership of the strategically important asset. You’ll leave with the AI Strategy Choice Card, a one-page test of whether your AI strategy is complete. You’ll also get the case for an AI Stop List: a short set of deliberate exclusions, each of which should disappoint someone. For healthcare CEOs, boards, C-suite and clinical executives, and anyone leading AI, digital or innovation work. References Michael Porter, “What Is Strategy?”, Harvard Business Review (1996): https://hbr.org/1996/11/what-is-strategyA.G. Lafley and Roger Martin, Playing to Win (2013)Jay Barney, “Firm Resources and Sustained Competitive Advantage,” Journal of Management (1991)NIST, AI Risk Management Framework 1.0 (2023)BCG, “Nearly Nine in Ten CEOs See Some Cost or Revenue Benefits from AI” (July 2026): https://www.bcg.com/press/22july2026-ceos-cost-revenue-benefits-ai-struggling-scaleMayo Clinic and Microsoft announcement (June 2026): https://news.microsoft.com/source/2026/06/02/mayo-clinic-and-microsoft-collaborate-to-develop-a-frontier-ai-model-for-healthcare/Previous episode: From Enterprise Strategy to AI Strategy Next episode: Signals, Not Headlines: What Changes Our Strategy?

    The Choices an AI Strategy Must Make

  4. Oct 15

    From Enterprise Strategy to AI Strategy

    In May 1961, President Kennedy committed the United States to landing a person on the Moon before the end of the decade. NASA didn’t yet know how it would do it. Over the next year it had to turn that objective into an approach, then into capabilities, resources and a programme of work it could measure. Healthcare organizations face the same problem with AI. “Improve access” or “grow specialty care” is an ambition. Buying AI doesn’t tell you how to achieve it. Many organizations end up with an enterprise strategy on one slide and a list of AI use cases on another, and nothing showing how one leads to the other. This episode argues that AI strategy is a translation problem and walks through seven steps to make that translation visible: Extract the enterprise’s real strategic choices.Identify what must be true and what’s in the way.Apply the AI interpretation.Turn scattered opportunities into a single strategic priority.Define the capabilities that priority needs.Match ends, ways and means.Build the value logic before the portfolio.Along the way we draw on Porter’s Southwest Airlines example, Barney’s resource-based view, Teece’s dynamic capabilities and the Army War College’s ends–ways–means model. We also look at recent BCG and McKinsey evidence on the gap between AI activity and value, and at how England’s NHS has tied AI imaging investment to a measurable diagnostic objective. You’ll leave with the Strategic Traceability Test, which checks whether you can trace every AI priority back to an enterprise choice and forward to enterprise value. There’s also an eight-sentence exercise to test whether an AI initiative is ready to be called strategic. For healthcare CEOs, boards, C-suite and clinical executives, and anyone leading AI, digital or innovation work. References Michael Porter, “What Is Strategy?”, Harvard Business Review (1996): https://hbr.org/1996/11/what-is-strategyA.G. Lafley and Roger Martin, Playing to Win (2013)Richard Rumelt, Good Strategy Bad Strategy (2011)Jay Barney, “Firm Resources and Sustained Competitive Advantage,” Journal of Management (1991)David Teece, “Explicating Dynamic Capabilities,” Strategic Management Journal (2007)BCG AI Radar (January 2025): https://www.bcg.com/press/15january2025-ai-optimism-autonomous-agentsMcKinsey, The state of AI (2026): https://www.mckinsey.com/capabilities/operations/our-insights/the-state-of-aiPrevious episode: What Does AI Change About the Enterprise Strategy? Next episode: The Choices an AI Strategy Must Make

    From Enterprise Strategy to AI Strategy

  5. Oct 13

    What Does AI Change About the Enterprise Strategy?

    In March 2016, AlphaGo played a move against Lee Sedol that almost no human expert would have chosen. The board, the rules and the objective were unchanged. What changed was which moves made sense. Episode 1 argued that enterprise strategy has to come before AI strategy. This episode looks at the other side of that relationship. Every strategy rests on assumptions about costs, capabilities, competitors, workforce and what is possible, and AI can quietly make some of those assumptions unreliable. Drawing on Playing to Win, Porter, Rumelt, Prediction Machines and Brynjolfsson’s work on the productivity paradox, we cover seven ways AI can change the conditions a strategy depends on: economics, capabilities, the system, competition, workforce, decisions and the strategy space itself. We also look at why most AI news never reaches the level of a strategic issue, and what recent WHO/Europe data shows about the gap between naming AI priorities and funding them. You’ll leave with two practical tools, the Strategic Assumption Audit and the AI Strategic Impact Scan, and an exercise to run on your own strategy: name five assumptions it depends on, and ask which ones AI could break. For healthcare CEOs, boards, C-suite and clinical executives, and anyone leading AI, digital or innovation work. Chapters 00:00 Move 37 02:00 Strategy is built on assumptions 05:25 Don’t confuse change with strategic change 07:05 How AI changes economics and capabilities 10:25 How AI changes the system and competition 13:55 How AI changes workforce, decisions and the strategy space 17:30 The AI Strategic Impact Scan, and a WHO reality check 20:20 The specialty-care case and the AI leader’s role 22:50 Five assumptions, and back to Move 37 References A.G. Lafley and Roger Martin, Playing to Win (2013)Michael Porter, “What Is Strategy?”, Harvard Business Review (1996): https://hbr.org/1996/11/what-is-strategyRichard Rumelt, Good Strategy Bad Strategy (2011)Ajay Agrawal, Joshua Gans and Avi Goldfarb, Prediction Machines (2018)Erik Brynjolfsson, Daniel Rock and Chad Syverson, “Artificial Intelligence and the Modern Productivity Paradox” (NBER, 2017)WHO/Europe, Artificial intelligence is reshaping health systems: state of readiness across the WHO European Region (2025): https://www.who.int/europe/publications/i/item/WHO-EURO-2025-12707-52481-81028Previous episode: AI Strategy Begins with Enterprise Strategy Next episode: From Enterprise Strategy to AI Strategy

    What Does AI Change About the Enterprise Strategy?

  6. Jul 23

    AI Strategy Begins with Enterprise Strategy

    If someone handed you ten promising AI use cases, could you say which ones your organization should pursue? Not without knowing the strategy. Most healthcare AI conversations start too low: with tools, use cases and pilots. In this first episode of Leading Healthcare AI, we move the conversation up to where it belongs, to the enterprise's strategic choices. Drawing on Michael Porter, Lafley and Martin's Playing to Win, and Richard Rumelt, we set out a simple sequence for the whole series: enterprise choice → AI implication → AI choice. We work through it using a regional health system competing on complex specialty care. We also look at what goes wrong when organizations reverse the order, and what this means for the executive accountable for AI. The episode closes with three questions to ask about your three largest AI investments. Expect at least one of them to make the room go quiet. For healthcare CEOs, boards, C-suite and clinical executives, and anyone leading AI, digital or innovation work. Chapters 00:00 A list of AI opportunities is not an AI strategy 02:45 What we mean by enterprise strategy 06:15 The hierarchy: enterprise strategy → AI interpretation → AI strategy 10:45 What happens when you start with the technology 13:15 Diagnosis: what is the strategy responding to? 15:45 What does AI actually change? 17:45 What this asks of the AI leader 19:30 Three questions for your AI portfolio References Michael Porter, "What Is Strategy?", Harvard Business Review (1996): https://hbr.org/1996/11/what-is-strategyA.G. Lafley and Roger Martin, Playing to Win (2013)Richard Rumelt, Good Strategy Bad Strategy (2011)WHO/Europe, Artificial intelligence is reshaping health systems: state of readiness across the WHO European Region (2025): https://www.who.int/europe/publications/i/item/WHO-EURO-2025-12707-52481-81028Next episode: What does AI change about the enterprise strategy?

    AI Strategy Begins with Enterprise Strategy
  • 6 Episodes

About

Healthcare AI strategy for hospital executives, health-system leaders and chief AI officers. Each episode starts with the enterprise challenge, not the tool, and shows how leaders turn AI possibilities into decisions, investment and measurable value.  Most health systems now have an AI list: use cases, pilots, vendor demos, governance committees. What many lack is a strategic centre. They haven't decided which challenge deserves priority, where scarce clinical and technical capacity should go, or what should stop. Leading Healthcare AI works through that gap.  Hosted by Dr. Temitayo Bewaji of Bewaji Healthcare Solutions, the show follows one line of executive reasoning. Enterprise strategy sets direction. AI strategy interprets those choices and turns them into priorities, capabilities, investment, operating models and results. A recurring health-system case runs across episodes so you can see how each decision shapes the next.  You'll hear about:  - Translating enterprise strategy into a healthcare AI strategy  - Diagnosing the constraint that actually governs performance before choosing an AI solution  - Building AI investment cases that compete for capital and pass the CFO test  - AI governance, decision rights and the operating model for the CAIO and C-suite  - Implementation, adoption, and how to tell whether AI is creating value (and when to stop)  Most episodes close with a question or exercise your leadership team can use right away.  More from Bewaji Healthcare Solutions: https://bewajihealth.com  Talk through your AI strategy or governance approach in a 20-minute conversation: https://bit.ly/BookatBHS 

Information

  • Creator
    Temitayo Bewaji
  • Years Active
    2026
  • Episodes
    6
  • Rating
    Clean
  • Copyright
    © 2026 Leading Healthcare AI
  • Show Website
    Leading Healthcare AI