How healthcare leaders can build AI confidence and culture
Healthcare organizations are navigating a crowded and confusing AI landscape. Recent reports show 95 percent of corporate AI pilots fail, and many executives see little return. Yet pioneers like Kaiser Permanente are showing what’s possible with careful governance and system-wide strategy.
We sat down with Justin Massa, founder of Remix Partners and AI expert, to talk about what leaders should prioritize today.
What advice would you give to healthcare leaders about staying competitive as AI adoption accelerates?
Justin Massa: Don’t treat AI like an IT project, treat it like a leadership challenge. The competitive edge will come not from buying tools, but from how well leaders reshape workflows, decision-making and incentives around those tools. Healthcare is already full of pilots and proofs-of-concept that never scale. The organizations that win will be the ones that force alignment: clinicians, administrators, IT and compliance all rowing in the same direction with executive sponsorship.
And in this moment, when the landscape is shifting month by month, leaders should avoid the trap of making a few very large, long-term bets. It is far smarter to place a larger number of smaller, shorter-term bets. That creates more surface area to learn quickly and to pivot as new capabilities emerge.
What capabilities should organizations start building now to make the most of AI in the future?
Justin: Two capabilities matter most: leadership fluency and experimentation muscle.
- Leadership fluency means executives and managers know enough about how generative AI works to ask the right questions and spot both hype and risk. You don’t need everyone to be a prompt engineer, but you do need leaders who can distinguish between a shiny demo and a capability that can actually scale across an enterprise.
- Experimentation muscle is particularly difficult in healthcare because of the payer, provider and patient complexity. Experimentation can feel unsafe in that environment. The organizations that thrive will be the ones that create clear guardrails and a clear strategy, so teams can experiment without losing trust or running afoul of compliance. This is about building confidence that “safe-to-try” experiments are part of the culture, not exceptions.
What risks do organizations face if they “wait too long” to engage with AI?
Justin: The most pressing risk is talent. If healthcare organizations wait too long, they will struggle on two fronts:
- Retention. Current talent, whether clinicians, administrators or analysts, are already seeing how AI can take the administrative grind off their plates. If you do not give them access to these tools, they will go to organizations that do.
- Acquisition. Just as importantly, you will make it nearly impossible to attract the kind of AI-native talent your future depends on. The people who will matter most for building sustainable AI advantage are growing up in an environment where probabilistic, generative tools are the default. If you are not already engaged with AI, they will not join you.
What signals should leaders be watching over the next 12–18 months to understand where AI in healthcare is heading?
Justin: There are healthcare-specific signals to watch:
- FDA clearances around AI-enabled diagnostics and decision support.
- Reimbursement models such as how CMS and major insurers define what is billable with AI-assisted care.
- Workforce sentiment including whether clinicians feel AI is augmenting or eroding their professional judgment.
- Vendor consolidation which, when it accelerates, signals maturing standards and reduced noise.
But healthcare leaders cannot afford to only watch through a healthcare lens. Much of the innovation is coming through “boring” business processes such as finance, HR and customer service. These are areas healthcare shares with every other industry. Leaders should track what companies outside of healthcare are doing with AI in these domains and adopt the best of it. Do not get so consumed by clinical use cases that you miss easy wins in the day-to-day work of running an enterprise.
What lessons from early adopters of AI can partners learn from today?
Justin: One of the clearest patterns in successful AI transformations is this: leaders use the technology themselves.
This is not something you can delegate. Generative AI represents a paradigm shift, moving from deterministic to probabilistic software. If leaders do not experience that shift firsthand, they cannot design the right strategies, set the right expectations or create the right culture.
Where leaders are hands-on, the whole organization moves faster, experiments more confidently and scales successful use cases. Where leaders are absent, resistance and fear take over. There is really no excuse anymore: leaders need to be in the sandbox, using AI daily, if they want their organizations to succeed.
Curious about AI implementation for your organization? Learn more about MATTER’s new AI leadership bootcamp.