I recently saw a commencement ceremony where something surprising happened: a commencement speaker was booed by the crowd. The speaker’s message? That the graduates entering the workforce today would be fine; that AI was just another technology shift, and that the jobs would adapt.
The crowd wasn’t buying it.
I don’t blame them. Because here’s the truth that I think a lot of leaders are still dancing around: AI readiness is no longer a career development conversation. It is a survival conversation. The professionals who learn to work with AI, who understand it, leverage it, and build alongside it, are the ones who will define what health IT looks like in five years. The ones who don’t will find themselves not just behind, but potentially out.
That’s a hard thing to say. But I think health IT leaders owe it to their teams to say it plainly, and then do something about it.
The real risk isn’t replacement, it’s irrelevance
When people worry about AI, they often frame it as a replacement story: Will AI take my job? I understand the anxiety. Across industries, from big technology companies to industrial organizations, AI is already changing the shape of work and compressing many traditional entry-level roles that once served as the first step in a career.
Health IT is not immune to this dynamic, and AI holds significant promise for improving how we work. But in my experience, the more immediate risk is not that AI replaces your team. It is that professionals who know how to leverage AI effectively, partner with the business, solve complex problems, communicate clearly, and work confidently alongside AI agents will move ahead of those who do not.
The distinction matters, because it changes what leaders need to do. This isn’t about protecting jobs from a machine. It’s about making sure your workforce, at every level, has the skills to stay relevant as the nature of the work changes around them. This moment is not unlike other seismic revolutions (i.e., industrial era to the digital age) when the organizations that adapted fastest were the ones that invested early in new skills, new mindsets, and new ways of working.
Meeting in the middle: What AI readiness actually looks like
One of the most useful frameworks I’ve developed for thinking about AI readiness is what I call the “meet in the middle” model, and it starts with an honest acknowledgment that AI readiness doesn’t look the same for everyone.
For the more analytical, technical members of your team, the gap is often on the human side. As AI agents take over more of the analytical heavy lifting, the premium on stakeholder communication, empathy, and collaborative problem-solving goes up, not down. The engineer who can also navigate a room, who can translate technical insight into clinical or operational language, becomes exponentially more valuable.
For the more creative, communicative members of your team, the gap tends to run in the other direction. They don’t need to become data scientists, but they do need enough technical literacy to understand how AI agents work, what they can and can’t do, and how to work alongside them effectively.
The goal is convergence: moving both groups toward a shared middle ground where human skills and technical fluency reinforce each other. That intersection is where the most effective, and most resilient, health IT professionals of the future will live.
Building a culture of AI readiness: What we’ve learned at Stanford
At Stanford Medicine, we have the advantage of operating within an institution that has innovation in its DNA. But the principles we’ve built on are ones that any health IT organization can apply, regardless of size or resources.
Start with openness, not gatekeeping. AI exploration can’t be limited to data science teams or senior leadership. When we made AI tools and training accessible across roles, from operations to communications to project management, we saw something happen organically: people started sharing what they learned. That peer-to-peer diffusion is more powerful than any formal training curriculum (though we developed that too so that we all spoke the same language when it came to AI).
Invest in upskilling before you need to. The organizations that will struggle most are the ones waiting for the “right” training program or the “right” moment. There is no perfect rollout. Start building AI fluency now, even imperfectly, because the cost of waiting compounds quickly. Just as important, treat learning as progressive and continuous. Some tools and techniques your team learns today may eventually be replaced, but the act of learning builds the confidence, context, and adaptability needed to move more quickly to whatever comes next. The team that has already started will have a shorter, more achievable path than the team starting from zero.
Hire for curiosity and adaptability and keep reinforcing their value. I’ve changed the way I think about hiring because of this moment. Soft skills such as curiosity, the willingness to learn, and the ability to pivot are no longer secondary to technical qualifications. In a landscape that shifts as quickly as this one, adaptability may be the most durable skill on any resume. I hire for it first, and as an IT organization, we continue to reinforce curiosity through inquiry, experimentation, and peer learning. At Stanford, we are fortunate to have talented colleagues who are willing to share what they know and help others grow.
Make the culture safe for experimentation. AI learning accelerates when people feel safe trying things, failing, and sharing what happened. If your culture punishes experimentation, you will lose the people most likely to drive your AI agenda forward.
It’s not too late, but the window isn’t open forever
I want to end with something that feels important to say directly to health IT leaders: the urgency is real, but so is the opportunity.
Even now, teams and individuals can position themselves as AI-forward. Leaders can still reshape their workforce strategies, revisit how they hire, and create the conditions for the kind of learning culture that AI adoption requires. The window hasn’t closed.
But it also won’t stay open indefinitely. The health systems that define the next decade of care won’t be remembered for the AI tools they adopted: they’ll be remembered for building the teams that knew how to use them. That work is ongoing, unglamorous, and urgent, but it is also a leadership imperative.
And it starts with telling your teams the truth: this is not just career development.
This is survival, and you’re going to help them get there.
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