AI is reshaping how decisions get made in healthcare — from catching at-risk patients earlier to rethinking what it means to build software for an agentic world. We sat down to explore what that shift looks like in practice.
On patient visibility
Where do you see AI having the biggest impact on patient visibility — and on catching risks before they turn into bigger problems?
AI is a real force multiplier in healthcare. It’s helping bring together pieces of the patient journey that have traditionally been fragmented — clinical data, pharmacy activity, access, and affordability — into one clearer picture.
Once you have that kind of visibility, you can catch risks much earlier. A delayed refill, a coverage issue, early signs that a patient is disengaging — those signals show up sooner. The shift is from reacting after something goes wrong to getting ahead of it.
“AI doesn’t replace the human side — it gives care teams better insight so they can step in earlier and support patients in a more thoughtful, personalized way.”
On implementation
For teams integrating AI into patient care, what’s the one piece of advice you’d give them?
Start with the problem, not the technology. Pick a clear, high-impact pain point — why are patients dropping off therapy, where are prior auths getting denied — and let AI be the tool that helps you answer it. Don’t start with “we need an AI strategy.” Start with the outcome you’re trying to achieve.
Two things are non-negotiable. First, explainability — if a user can’t understand why the model is recommending something, they won’t trust it, and they shouldn’t. Second, keep users in the loop. The goal isn’t to replace judgment, it’s to amplify it. The best systems surface insights, highlight tradeoffs, and make it easier to act — but still leave room for nuance and empathy.
On what teams are missing
As product leaders race to implement AI, what’s the piece most teams are getting wrong right now?
We’re entering an era where AI agents aren’t just tools — they’re becoming users of our platforms. Agents that operate independently, navigate systems, and execute workflows without constant human oversight. And that flips a lot of assumptions.
Most teams aren’t designing for agent-driven interaction. Are your systems structured in a way an agent can reliably operate in? Are your APIs, permissions, and workflows built for that kind of access? And it’s not just a product question — it’s a go-to-market one too. If agents are doing more of the work, who are you really building for?
“The shift from AI as a tool to AI as a participant is the piece most teams are underestimating right now.”
