Chariot Solutions

85% of healthcare organizations have explored or adopted AI. Only 18% are actually ready to deploy it in care delivery.

That gap? It’s where most AI projects quietly lose steam.

The pressure to move is real. Boards want a strategy. Investors want a roadmap. But the top obstacles to AI success are data quality and readiness (43%), lack of technical maturity (43%), and shortage of skills and data literacy (35%).

Those are not technology problems. They are infrastructure and organizational problems, and they do not get solved by picking a vendor. Informatica

The gap between adoption and readiness is the defining challenge in Health Tech AI right now.

The organizations making real progress are the ones that got honest about where they stood before they started building. That work is less exciting than a product demo. It also tends to be the difference between a proof of concept and something that actually ships.

That's exactly the work Chariot Solutions does with healthcare and life sciences organizations every day. We are not just building the technology, but building the AI readiness behind it.

If your team is stuck in the gap between exploration and deployment, let's talk.

Last week, for our Tech Talks for HealthTech, we partnered with Layer 8 Security to host a panel called AI Is Writing Code. But Who's Responsible for What It Gets Wrong?

A few things that stuck with us:

The clean code illusion. AI-generated code often looks right. But as panelist Dan Costantino pointed out, the real danger isn't rudimentary bugs — it's architectural decisions that look fine on the surface and create problems at scale.

Speed without governance is a trap. Panelist Matt Winter put it plainly that we can't take all the gains on the front end without discipline on the back end. In health tech especially, a bad actor is always looking for your mistake and the investment to prevent that has to be made.

The skills question is real. Are we moving from developers as authors to developers as editors? Maybe. But both panelists agreed that understanding how to code still matters. Architectural judgment, pattern recognition, knowing when the AI is wrong, those are human skills, and we can't afford to let them atrophy.

Defense has a structural advantage, but attackers only need one way in. Security teams that aren't using AI to keep pace are already behind.

This is exactly the kind of conversation our industry needs to keep having.

And let us know if you want to be on our events mailing list for fall events.

If AI writes the code, who is accountable when something breaks?

Join us at Philly Tech Week for a timely conversation on the future of software, security, and accountability. “AI Is Writing Code. But Who's Responsible for What It Gets Wrong?” brings together Jeff Lipson of Layer 8 Security, Dan Costantino of ModMed, Matt Winter of Tiger Biosciences, and additional panelists to explore what this shift means in practice.

With reports showing that nearly half of AI-generated code contains security flaws, this session will dig into what developers, security leaders, and health tech teams need to think about now.

Wednesday, May 13
5:00 – 7:00 PM
University City Science Center, Philadelphia

Register here: https://ow.ly/5YSZ50YP1E2