A health system can have tens of AI tools running across departments. Most CMOs know only a few. Usually just the ambient scribes and the big radiology ones, and maybe the voice responder for appointment management.

The reality of healthcare AI adoption in 2026 isn’t a top-down, carefully orchestrated rollout. It’s shadow AI.

But not the kind where employees use unapproved chatbots on their phones. It’s vendor-embedded AI.

How does that happen, though?

A health system approves a clinical documentation platform. Six months later, the vendor updates the software to include a predictive AI module. A revenue cycle tool gets a machine learning upgrade. A patient engagement platform adds an autonomous scheduling agent. None of these went through a new governance review. They slipped in through the side door of existing contracts.

The result?

22% of healthcare organisations have implemented domain-specific AI tools – a 7x increase over 2024 (US-specific). And according to a recent Censinet benchmarking study, while 70% of healthcare organisations have AI governance committees, only 30% maintain an enterprise-wide AI inventory.

That’s a 40-point gap between having a committee and actually knowing what’s running. Committees don’t govern anything if they can’t see what’s happening.

When an AI system processes the protected health information without inventory tracking, audit trails, or clear clinical ownership, it stops being an IT problem. It becomes a patient safety risk.

You can’t manage the clinical risk of an algorithm you don’t know exists. You can’t measure the ROI of a tool that isn’t being tracked. And you certainly can’t defend it in an audit.

The organisations closing this gap aren’t doing it by scheduling more committee meetings. They’re doing it by building operational visibility; at least that is what we are seeing, though it’s rare to see this approach, as of now. They are treating AI the same way they treat physical medical devices – with strict inventory, continuous monitoring, and clear clinical accountability for outcomes. They know exactly what is running, what data it touches, and whether it’s really improving care.

If your governance committee met tomorrow, could they pull a complete, accurate list of every AI tool currently interacting with your patient data?

In case the answer is no, you have a collection of algorithms. You do not have an AI strategy yet.

How is your health system tracking vendor-embedded AI?

References 

Shailendra Gupta
(Co-Founder and CEO of Mind IT Systems)

Shailendra Gupta co-founded Mind IT Systems in 2014. Over eleven years the company has modernised and rebuilt software for businesses across fintech, healthcare, supply chain, and business services — in India, the UAE, New Zealand, the UK, and the US. The decision between modernising and rebuilding comes up in almost every legacy engagement we handle, and the right answer is rarely obvious at the outset.