AI Governance at the Site: Practical Frameworks Sites Need Now
Why meaningful AI transformation in clinical trials depends on what happens at the site level
Sites are not facing a single AI governance challenge. They’re facing two simultaneously — and they have almost no infrastructure for either.
On one side, sponsors and CROs are pushing AI tool adoption at an accelerating pace, often without providing sites the training, validation guidance, or vendor standards needed to use those tools responsibly. On the other hand, sites are being scored, ranked, and selected by AI-driven algorithms they cannot see, question, or appeal — based on data they may not even know is being captured.
The site is expected to govern AI and be governed by it at the same time, and largely without support. That is not a sustainable position.
Three Friction Points Holding Sites Back
- The Policy Vacuum
Most sites have no AI use policy. Without organizational guardrails, staff are making individual decisions about AI use, sometimes in regulated workflows, entirely on their own. This is not a reflection of carelessness. Structured guidance built specifically for site-level AI governance simply does not exist yet. The industry has not provided it, and sites cannot be expected to build it from scratch.
- The Vendor Trust Problem
When sponsors and CROs deploy AI tools at sites, critical questions frequently go unanswered: What operational or patient-adjacent data is being collected during tool use? How is that data used beyond the immediate workflow? And perhaps most concerning, is a site’s own performance data feeding the very algorithms that will later score, select, or reject them? Data use policies are rarely disclosed in a form that sites can meaningfully evaluate, question, or negotiate. That needs to change.
- The Training Gap
Enterprise-level AI governance frameworks, when they exist at all, rarely translate to the coordinator or PI level. The people closest to the patient — the ones whose daily decisions shape data quality, protocol adherence, and participant experience — are consistently the last to receive structured guidance on AI use. That inversion carries real risk.
What a Practical Framework Actually Requires
These are not unsolvable problems. But solving them requires moving from abstraction to structure. Three requirements define what a workable framework looks like.
Transparency Is Non-Negotiable
If AI is being used to evaluate site performance, sites deserve to know the inputs that drive the evaluation, the logic by which factors are weighted and interpreted, and the recourse available when they want to question or appeal a decision. This is the same standard of accountability expected in any performance management system. No lesser standard is warranted here.
Tiered Governance — Not a Blanket Block
Not every AI application carries the same risk, and governance policy should reflect that. A practical two-tier model enables adoption where it is appropriate while protecting what matters most. Administrative and operational uses — scheduling, documentation support, internal reporting — warrant a lower bar, with basic oversight and encouragement of adoption. Regulated, patient-facing, or sponsor-connected uses — those touching data integrity, patient workflows, or sponsor relationships — require a higher bar: validation, training, and documented review. Treating all AI use as equally risky creates friction without protection. Treating all of it as equally low-risk creates exposure without awareness.
Partnership Over Deployment
The sites that are successfully integrating AI are doing it with sponsors and vendors — not having tools handed to them. The difference is structural. It means sites participate in tool design and configuration, not just implementation. It means structured feedback channels exist so sites can surface issues and drive real-time improvement. And it means governance is built into the relationship itself — not appended as a clause in a contract that no one revisits.
Where It Actually Lands
AI transformation does not succeed or fail at the sponsor level. It does not succeed or fail at the vendor level. It succeeds or fails at the site, in the coordinator’s workflow, in the PI’s trust, and in the patient’s experience. Every governance decision made upstream lands somewhere concrete. That place is the site. For vendors and sponsors, the question to carry into every AI deployment, and every AI-assisted site evaluation, is a straightforward one: is the site a partner in this process, or a subject of it? That distinction is where governance becomes real, or stays theoretical. And for the industry to make meaningful progress on AI, it needs to become real at the site level.
