The EU AI Act is no longer a proposal. It is law, and it applies in stages. The European Commission sets out the application timeline, including key dates in 2025 and 2026, with some obligations running on longer transition periods into 2027.
The staged timeline is also laid out in the Commission’s AI Act Service Desk, which frames a progressive rollout with full roll out foreseen by 02 August 2027 for parts of the framework.
High risk obligations are often discussed in connection with 2026 because that is when much of the Act becomes broadly applicable. At the same time, reporting shows ongoing pressure and debate about timing and readiness, including discussion of delaying elements of the high risk framework.
For clinical research operations, this creates a clear direction and active scrutiny of how implementation will work in practice. Trial delivery already relies on AI enabled systems across monitoring, data review, coding, signal detection, and workflow management. Regulatory expectations are sharpening while operational reliance continues to expand.
The first operational impact for sponsors and vendors
In practice, procurement and vendor management are where teams notice the impact first. AI enabled functionality is increasingly embedded within broader platforms rather than delivered as standalone tools. Classification under the AI Act influences how those systems are described, governed, and monitored.
Oversight routines also change, because questions that were previously informal become structured. Teams need clear answers on intended use, what documentation exists, what logging is available, and what governance controls apply. These are no longer theoretical considerations. They influence internal approval processes and inspection readiness.
AI enabled tools influence attention and prioritization. A ranked site list, a risk indicator, or an automated signal does not formally “decide” an action, yet it influences what teams review and escalate. That influence places the tool within the operational control environment of the trial.
High risk classification and sponsor accountability
The AI Act distinguishes between categories of systems, including those treated as high risk. High risk classification carries defined expectations around risk management, data governance, technical documentation, logging, transparency, and human oversight.
Clinical research teams may not develop these systems, yet reliance on their outputs remains visible. Sponsor accountability does not disappear because a vendor provides the platform. Delegation does not remove responsibility for oversight. When system outputs influence monitoring focus, escalation decisions, or quality review, they form part of the sponsor’s control framework.
Governance expectations extend beyond contractual clauses. They cover how systems are adopted, how outputs are interpreted, and how disagreements are handled.
Vendor questions every research team should ask
AI enabled features are now common within the platforms used to deliver trials. Vendor discussions need to establish a clear intended use, clear limits, and clear evidence on how the tool behaves in practice. The same questions come up repeatedly during onboarding, validation, and change control, because they determine whether outputs can be relied on and explained later.
Intended use and boundaries
Intended use and scope need to be explicit. The tool should have a defined operational purpose within a specific trial context. It also needs a clear boundary around what it does not do. When that boundary is vague at adoption, reliance tends to expand over time without anyone noticing.
Data inputs and control
Data inputs need scrutiny. Teams should understand what data feeds the system, who controls the pipelines, and how inconsistencies are detected and handled, because these factors directly affect output reliability. A dashboard view is not the same thing as understanding the underlying data flow.
Change management and impact on outputs
Change management needs the same level of attention. AI enabled platforms evolve through model updates, configuration adjustments, and feature activation. Teams need visibility into what counts as a meaningful change, when changes are made, and how changes are communicated. When outputs change, the oversight conditions change too, even when the workflow looks the same on the surface.
Traceability and inspection readiness
Traceability underpins inspection readiness. Version history, configuration state, logging, and documentation of human overrides allow reconstruction of decision pathways at a point in time. Outputs without retrievable context weaken defensibility under inspection.
Ownership and escalation need to be explicit
AI enabled features can become part of trial delivery without anyone clearly owning them. In complex vendor ecosystems, functionality is often embedded within wider platforms, activated during upgrades, or introduced as part of a broader rollout. Automated prioritization becomes routine. Workflow queues reflect algorithmic ordering. The system is used daily, yet no individual or function formally governs its influence, or can explain, in plain terms, how it is meant to be used.
Diffuse ownership creates vulnerability. When outputs conflict with site reality, monitoring judgement, or source data findings, resolution can stall. People debate the output, not the process. Without named accountability and documented escalation routes, oversight becomes reactive, and the same argument repeats across studies and over time.
This is avoidable. Control does not require teams to become technical experts, it requires decisions to be attributable.
- Key controls that prevent “we used it, but nobody owned it” situations
- Named ownership for the feature, not only the vendor relationship
- A defined use case that explains where the output informs decisions, and where it does not
- Documented escalation routes for conflicts between tool outputs and human review
- A routine review step that checks whether outputs still make sense as the platform changes
- A record of overrides and the rationale, when teams disagree with the output
Clear allocation of responsibility, defined use cases, and recorded decision routines prevent erosion of oversight over time. Oversight must remain attributable.
Staying inspection ready as requirements expand
Preparation begins with structured awareness as requirements expand. Teams need a working understanding of how AI enabled tools function within their processes, including limitations and dependencies. Technical depth is not required, yet informed oversight is.
That awareness supports better control at adoption. Document why the tool was selected, how it is used, what controls apply, and who reviews performance. Review routines should then look beyond usage metrics and check whether outputs remain aligned with source findings and operational reality.
As staged obligations continue through 2026 and beyond, regulatory awareness cannot be confined to periodic updates. Guidance evolves, vendor positioning shifts, and enforcement practice develops. Keeping knowledge current supports steady adaptation without disruption.
GCP Central’s thoughts.
The AI Act raises expectations for governance of AI enabled systems. Clinical research teams already operate in an environment where oversight must be visible and decisions must be traceable. Taken together, that puts pressure on the basics: clear intended use, named ownership, disciplined documentation, and routine review of tool behavior as platforms change.
Most teams do not struggle with the concept. The strain shows up in execution, during onboarding, upgrades, validation work, and the ordinary judgement calls where AI supported outputs influence what gets reviewed and escalated. Teams need a shared way of working so those outputs are treated as inputs to sponsor judgement, with a documented record of how they were interpreted and what action followed.
This is why GCP Central is talking about the AI Act. The regulation applies in stages while AI enabled functionality continues to spread across trial delivery. Our focus is continuous learning that stays current as the rules, guidance, and vendor platforms change. It helps teams keep intended use, ownership, documentation standards, and day to day handling consistent across studies and over time.
Practical vendor discussions remain a reliable test of readiness. Clear answers on traceability, change control, and human oversight matter, and supporting documentation distinguishes reassurance from evidence. In 2026 and beyond, inspection readiness will depend on whether AI enabled tools are used in a controlled and attributable way, with decision pathways that can be clearly explained.
Looking for more on AI in clinical trials? Our previous article, “Rethinking Sponsor Oversight For AI-Enabled Trial Deliver Under ICH e6 (R3)” can be found here.
