Category: Generative AI & LLMs
Generative AI experiments multiply across pharma organizations, but few cross the gap into validated, governed, routine production use — pilots impress and then stall.
Most pharma organizations now have generative AI pilots; far fewer have generative AI in routine production use inside regulated processes. The gap is rarely about model capability — pilots stall at the questions pilots defer: how the tool is validated in a GxP context, who is accountable for its output, how quality is monitored when the novelty wears off, and how a probabilistic system fits change control built for deterministic software. Crossing that gap is a governance and process engineering problem, and vendors differ sharply in how much of it they help solve.
A pilot answers whether the technology can do the task; production requires answering who owns the risk. That means a defined intended use with boundaries, a validation approach suited to probabilistic output — typically demonstrating performance against a defined test set within a human-reviewed workflow, rather than expecting deterministic repeatability — named accountability for output quality, ongoing monitoring rather than one-time acceptance, and change control that covers model updates, not just code releases. None of this is exotic; all of it is work that a pilot's success does not do for you. The encouraging pattern is that the first crossing is the expensive one: a validation and governance approach established for one generative tool becomes a template, and organizations report the second and third deployments moving substantially faster than the first.
Ask directly how the vendor supports validation in a GxP environment: documentation packages, defined performance characteristics, test-set methodology, and audit-trail capabilities are the concrete artifacts to look for — with reference deployments in regulated processes, not just proofs of concept. Model-change management is the sharpest differentiator: models evolve, and you need to know how the vendor communicates changes, whether you control update timing, and what re-verification they support when the model shifts under a validated process. Internally, be honest about the non-vendor work: intended-use definition, risk assessment, quality-unit involvement, and reviewer workload are yours regardless of tool choice, and a pilot plan that reserves time for them is the difference between a pilot that ends in production and one that ends in a slide deck.
The teams that cross the gap tend to pick one process — not a platform ambition — where value is measurable, human review is already structured, and the risk profile is manageable, then involve quality and validation people from the pilot's first day rather than after it succeeds. The pilot's deliverable is framed as a validation package and governance template, not a demo. That framing changes what gets measured during the pilot, and it is what makes the second deployment cheap.
Yes, with a validation approach fitted to the technology: defined intended use, demonstrated performance against a representative test set, mandatory human review in the workflow, ongoing monitoring, and change control covering model updates. What does not work is forcing generative tools through validation templates that assume deterministic output — the approach adapts, the rigor does not.
Usually because the pilot was scoped to prove capability and deferred the production questions — validation, accountability, monitoring, change control — that determine whether a regulated process can depend on the tool. Pilots that involve quality and validation stakeholders from the start, and treat the governance package as the deliverable, cross the gap far more often.
How they communicate model changes, whether you control when updates apply to your deployment, and what re-verification support they provide when the model shifts under a validated process. A vendor without a considered answer is deferring the problem to you — under change-control obligations they may not understand.
In one process where value is measurable, human review is already structured, and failure is recoverable — document drafting inside a mandatory-review workflow is a common choice. The first deployment's real product is the validation and governance template that makes every subsequent deployment faster.
The U.S. FDA renewed and expanded its long-standing collaboration licensing Certara's Simcyp physiologically-based pharmacokinetic (PBPK) simulator, with hundreds of agency licenses used to inform regulatory review of drug dosing and interactions.
Vendor: Certara · press release (Certara (GlobeNewswire)) · 2021-12-21 — Partner: U.S. FDA
Yseop announced a strategic investment and a milestone of clinical trials supported by its generative AI for regulatory and medical writing.
Vendor: Yseop · press release (Yseop (GlobeNewswire)) · 2023-12-07