Induced from William (Bill) Gargano of RCM Data & Solutions at the AI in GxP conference (2026-09-15-0822-bill-morgano-ai-continuous-validation-life-sciences), 2026-09-15, including his quote of Brian Dennis, founder of Square: “Validation isn’t an event. Validation is data.”
The one-time validation event is the anachronism
Buying software and budgeting 20 to 30 percent of the plan for one-time validation documentation invites management to cut it under schedule pressure. Manufacturing abandoned batch-thinking for continuous manufacturing twenty years ago; validation is next. CSA is a 40-year-old predicate rule, and implementing it on paper without changing the underlying process is a waste of time. For a drifting model the same claim becomes AI validation evidence has a shelf life, and the measurement form of the live record is Validating an AI system in a regulated industry means validating a measurement system.
Nightly releases with provable coverage passed three FDA inspections
A financial-services vendor entering life sciences integrated Caterpillar and Cucumber into its toolchain for self-testing and regression testing, moved to nightly GMP releases, and passed three FDA inspections by proving 99.9 percent test coverage of everything the software does. Layering AI analysis on top demonstrates likely failure points and probable customer misconfiguration. No mainstream enterprise vendor can prove coverage at that level; a three-day document review of a three-million-line codebase proves nothing. Observability and evaluation answer different questions about an AI system is what that nightly proof splits into once the system is an LLM.
The economics redeploy people into Quality
Automating CSV, equipment qualification, and process validation together cuts costs by roughly 65 percent within eighteen months and more than halves throughput time, with zero headcount cuts: the bright people doing validation paperwork redeploy into Quality, because that is where the FDA’s attention already sits. The number one inspection focus is data integrity, specifically trend analysis across deviations and failures; the second is being able to answer what the data is telling you about the organization. Those are Quality questions, not documentation questions. They are also why Human error, not AI failure, drives GxP warning letters stays a human-systems problem even after the paperwork is automated, and why Regulators audit against principles, so size quality rigor to risk measures the live record rather than the thickness of the binder. In the GxP control loop, AI acts, humans decide, and the system remembers is that live record with a named human still on the decision.
Related
- Home
- Validating an AI system in a regulated industry means validating a measurement system
- Human error, not AI failure, drives GxP warning letters
- AI validation evidence has a shelf life
- Observability and evaluation answer different questions about an AI system
- Regulators audit against principles, so size quality rigor to risk
- In the GxP control loop, AI acts, humans decide, and the system remembers