Put AI to work with a clear purpose and evidence you can assess.

Establish where AI can support the business and what must be understood before relying on it.

• Use-case inventory, intended use, system boundaries, and GxP impact.
• Supplier evidence, data access, privacy, and deployment assumptions.
• Accountable owners, decision rights, escalation, and review responsibilities.

Typical outputs: prioritized use-case register, gap assessment, risk rationale, governance responsibilities, and adoption roadmap.

Define a proportionate evidence strategy for the intended use and the consequences of error.

• Requirements, proposed acceptance criteria, and representative evaluation tasks.
• Normal, boundary, and failure cases, including missing evidence and unsupported answers.
• Separate assessment of deterministic software, AI behavior, retrieval, and the complete human workflow.

Typical outputs: assurance plan, evaluation design, traceability approach, evidence-gap review, and recommendations for client authorization.

Keep the basis for an AI-supported decision understandable as the workflow evolves.

• Source provenance, record retention, access, review, and audit-trail requirements.
• Traceable model, prompt, configuration, and source-document versions.
• Performance monitoring, incident handling, change assessment, and re-evaluation triggers.

Typical outputs: control requirements, lifecycle responsibilities, monitoring recommendations, and change-impact assessment framework.

Inputs: authorized, version-controlled procedures and technical records.

AI role: locate relevant passages and prepare a cited draft for a qualified reviewer.

Challenge cases: superseded procedures, absent answers, conflicting records, and misleading instructions within source material.

Controls & decision: source/version traceability, access control, grounded-answer evaluation, and explicit abstention. The designated document owner checks evidence and approves the final content.

Inputs: reconciled process data, approved limits, and process context.

AI role: assist exploration, summarize patterns, and draft questions for investigation.

Challenge cases: missing values, sensor drift, changed process conditions, false alerts, and missed signals.

Controls & decision: qualified data, justified statistical methods, benchmark comparisons, and review of model changes. Process specialists and Quality determine the investigation and any action under client procedures.

Inputs: reviewed monitoring records, locations, methods, and relevant operational events.

AI role: organize evidence and suggest investigation questions for specialist review.

Challenge cases: sparse data, sampling changes, misidentified organisms, and unsupported causal conclusions.

Controls & decision: preserve original records and scientific context; evaluate omissions and false associations. Microbiology specialists and Quality assess significance and approve conclusions. Batch disposition remains an authorized human decision.