Why “safe adoption” needs a framework
AI is already improving how teams search, summarize, classify, and predict—but regulated environments add a simple constraint: if an AI output influences quality decisions, you need a credible, controlled approach.
Recent regulatory thinking reinforces this direction. FDA’s 2025 draft guidance on AI used to support regulatory decision-making emphasizes a risk-based credibility assessment, including clear context-of-use, representative and reliable data, and documentation proportional to risk. (U.S. Food and Drug Administration) EMA’s reflection paper similarly frames AI/ML as part of the medicinal product lifecycle and highlights managing new risks (e.g., bias, transparency) when AI supports development and manufacturing. (European Medicines Agency (EMA))
In practice, “safe adoption now” usually means starting with bounded use cases that:
- reduce repetitive work,
- keep humans accountable for decisions,
- preserve data integrity and auditability,
- and can be governed inside your existing QMS.
The checklist below is a practical way to do that.
Step 1: List three repetitive tasks and score their impact
Start with friction you can already feel. Don’t start with the most exciting model or vendor.
Good candidates in chemistry & API manufacturing (examples):
- Deviation/investigation intake: extracting key facts, timelines, and impacted lots from structured fields and attachments (AI drafts; humans finalize).
- CoA and raw material document checks: comparing supplier CoAs to internal specs and flagging discrepancies (AI flags; QA disposition remains human).
- Batch record review support: highlighting missing entries, inconsistent units, or out-of-range process parameters (AI flags; disposition remains human).
A simple scoring model (keeps selection objective)
Score each task 1–5 on:
- Frequency (how often it occurs)
- Cycle-time drag (how much it delays throughput)
- Error pain (rework / deviations / review loops)
- Data readiness (are inputs digital, accessible, consistent?)
- Compliance sensitivity (does it directly affect GMP disposition?)
Then compute:
Impact Score = (Frequency + Cycle-time + Error pain) – Compliance sensitivity + Data readiness
This intentionally rewards “high pain + ready data” and penalizes “direct disposition risk” for first-wave adoption.
Step 2: Choose one use case and define success, data, and timelines
Pick one use case and define it precisely. Your goal is not “use AI.” Your goal is “reduce review time for X by Y% while maintaining quality.”
Define the “context of use” (COU)
Write one sentence:
“AI will be used to [do what] on [which inputs] by [which users] to support [which workflow], and will not be used to [explicit exclusions].”
This aligns well with FDA’s focus on clearly defining the question of interest and intended context of use when assessing AI credibility. (U.S. Food and Drug Administration)
Define success metrics (keep them measurable)
Examples:
- Reduce deviation triage time from 45 minutes to 25 minutes (median).
- Increase right-first-time investigation narratives from 60% to 80% (as measured by QA rework).
- Reduce CoA review turnaround time by 30% without increasing discrepancies missed (measured via sampling audits).
Define the minimum data you need (and the data you will not use)
Document:
- data sources (e.g., approved templates, LIMS exports, controlled PDF attachments),
- data classification (confidential, supplier proprietary, personal data),
- retention rules and access controls,
- what gets redacted.
If the use case touches GMP records, anchor your controls in established expectations for data integrity (accurate, complete, traceable) and governance. FDA’s CGMP data integrity guidance focuses explicitly on the role of data integrity in drug CGMP. (U.S. Food and Drug Administration)
Set a realistic timeline (pilot first)
A practical pilot cadence:
- Week 1: define COU + risk screen + data access
- Weeks 2–3: prototype + run side-by-side with humans
- Weeks 4–5: measure outcomes, refine prompts/rules, finalize logs
- Week 6: implement controlled rollout + training + post-launch monitoring plan
Step 3: Create a simple approval log for AI‑assisted decisions
This is the difference between “we tried a tool” and “we operated a controlled process.”
If AI touches anything that could be a GMP record, align your approach to computerized systems expectations (risk management, validation/qualification as appropriate, and no decrease in product quality or QA control). EU GMP Annex 11 explicitly applies to computerized systems used in GMP-regulated activities and states the application should be validated and that replacement of manual operations should not increase overall process risk. (Public Health)
If AI-generated outputs are stored or relied on as electronic records, ensure your approach aligns with electronic record expectations. 21 CFR Part 11 sets criteria for when FDA considers electronic records trustworthy and reliable. (ecfr.gov)
Minimum viable approval log (what to capture)
For each “AI-assisted” item:
- Date/time, user, workflow step
- Input set (document IDs, batch IDs, deviation ID)
- AI output (versioned)
- Human reviewer name + decision
- Rationale (especially when disagreeing with AI)
- Any corrective actions (prompt change, rule update, escalation)
Key principle: If it isn’t logged, it didn’t happen.
Step 4: Record decisions, owners, and due dates
AI initiatives fail less from model quality and more from governance gaps: unclear ownership, moving goalposts, and “we’ll document later.”
Maintain a single decision log covering:
- chosen COU and exclusions,
- success metrics and baseline,
- approved datasets and redaction rules,
- validation/verification approach (fit-for-use),
- go/no-go criteria,
- change control triggers.
Use a quality risk management mindset: explicit risks, mitigations, and proportional controls. ICH Q9(R1) is the current ICH framework for quality risk management, emphasizing more informed and timely decisions and reducing subjectivity in risk outputs. (ICH Database)
Step 5: File supporting documents with the final record
Treat your AI pilot like any other operational change: if it’s worth doing, it’s worth being able to reconstruct.
A strong “final record” typically includes:
- Use-case charter (COU, success metrics, exclusions)
- Risk screen / QRM worksheet
- Data inventory + access approvals + redaction rules
- Vendor assessment (if applicable) and security posture summary
- Test evidence (side-by-side results, sampling plan, exceptions)
- Approval log exports
- Training record / user guidance
- Change control record (if your QMS requires it)
- Post-implementation review and CAPAs (if any)
If you operate AI in GxP areas, industry guidance increasingly exists specifically to bridge AI with established GAMP concepts. ISPE’s GAMP guide for AI is positioned as a holistic interpretation for developing and using AI-enabled computerized systems in GxP while safeguarding patient safety, product quality, and data integrity. (ISPE)
Step 6: Schedule a follow‑up review to capture lessons learned
AI outputs can drift, user behavior changes, and “small prompt tweaks” can create untracked change.
A follow-up review should explicitly cover:
- Did we hit success metrics (time, quality, rework)?
- What failure modes occurred (hallucinations, missed exceptions, bias, inconsistent outputs)?
- Did users over-rely on outputs?
- What needs to be standardized (templates, prompts, redaction rules)?
- What triggers re-validation or rollback?
This aligns with concerns highlighted in FDA’s AI draft guidance, including the possibility that model performance can change over time or across deployment environments and the need for transparency and documentation proportionate to risk. (U.S. Food and Drug Administration)
Checklist
- List three repetitive tasks that slow your team and score their impact.
- Choose one use case and define success, data, and timelines.
- Create a simple approval log for AI‑assisted decisions.
- Record decisions, owners, and due dates.
- File supporting documents with the final record.
- Schedule a follow‑up review to capture lessons learned.
Notes:
This checklist is for educational use only and does not replace your internal procedures.