[insight]
AI Readiness Checklist For Pharma Manufacturing Operations Teams
How to use this checklist
Work through each section with your operations and data leadership. Mark every item you can confidently answer yes to today — not what you plan to have in place. Your score will tell you where you are, and what to prioritize before committing to an AI pilot.
Scoring: Count checked items across all 5 sections. Total possible: 25.
01 Data Layer
The foundation. If the data isn't ready, neither is the AI.
[ ] We have a complete inventory of every data source relevant to our target AI use case. Includes ERP, SCADA, MES, LIMS, manual records, sensor feeds, and third-party platforms.
[ ] We know the data format, update frequency, and completeness for each source. Gaps, null rates, and inconsistencies have been documented — not assumed away.
[ ] The same variables are defined consistently across systems. No ambiguity about what "batch yield" or "scheduled runtime" means in each system.
[ ] We can build a production data pipeline — not just pull a historical extract.
The data path that feeds the pilot is the same path that will feed the live model.
[ ] Data access and ownership is clear across IT, operations, and quality.
No single team can block a pipeline decision unilaterally.
02 Governance & Regulatory Readiness
Required for any AI touching a GMP process. Not optional.
[ ] We have a validation strategy for AI tools used in or adjacent to GMP processes. Aligned to FDA 21 CFR Part 11, computer software assurance (CSA) guidance, and the 2024 FDA/EMA joint AI principles.
[ ] We have defined change control procedures for model updates and retraining. Includes who approves changes, what triggers retraining, and how version history is documented.
[ ] A named process owner exists for each AI output before the pilot launches. Not the data science team. An operations or quality owner who will manage the model in production.
[ ] We have a documented plan for what happens when the model drifts or produces anomalous output. Includes human override protocol and escalation path.
[ ] Legal and quality leadership have been briefed on the AI initiative. No regulatory surprises after deployment. Alignment reached before the first sprint.
03 Strategy & Use Case Selection
A good use case is defined before the pilot. Not discovered during it.
[ ] We have selected one primary use case based on ROI potential and data readiness — not enthusiasm. Not five use cases. One. The highest-value, highest-readiness option.
[ ] Success is defined in operational terms before the pilot starts. Example: "Reduce scheduling time by 25%, measured in hours per week, against a documented current-state baseline."
[ ] We have a clear ROI framework connecting model performance to operational dollars. Accuracy metrics are not business outcomes. The link between the two is explicit.
[ ] The pilot timeline is aligned to a board review or business decision checkpoint. Not open-ended. A hard delivery date tied to a moment when the result will matter.
[ ] We have evaluated build-vs-buy and selected an implementation partner based on production delivery track record. Not based on demo quality or marketing materials.
04 Adoption & Workforce Readiness
The people running the plant determine whether the AI actually gets used.
[ ] Plant floor leadership — shift supervisors, process engineers, quality techs — have been involved in use case design. Not informed. Involved. Their input shaped the interface, thresholds, and workflow integration.
[ ] We have a change management plan that begins at sprint one, not at go-live. Communication, training, and feedback loops are designed as part of the pilot, not added afterward.
[ ] The AI output is interpretable to the people who will act on it. Operators can explain why the model recommended what it did. Black boxes do not get used.
[ ] We have a feedback mechanism for plant floor teams to flag model errors or edge cases. Production AI improves when the people closest to the work can surface issues quickly.
[ ] Executive sponsorship is active — not just present on the project charter. A senior operations leader is visibly committed to the initiative and using the outputs.
05 Scale Readiness
Before expanding, the foundation has to hold.
[ ] The first production model has been running for at least 30 days with no critical failures. Not in testing. In production. With real data, real operators, and real consequences.
[ ] The data pipeline built for use case one can support additional use cases without redesign. The infrastructure is not single-purpose. It was built to grow.
[ ] We have a model monitoring process in place — including drift detection and retraining triggers. The operations team runs this. It is not dependent on the implementation partner.
[ ] The governance framework scales to additional use cases without requiring a new compliance process for each. Templates, documentation standards, and ownership models are repeatable.
[ ] We have a 90-day roadmap for the next 2–3 use cases, prioritized by the same criteria as the first. Data readiness, ROI potential, operational constraint severity. Not what is exciting.
Your Score: ____ / 25
What the score means
0–8 — Start with a data and use case assessment before committing to a pilot. You're likely missing the foundation.
9–15 — Partial readiness. The gaps in your score are where pilots stall. Address them before launch.
16–21 — You're ready to move. One or two use cases are likely viable for a production build now.
22–25 — You have the foundation. The question is which use case to scale next.
Talk to our team about your score
Whether you're at 4 or 22, we can tell you exactly what the next step looks like. Our 4–6 week assessment closes the gaps that this checklist surfaces — with a prioritized use case roadmap, a data readiness evaluation, and an ROI framework you can take to your board.
