Imagine a quality reviewer preparing for the morning production meeting. A deviation needs attention. The relevant procedure is available, the equipment history exists, and a similar incident was investigated last year.

Yet the reviewer is still moving between folders, reports and messages to piece the story together.

The difficulty is not a lack of information. It is the effort needed to turn that information into a decision people can trust.

This illustrative scenario captures a useful starting point for artificial intelligence in pharmaceutical operations: helping people find, organise and examine evidence, while making responsibility for the decision explicit.

For a team considering its first AI project, that is a more useful ambition than trying to transform an entire facility at once.

Start with the task that slows good people down

“We need an AI strategy” can become a very expensive conversation if nobody first identifies the problem.

Start with something observable. Reviewers spend too long finding relevant historical records. Engineers struggle to distinguish useful equipment alerts from background noise. Training coordinators can see completion rates but need help finding recurring knowledge gaps.

Each problem suggests a different solution. Some need better search, clearer procedures or straightforward automation. Others may benefit from AI.

Generative AI can help draft and summarise language. Predictive models can estimate future outcomes from data. Anomaly detection can flag unusual patterns. These capabilities have different limitations and should be evaluated against the actual job.

Before selecting a tool, finish this sentence:

We want to help this team perform this task better, using these records, with this person responsible for checking the result.

If that sentence is difficult to complete, the project needs more definition.

Four practical places to explore

The following are proposed starting points, not claims that every pharmaceutical organisation should deploy them. Their suitability depends on the intended use, available data and consequences of error.

1. Finding the right controlled information

A retrieval assistant could help employees locate relevant passages in approved procedures and technical documents. The useful output is an answer accompanied by the source, document version and relevant section.

The boundary matters. An assistant must not quietly combine an obsolete procedure with a current one or invent an instruction when the source is unclear. Access permissions must carry through to search results.

Start with a narrow document collection. Test whether users reach the correct source faster, including when their question is ambiguous. A reliable “I cannot find enough information” is better than an unsupported answer.

2. Preparing information for quality review

An assistant could assemble a draft chronology, retrieve potentially related deviations or organise material for a report. That can give the reviewer a better starting point.

Similarity is not proof of a shared root cause. A previous investigation may involve different equipment, materials or operating conditions. Investigators still need to examine the evidence and explain their conclusions.

Measure the time needed to reach an accepted, checked output. A draft produced in seconds is not a saving if it creates an hour of corrections.

3. Supporting equipment maintenance

Predictive models may help engineers identify changes in equipment behaviour using signals such as vibration, temperature or operating history.

An alert should begin a technical assessment. Engineers need to distinguish genuine deterioration from sensor faults, maintenance changes or unfamiliar operating conditions. Actions affecting production should follow the site's authorised procedures.

Useful measures include warning lead time, false alarms and missed events. A model that generates more notifications without improving decisions may simply add work.

4. Making training more focused

AI could help analyse recurring assessment mistakes or prepare draft learning material from approved content. This may help trainers identify which concepts need more explanation.

But a completed module, a high quiz score and demonstrated competence are different things. Trainers and supervisors need appropriate evidence that someone can perform the task correctly.

For an initial pilot, evaluate the quality of the learning material and the usefulness of the identified gaps. Keep employee qualification decisions within the established training process, with appropriate access controls for individual performance data.

A real industry example: quality report preparation

In June 2025, Sanofi described using generative AI for report creation, including data collection and formatting for approximately 3,500 annual Product Quality Reports. The company stated a target of reducing creation time by 70%. That figure was a target in its account, not evidence that every deployment had achieved that reduction. Read Sanofi's account.

The practical lesson is the specificity of the task. Report preparation creates an identifiable workload that can be measured before and after a change. It also makes it possible to distinguish preparation time from the effort required to review and approve the finished work.

For another organisation, the useful question is whether a similarly well-defined task exists in its own workflow. Sanofi's target should not become someone else's promised return.

Where human judgment matters most

Return to the reviewer at the morning meeting. Suppose an AI assistant presents a polished summary and highlights an apparently similar incident.

The summary may help. It cannot, by its presentation alone, establish that the records are complete or that the comparison is valid.

Someone must notice the missing context, challenge the suggested explanation and decide what further evidence is needed. That work requires both technical knowledge and the authority to act.

“The first principle is that you must not fool yourself—and you are the easiest person to fool.”

— Richard Feynman, 1974 Caltech commencement address. Source: Caltech.

Feynman was discussing scientific integrity, not AI. His observation is useful here because a convincing result can make us less inclined to question the assumptions behind it.

Human oversight therefore needs to be more than an approval button. Reviewers need access to the underlying evidence, enough time to assess it, and a clear route to reject or escalate an output.

For a first assistive deployment, retain existing authorised responsibilities for decisions such as batch disposition, investigation conclusions, corrective and preventive action approval, and employee qualification. Any proposed change to those responsibilities needs its own assessment under the applicable quality system and regulatory framework.

Give every responsibility an owner

An AI pilot crosses departmental boundaries. Assign named owners before it reaches everyday work.

One person may hold several roles in a smaller organisation. Responsibilities still need to be explicit, including who can suspend use when something goes wrong.

What responsible adoption looks like

In January 2026, the FDA and EMA published joint principles for good AI practice in drug development, including manufacturing within their stated scope. They emphasise a defined context of use, proportionate validation and oversight, data governance, multidisciplinary expertise and life cycle management. They also identify adherence to relevant standards, including GxP. These are guiding principles, not a blanket approval of a particular tool or application. Read the FDA–EMA principles.

For a proposed pilot, translate those considerations into concrete questions:

• What may the system do, and which actions are outside its scope?

• Which records may it access, and how are source versions controlled?

• What evidence will demonstrate acceptable performance for this task?

• What happens when information is missing, conflicting or unfamiliar?

• Who reviews changes to the model, prompts, source documents or integrations?

• How will users continue working if the system is unavailable or withdrawn?

The answers should shape the deployment before it becomes part of routine work.

The cost is bigger than the subscription

A useful business case includes data preparation, integration, evaluation, applicable validation work, security, user training and ongoing support. Human review is also a cost. So are monitoring and reassessment after changes.

Use a simple calculation to make the assumptions visible:

Monthly capacity value = tasks completed × net minutes saved per task ÷ 60 × fully loaded hourly labour cost.

For illustration only, suppose a team completes 200 tasks each month and saves 15 minutes per task after review and correction. That releases 50 hours. At an assumed labour cost of ₹1,000 per hour, the capacity value is ₹50,000 per month.

These are hypothetical inputs, not market prices or an ROI forecast. Released time becomes a cash saving only when expenditure actually falls. Otherwise, its value depends on whether the team can use that capacity productively.

Compare the benefit with recurring costs and the one-time implementation investment. Keep any claimed reduction in errors separate until there is evidence to support it.

A manageable route from idea to pilot

Use the following sequence as a planning framework. The time required depends on the application and its risk.

1. Define one task. Choose a contained workflow, name its owner and measure the current effort and error patterns.

2. Check readiness. Review source quality, permissions, integration needs and the consequences of incorrect output.

3. Agree on acceptance criteria. Include quality, review effort and failure handling alongside speed. Set criteria before seeing the results.

4. Test realistic cases. Use representative records, difficult cases and missing information. Keep evaluation examples separate from those used to tune the system where applicable.

5. Run alongside the existing process. Where appropriate, compare results without letting pilot outputs control live decisions. Record corrections, missed issues and user feedback.

6. Decide from the evidence. Expand only when the agreed criteria are met and the necessary approvals are complete. Otherwise, revise the approach or stop.

A smaller pilot that reveals a limitation is useful. It can prevent the organisation from scaling a problem it does not yet understand.

Make the next decision easier to trust

The reviewer in our opening scenario does not need a more impressive dashboard. They need the right records, a clear view of what is missing and enough time to think.

That is a worthwhile test for AI in pharmaceutical operations. Does it help people work with better evidence? Does it reduce effort after checking and correction? Can the team explain where it is useful and where it should not be relied upon?

Start with one task where those questions can be answered. Build confidence through measured results, and keep responsibility visible as the technology develops.

Which part of your team's work takes more time to prepare than to decide? That may be the best place to begin the conversation.