Artificial Intelligence is rapidly becoming part of the pharmaceutical transformation agenda. Companies are exploring generative AI, predictive analytics, machine learning, intelligent automation and increasingly AI agents across manufacturing, laboratories, quality, validation and supply chain.
The opportunities are substantial.
But perhaps the most important question is not:
Where can we use AI?
It is:
Where should we start?
For pharmaceutical organisations, the answer should probably not begin with the technology. It should begin with the process.
A useful principle is:
Process first. People second. Change third. Data fourth. Technology last.
That may sound counterintuitive in an AI discussion, but it is precisely because AI is so powerful that process discipline becomes more important.
If a deviation process takes 60 days because ownership is unclear, adding AI will not automatically solve the problem. If CAPA investigations routinely identify weak root causes, generating the investigation report faster will not improve quality. If validation contains unnecessary duplication, using AI to generate the documents simply produces unnecessary documentation more efficiently.
The starting point should therefore be to identify where work is genuinely broken.
Where are highly skilled Quality professionals spending time on repetitive administration? Where are investigations waiting unnecessarily? Where are people searching across multiple systems for information? Where are recurring deviations not being connected? Where does knowledge disappear inside documents rather than supporting future decisions?
Those are potential AI opportunities—but only after the process has been understood.
The next question should be whether AI is actually the appropriate solution. Some problems can simply be eliminated. Others can be solved through workflow redesign, clearer ownership, better master data or conventional automation. AI should be used where intelligence genuinely adds value: analysing large volumes of information, detecting patterns, summarising complex evidence, supporting decisions or identifying relationships that humans may struggle to see efficiently.
The regulatory direction supports this risk-based approach. FDA and EMA’s joint principles for good AI practice in drug development emphasise clear context of use, human-centric design, risk-based assessment, multidisciplinary expertise, data governance and lifecycle management. The message is important: AI should be applied deliberately and proportionately rather than simply because the capability exists.
For Process Excellence teams, this creates an interesting new role.
They can become the bridge between Quality, Operations, Digital and AI.
They understand the workflow.
They can identify waste.
They can determine where decisions occur.
They can establish where human judgement matters.
And then they can ask where AI genuinely improves the system.
Perhaps the starting point for AI in pharma should therefore be surprisingly traditional:
Go and find the broken processes.
Understand them.
Simplify them.
Develop the people.
Strengthen the data.
Then introduce AI where it genuinely makes the process better.
The organisations that do this are more likely to move beyond impressive AI demonstrations and create something much more valuable:
better pharmaceutical operations.