Pharma teams should prioritize AI use cases that address a defined business or patient need and offer a measurable improvement. A useful starting point is to assess whether the necessary data is available and whether someone can act on the results. Projects also need to fit the work employees already do, with appropriate controls for the information involved.

A live discussion at Future Pharma 2026, now DigiPharma, examined how identifying the underlying problem can change an AI investment decision. Sarah Daniel, Product Marketing Manager at Authenticx, encouraged teams to "start identifying the problems you need to solve and matching those with the right tools." A colleague illustrated this with a contact center example: an AI tool could analyze repeat patient calls to identify confusing instructions or incorrect transfers, helping staff address the reasons patients needed to call again.

A separate conference workshop explored how usage data can reveal where employees need help completing tasks in an application. When prioritizing AI use cases, pharma leaders should consider the training and on-screen guidance employees may need to use each tool effectively.

Commercial innovation, digital strategy, and analytics leaders should define the problem and its current cost before comparing potential solutions. For each proposed use case, document the required data, the person responsible for acting on the output, and a measure of success, such as fewer repeat calls. Test the selected use case with its intended users, then evaluate accuracy and adoption alongside the business result before expanding it.

Join us at DigiPharma to compare how pharma commercial leaders select AI investments that address business priorities and patient needs.