AI Opportunity Assessment: Where Your Medical or Commercial Team Should Start

The question more medical and commercial teams are asking today isn't whether they should use artificial intelligence, but where to start without wasting time and budget on tools that don't solve a real problem. A well-done assessment, before investing, avoids that mistake.

A recent survey of executives across the payer, provider, and pharma sectors identified four main barriers to scaling generative AI beyond the pilot stage: information security concerns (52% in pharma), lack of internal AI expertise (52% in pharma), costly integrations, and difficulty preparing AI-ready data — the latter particularly pronounced in pharma (47%, versus 41% in providers and 39% in payers) ^[1]. The most useful data point in the study, however, is a different one: budget was not identified as the main barrier to scaling projects from pilot to production ^[1].

A review on AI adoption barriers from the perspective of healthcare professionals identifies human factors as being just as determinant as technical ones: concerns about professional autonomy, limited clinical trust, insufficient AI literacy, and fear of "de-skilling" ^[2]. The availability of trained professionals — both clinicians and managers — proved to be as critical a factor as the technology itself for effective implementation ^[2].

This suggests that a well-done AI opportunity assessment shouldn't start with the technology, but with mapping the team's real bottlenecks: where is the most time lost on low-scientific-value repetitive tasks? Where is lack of internal capacity — not lack of tools — the actual limit? Which process, if poorly automated, would create a bigger regulatory risk than the time it would save?

Answering those questions rigorously — before choosing a tool — is what separates an AI investment that actually solves a problem from one that simply adds a technological layer to a process that was still poorly designed.