How AI Is Reshaping Medical Affairs — And What Most Companies Get Wrong

Every pharmaceutical company we speak to has AI on the agenda. The pressure from leadership is clear: adopt AI, move faster, do more with less. But when we look at how most Medical Affairs teams are actually approaching it, we see the same pattern repeating.

The tool-first trap

Most AI adoption programmes start with the technology. A platform gets selected, a vendor runs a training day, and teams are told to "start using it." Three months later, adoption is patchy at best. The people who were already curious are experimenting. Everyone else has gone back to their old workflows.

The problem isn't the tool. It's the approach.

Capability before technology

The companies seeing real results are the ones that start with capability, not technology. They ask different questions: What decisions do our MSLs make every day? Where are the bottlenecks in our medical communication workflow? What would it look like if our team could process scientific literature twice as fast?

When you start with the work, the technology choices become obvious. And more importantly, adoption becomes natural because people can see the direct connection between the tool and their daily reality.

What we've learned from the field

After running AI capability programmes across multiple markets, a few patterns have emerged:

**Role-specific training beats generic workshops.** An MSL needs different AI skills than someone in medical communications. One-size-fits-all training wastes everyone's time.

**Small wins compound.** Starting with a single, high-value use case — like literature screening or medical inquiry response — builds confidence. That confidence spreads.

**Leadership has to go first.** If the Medical Affairs director isn't using AI in their own work, nobody else will either. We've seen this dynamic play out in every market we've worked in.

The opportunity is now

Healthcare is one of the last industries to truly embrace AI at scale. That's not a criticism — the stakes are higher, the regulatory environment is more complex, and the consequences of getting it wrong are real. But the companies that build genuine AI capability now will have a significant advantage in the years ahead.

The question isn't whether your teams will use AI. It's whether they'll be good at it.