How to Use AI in Medical Affairs: What to Resolve Before You Scale
Most Medical Affairs teams now have access to an approved AI tool. Ask how it has changed the way they synthesise insights, cover a congress or prepare for an expert meeting, and the answer is usually: not much yet.
The tools are not the problem. The problem is that AI was rolled out as a licence and a training session, and Medical Affairs work did not change around it. Before you scale, there are five decisions to make. Teams that make them early move faster and take fewer risks.
The gap is not access. It is design.
The first instinct is to buy better tools or run more training. Both help. Neither fixes the core issue.
AI creates value in Medical Affairs when it is built into specific workflows, with clear rules on data and verification, and someone accountable for the result. Without that, you get pockets of enthusiastic users, a quiet majority who tried it once, and no way to show leadership what changed.
Decision 1: What data can AI touch?
This is where most teams stall, and where uncertainty does the most damage. If people do not know what is allowed, they either avoid AI or use it in ways nobody has approved.
Write down, in plain language, which information can go into which approved tools:
- Public literature and congress materials
- Internal documents that are not confidential
- Confidential and unpublished data, only in a secure, approved environment
- CRM data, insights and anything containing personal data, under specific governance
One page, agreed with Compliance, Legal and IT, removes more friction than any training course.
Decision 2: How will outputs be verified?
Generative AI produces fluent text that can be wrong. In Medical Affairs, a wrong number or a fabricated citation is not an inconvenience. It is a compliance issue and a credibility issue.
Set a verification standard by output type. Internal summaries might need a check of key facts against sources. Anything scientific or external needs every claim and citation verified, and a documented route through medical review or MLR. Tools grounded in approved sources, which must cite what they use, make this far easier.
Decision 3: Which workflows first?
Avoid starting with "everyone use AI for everything". Pick three to five high-volume, lower-risk workflows where the time saving is easy to see. In Medical Affairs, these usually deliver first:
- Literature monitoring and summaries. Weekly digests of new publications in your therapy area.
- Congress coverage. Turning session notes and posters into structured debriefs for internal teams.
- Insights synthesis. Clustering field insights into themes with supporting evidence, so leaders can act on them.
- Pre-call preparation. Summarising an expert's recent publications and previous interactions.
- Medical information drafts. First drafts of standard responses, always reviewed before use.
Notice what is not on the list: fully automated content for HCPs. That comes later, if at all, once governance and verification are mature.
Decision 4: Who owns it?
When AI belongs to everyone, it belongs to no one. IT owns the platform. Compliance owns the policy. But someone in Medical Affairs needs to own the use cases, the skills and the results.
That can be a named AI champion in a small team, or a dedicated Medical Excellence role in a larger one. What matters is a person with a mandate, a short roadmap and two or three measurable goals for the year.
Decision 5: How will you show value?
Leadership will ask. Decide the measures before you start, not after:
- Time saved on specific tasks, measured before and after for a few weeks.
- Quality, such as fewer review rounds or more complete congress debriefs.
- Decisions influenced, such as insight themes that reached a brand or evidence plan.
Anecdotes will not defend budget. Your own numbers will.
What is changing in 2027
Two shifts make these decisions more urgent.
First, AI is moving inside the systems Medical Affairs already uses: CRM, insights platforms, literature tools and content review systems. The question is no longer whether to use AI, but whether your processes and data are ready when it arrives in your existing tools.
Second, HCPs increasingly ask AI tools their clinical questions before they talk to an MSL or contact Medical Information. If those tools give incomplete or outdated answers about your therapy area, that shapes the conversation before it starts. Medical Affairs teams are beginning to monitor what AI tools say, and to think about how accurate, balanced scientific information reaches these channels.
Regulation is also tightening. The EU AI Act and evolving company policies mean governance is not optional. Teams that built clear rules early will adapt faster.
Where to start this quarter
If you lead a team, do three things in the next 90 days:
- Agree the one-page data and verification standard with Compliance.
- Pick three workflows and run them with AI for 30 days, measuring time and quality.
- Name an owner and present the results at your next leadership meeting.
If you are an individual contributor, pick one task you repeat every week and run it through your approved tool every time for a month. Proficiency comes from repetition on real work.
Not sure where your team stands today? The free Medical Affairs AI Readiness Check scores adoption, workflows, governance, insights and capability in about four minutes. And if you want to turn the results into a plan, book an AI Medical Affairs Assessment call.