Is Your Medical Affairs Team Actually Using AI Well? A 10-Question Check.

Nearly every Medical Affairs team now has some AI in the mix: a chatbot, a summarizer, a tool someone in the group started using. Having AI and getting real value from it are different things. A lot of teams have adopted tools that look impressive in a demo but don't actually change how the work gets done.

Here's an honest self-check. For each question, the useful answer isn't "do we have a tool for this." It's "has this genuinely reduced the manual load on my team." Ten questions, grouped by where Medical Affairs work actually gets hard.

Congress and literature coverage

1. When a major congress releases its abstracts, does someone still triage them largely by hand? If your team is still reading and sorting thousands of abstracts manually, or is limited to keyword searches that miss abbreviations and variations in nomenclature, AI can help.

2. Can you search your scientific content by meaning, not just exact keywords? A search that finds your molecule only when someone typed its exact name is a keyword tool wearing an AI label. Real value is catching the abstract that used the generic name, the brand name, or the development code interchangeably. Good AI can also identify trends in the data or things that are related to or support each other, both things that would take a human weeks after the meeting to spot.

3. How long does a post-congress or literature synthesis take, and has that number actually dropped? If your synthesis reports still take the same days they always did, the AI you added isn't touching your most time-consuming, time-sensitive deliverable.

Speed and turnaround

4. Are your team's senior scientists still doing work a tool could draft? The test of good AI adoption isn't whether the tool is clever. It's whether it's freed your most valuable experts from the administrative load so they can do the scientific thinking only they can do.

5. When leadership asks for a fast readout, can you deliver it in hours instead of days? If the answer is still "days," the bottleneck is manual work AI should be absorbing.

Trust and accuracy

6. Do you trust the AI's output enough to use it, or does someone re-check everything from scratch? If your team quietly redoes the work because they don't trust the tool, you're paying twice. Good AI output is grounded, traceable to real sources, and reviewable. Verification is a quick check and part of the learning process from the deliverable. Not a redo.

7. Can you see where an AI answer came from? A tool that produces a confident summary you can't trace back to source documents is a liability in a scientific setting. You should be able to click through to the abstract, the paper, the primary data.

Coverage and completeness

8. Are there meetings or data sources your team simply can't cover because you don't have the people? If "we just don't get to those" is a real answer, that's exactly the gap AI is meant to close: comprehensive coverage without proportional headcount.

9. When someone leaves or is out, does institutional coverage knowledge leave with them? Good tooling makes coverage repeatable and not dependent on one person's spreadsheet and memory.

The honest one

10. If your AI tool disappeared tomorrow, would your team actually notice? This is the real test. If the answer is "not much would change," the tool isn't yet doing meaningful work. It's a login. The tools worth having are the ones your team would fight to keep.

What "using AI well" actually looks like

Using AI well in Medical Affairs isn't about having the most tools or the flashiest features. It's about whether the genuinely heavy, repeatable work, abstract triage, congress coverage, literature synthesis, the first draft of a report, is getting lighter, faster, and more complete, while your scientists spend their time on judgment and engagement rather than administration.

If you went through these ten and found more gaps than you expected, that's not a failure. It's most teams. The tools that close those gaps exist now; the question is just whether the ones you're using are actually doing it.

At Datym, this is exactly the work we built for: conference intelligence that takes the manual load off Medical Affairs teams, grounded in real sources and built by someone who did the job for fifteen years. If your answers to a few of these questions stung a little, it might be worth a conversation.