Most pharma companies are using AI somewhere between discovery and commercialization. On the commercial side, one panelist at Fierce Pharma’s September 2026 session said they feel most confident about a specific use: helping a rare disease field team find patients by combining new data sets with AI so reps are in the right place at the right time, addressing the right barriers. (Part two of five from the panel. Part one: just because you can personalize to one, should you?)
The result, and the caveat
The panelist said they see roughly two to three times the impact when the field actually acts on the AI-generated suggestion. Note the condition: the lift appears when reps take the suggestion, not simply when it’s generated. That figure was stated on stage and isn’t independently documented, so quote it as the panelist’s own result.
Why these suggestions worked: they were friendly
The interesting detail was the format. The underlying data was turned into a suggestion that reads like a useful prompt rather than a model score, for example that a specific physician just published a new article or took part in a speaker bureau program. Those are small, human reasons to make a call, and they give the rep a natural opening.
How they measured it
The panelist laid out a simple chain of questions:
That’s a better measurement habit than counting recommendations produced. It separates usage, action and outcome, so you can see where a suggestion engine is leaking value. Marketers in any industry could borrow it; it’s the same reason I tell SEO teams that deliverables won’t get you renewed.
What the vendor literature adds
Next-best-action platforms typically combine CRM, prescription and digital engagement data and recommend a single next step per healthcare professional, with a feedback loop from reps. One vendor page from Tiger Analytics claims 20 to 40 percent higher HCP response rates from preference-aligned outreach, but it gives no methodology or sample size, so treat that as marketing. The page also says nothing about adoption barriers, which is where the panel’s point matters.
On the access side, a Precision AQ survey of 100 practicing providers describes HCPs seeing fewer reps each week, which makes each interaction more valuable. The detailed findings sit behind a download form, so only the report’s themes were verified here.
Practical checklist
- Write suggestions as plain-language reasons to call, not scores.
- Track the full funnel from opened to viewed to acted to outcome.
- Put a rep feedback button on every suggestion and use it to retrain. (A rep feedback button is a human-in-the-loop eval, the same idea behind calibrating AI judges.)
- Pilot with one team before rolling out, and compare acting reps with non-acting reps to size the lift.
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