Most pharma field integration initiatives are built on sound logic. Align medical and commercial. Scale scientific engagement digitally. Use AI to prioritize field activity. The strategy makes sense. The execution keeps breaking down.
The reason isn't culture or commitment. It's structural. The tools organizations build to enable integration, including dashboards, omnichannel platforms, compliance review processes, are almost universally designed to serve headquarters, not the field. Three specific design mistakes drive this, and each one is fixable without new technology.
Mistake 1: Your budget structure forces field and digital to compete
Medical affairs leaders know that field and digital serve different purposes. As Florin Draica, VP of US Medical Affairs at Shionogi, put it: “MSLs provide depth; complex scientific dialogue, KOL relationships, and real-time clinical judgment. Digital provides breadth; amplifying data across audiences who will never get one-on-one field time. Neither replaces the other.”
But knowing that doesn't change the budget reality. When both sit inside the same medical affairs P&L, every planning cycle turns them into competitors. Draica continued, "There's no competition between field and omnichannel. But... we see them in competition simply because we are part of a P&L."
The more productive frame, and one that's gaining traction, is to think of digital as a field quality multiplier, not a reach substitute. When an HCP engages with a plain language summary, watches a webinar, or opens a scientific email, that activity generates intent data. Those signals can be "warm transferred" to MSLs so they follow up with the right people at the right moment. It's functionally a qualified lead and it makes the MSL's time more valuable, not redundant.
As Marie-Ange Noué, Senior Director and Head of Scientific Communications at EMD Serono, described it: "When you are able to actually warm transfer those insights to your MSL teams, and they are now allowed to do the follow-ups in a very focused way. I think it starts being meaningful."
What to do:
• Separate "reach investment" and "depth investment" in your budget architecture rather than running both through a single line item
• Track digital engagement as an input to MSL prioritization, not a standalone channel metric
• Reframe the ROI conversation: digital success is measured partly by the quality of field interactions it enables
Mistake 2: AI is being deployed where it's needed least
The most common AI use case in field engagement is top-target identification. It's also the least useful one. Field reps already know who their top targets are. Those accounts are scheduled in advance and protected.
The real prioritization problem sits in the middle tier, the hundreds of accounts where human judgment alone can't efficiently rank options against available hours. That's where AI actually changes field behavior. As Ariel Han, advanced analytics and data science leader, explained: "It's those middle tier targets where you might need some help from AI to help you prioritize. If I have some time today, who should I talk to?"
There's one exception where AI justifiably overrides a scheduled route: real-time clinical triggers. In rare disease or oncology, a biomarker test result or new diagnosis creates a time-sensitive window. Han put it directly: "This is the time to influence that prescription decision." Outside that specific scenario, AI's value sits firmly in the middle tier.
A more strategic AI layer is also emerging, what Noué calls "return on intelligence": "using AI to be able to estimate the scientific share of voice... and sometimes even predict where clinical adoption may evolve." Most organizations are building the tactical layer. The strategic layer remains largely unbuilt.
What to do:
• Audit your current AI deployment: is it solving problems reps already know the answer to?
• Focus AI investment on mid-tier prioritization and real-time clinical triggers first
• Explore competitive intelligence applications that feed into field and omnichannel planning, not just individual call planning
Mistake 3: Insight systems are built for headquarters visibility, not field decisions
Ask your MSL to open their laptop on Monday morning. Does the system tell them what headquarters wants to know, or what they need to do next? The answer to that question diagnoses most field insight failures.
Dashboards are designed to give leadership a view of everything — coverage rates, call frequency, channel mix. That's a legitimate function. The problem is deploying them as field decision tools, which they were never designed to be. As Han put it: "Starting with a decision, not the data. What do we need the insight to do? Because it has to be tied to a decision... you can see why the dashboard is not necessarily a good idea."
The fix isn't a better dashboard. It's a different starting point. Design the insight system backward from the specific decision it needs to support — message testing, congress prioritization, account re-segmentation — and the data requirements become clear. Build a cross-functional triage process where commercial, analytics, and medical teams review field signals together, separate patterns from anecdotes, and tie validated insights to pending decisions.
The element most systems skip is the feedback loop. When field contributors don't know what happened to the insights they shared, they stop sharing. Han was direct: "The field would probably stop contributing if they don't know where their insights are gonna go. So it's critical to build this sort of feedback mechanism to say, hey, this is what you shared with us — and this is how we plan to use it."
Closing that loop isn't a courtesy — it's what keeps the input quality of the whole system from degrading.
What to do:
• Start every insight initiative with a specific pending decision, not a data inventory
• Build a triage process with cross-functional representation to validate signals before acting on them
• Create a systematic feedback mechanism: tell field contributors how their insights were used and what decisions they informed
• Audit current dashboards — identify which ones serve headquarters reporting and which ones (if any) serve field decisions, and treat them as separate design problems
One thread connects all three mistakes: the tools were built facing inward, toward organizational structure and reporting needs, rather than outward, toward the field decisions they're supposed to support. Reversing that directional assumption doesn't require a platform overhaul. It requires asking a different question at the design stage — not "what do we need to track?" but "what does the field need to decide?"
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Discover more on this topic at Pharma Customer Engagement USA 2026 (October 27-28, Philadelphia) - where commercial, marketing, medical, data and AI pioneers converge. Explore the agenda here.
