Established key opinion leaders publish frequently, speak at major congresses and already have industry relationships. The harder task is identifying physicians and researchers whose influence is growing before they gain wider recognition.

If your KOL list misses emerging scientific voices, you may overlook changes in regional influence, miss relevant engagement opportunities and build plans around yesterday’s network. Medical Affairs remains essential for validating scientific relevance and deciding whether engagement is appropriate.

Manual KOL identification favors the people you already know

Traditional KOL mapping often combines desk research, congress attendance, internal recommendations and regional spreadsheets. The result can be slow to refresh and heavily influenced by existing relationships.

That creates a visibility problem. A recognized professor at a major institution will appear in almost every search. A less established investigator leading relevant trials, publishing within a narrow disease area or building a strong regional network may remain outside the established list.

The gap is not theoretical. Veeva reports that 30% of global experts identified in Veeva Link Key People had no recorded interaction with MSLs. This does not mean all should be prioritized. It shows how much potential scientific reach can sit outside familiar networks.

The cost spreads across regions and functions

KOL planning draws on several functions. Commercial and Omnichannel teams define the market-planning need; Medical Affairs independently assesses scientific relevance and any proposed scientific engagement. Regional teams add context, and analytics teams connect permitted sources.

When teams use different criteria or files, several costs follow:

  • the same research is repeated across regions;
  • lists become difficult to compare or consolidate;
  • regional opportunities reach national teams too late;
  • engagement plans continue to favor established experts; and
  • teams spend time debating whose version is current.

The largest loss may be a late view of shifts in scientific influence across the therapeutic area.

Put a number on the manual work

There is no credible universal figure for what manual KOL identification costs. Therapeutic areas, data access and review processes differ too much. A transparent internal calculation is more useful.

ILLUSTRATIVE EXAMPLE  Eight regional teams complete two KOL mapping cycles each year. At 40 hours of research, reconciliation and review per cycle, the process consumes 640 hours. If a governed data pipeline and AI-enabled analysis remove half of that preparation, they free up 320 hours. At an illustrative blended cost of $100 per hour, that represents $32,000 of annual capacity.

This is not a promised saving. It excludes data, software, integration and governance costs, but gives Commercial Operations a baseline to replace with its own figures.

A national list can miss a regional emerging voice

Consider a national oncology team preparing engagement plans across Northeast, Midwest and Western regions. Its central KOL list includes established experts selected during the previous annual review.

Since then, an investigator at a Midwest cancer center has led a relevant trial, co-authored publications and gained congress visibility. The signals sit across separate sources, so the profile may not surface until a regional colleague notices it.

An AI-enabled solution can connect permitted sources, resolve the HCP’s identity and track changes in publications, trials, congress activity, guideline participation and approved interactions. Rather than produce a black-box ranking, it flags the profile and shows which signals changed.

Once Medical Affairs validates scientific relevance and confirms whether engagement is appropriate, Commercial and Omnichannel teams gain a stronger basis for market prioritization, engagement planning and channel strategy — as well as an earlier signal that the regional influence network is changing. The validation layer prevents an algorithmic signal from becoming an automatic outreach decision.

What a trustworthy solution should do

A practical AI-enabled KOL identification solution should:

  • bring permitted sources into a consistent, governed data view;
  • link duplicate profiles across institutions, systems and regions;
  • distinguish established prominence from recent growth in influence;
  • let teams adjust criteria by therapeutic area and regional need;
  • explain why each profile appears and which evidence supports it;
  • refresh on an agreed schedule or when teams trigger a review; and
  • route proposed profiles through Medical Affairs validation before they enter engagement planning.

In a Veeva account of data-driven KOL engagement, Ipsen described using consolidated data to validate current thought leaders and identify emerging experts. Bayer compared relationship-based lists with objective data to prepare more relevant conversations.

Governance matters as much as the algorithm       

Emerging KOL signals should support judgment, not replace it. Teams must test whether the model favors large institutions, English-language publishing, active digital profiles or regions with better data coverage.

For US programs, HCP professional data and patient-related information are not the same category. Teams should define permitted sources, use rights, access controls and retention rules. Most KOL identification should not require identifiable patient data.

If patient-related data is proposed, legal and privacy reviewers should assess whether the HIPAA minimum necessary standard applies. Where de-identification is needed, HHS describes Expert Determination and Safe Harbor as alternative methods under the Privacy Rule.

Find the next relevant voice, not simply more names

The objective is to identify relevant scientific influence earlier, explain why it matters and give regional teams evidence to review.

A focused first step is one therapeutic area, a small group of regions and a clear decision the system should improve.

AI can make emerging influence easier to see. The final decision about relevance and engagement should remain with the people who understand the science, the market and the relationship.

Emerging KOL identification is one of five connected AI workflows for pharma Commercial and Omnichannel teams. Explore all five use cases.

 

Sources

Veeva Systems, Four Pillars of Data-Driven KOL Engagement (2024)

Veeva Systems, Ipsen, Servier, Bayer: Insights on Data-Driven KOL Engagement for Launch and Beyond

US Department of Health and Human Services, Minimum Necessary Requirement

US Department of Health and Human Services, Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the HIPAA Privacy Rule