AI in medical affairs: MSL enablement, KOL insight, scientific exchange

AI is used in medical affairs to compress the work that surrounds scientific exchange, not the exchange itself. It prepares a medical science liaison for a physician conversation, structures the field insights that come back, drafts responses to unsolicited medical information requests, and maps the scientific standing of key opinion leaders across published and public signals. The judgment stays with the medical professional. The retrieval, summarisation, and structuring that used to eat their week does not.

Medical affairs is the function most people outside pharma cannot name and most people inside pharma now call the third strategic pillar, alongside research and commercial. McKinsey argued the case for that standing in its vision for the function in 2020, and the industry has been building toward it since. (Source: McKinsey, "A vision for Medical Affairs in 2025," 2020.) The reason it matters for AI is simple. Medical affairs is a scientific-communication function, and scientific communication is retrieval, synthesis, and documentation. That is precisely the shape of work current language models handle well, and precisely the shape of work that a compliant deployment can wrap in an audit trail.

This piece is analysis, not a description of a Nextdot deployment in medical affairs. Nextdot's production healthcare work sits elsewhere. The argument here is about where the fit is real and where it is not.

The function, and why the fit is unusually clean

Medical affairs owns the scientific relationship between a company and the medical community. Three activities carry most of the weight. Medical science liaisons hold peer-level scientific conversations with clinicians and researchers in the field. Medical information teams answer unsolicited questions from healthcare professionals about a product, its evidence, and its safety profile. And the function generates KOL insight, the structured intelligence about who the influential clinicians are, what they are publishing, and where the scientific consensus is moving.

None of this is promotional. That distinction is the whole reason the AI fit is cleaner here than in commercial. A medical affairs interaction is scientific exchange, governed by non-promotional standards and a firm firewall from the sales organisation. There is no claim to substantiate against a label, no fair-balance calculation, no share-of-voice clock. The compliance surface is different, and in several respects more forgiving, than the promotional review that governs marketing content. That does not make it unregulated. It makes the risk profile legible, which is what a serious deployment needs.

The volume pressure is real, and it is the part budgets already acknowledge. Field medical has become the single largest investment area inside medical affairs budgets, a signal that the industry is putting money against exactly the activities described here. (Source: ZS 2025 Medical Affairs Outlook.) [verify the 27 percent budget-share figure with a named ZS report page before publication] More field medical headcount means more preparation, more insight capture, and more downstream synthesis, all of which scale linearly with people unless something changes the unit economics of the work.

MSL enablement: prepare, capture, synthesise

An MSL's job is the conversation with the clinician. Everything around that conversation is overhead, and the overhead is substantial.

Before a meeting, an MSL has to know the clinician cold. Recent publications, trial involvement, stated positions at congresses, prior interactions logged in the CRM, and the current evidence base for the therapeutic area. Assembling that picture by hand across PubMed, congress abstracts, and internal notes is hours of work per meeting. A retrieval system that pulls the clinician's recent scientific output and the relevant internal history into a single structured briefing does not replace the MSL's judgment about what matters. It gives them the raw material in minutes instead of an afternoon.

After the meeting, the MSL owes the organisation an insight. What did the clinician say about an unmet need, a competitor's data, a gap in the evidence, a barrier to appropriate use. This is the highest-value output medical affairs produces, and it is routinely lost or flattened into a checkbox because writing it up properly is tedious. A model that turns an MSL's rough voice or text note into a structured insight record, tagged to therapeutic area and strategic theme, raises the quality of the data the whole function runs on. The MSL still decides what the insight is. The system handles the structuring.

Administrative load is a documented drag on this role. In one industry survey of medical science liaisons, administrative tasks and paperwork ranked among the top complaints MSLs raised about the position. [verify: PharmExec MSL survey, specific figure and date] Reducing that load is not a productivity nicety. It is the difference between a scientifically trained professional spending their week on scientific exchange or on data entry.

KOL insight: mapping without overreaching

KOL identification and mapping is where AI's pattern-matching is genuinely useful and where the failure mode is genuinely serious.

The useful version reads public scientific signal at a scale no analyst can match. Publication records, citation networks, trial leadership, guideline authorship, congress presentations. From that, it proposes a map of who holds scientific weight in a therapy area and how their positions relate. For a medical affairs team entering a new indication or a new region, that map is a starting point that would otherwise take a quarter to assemble.

The overreach version is the one to refuse. The moment a KOL scoring system starts ranking clinicians by their commercial usefulness, or ingesting interaction data to optimise for prescribing influence, it has crossed out of scientific engagement into something the medical-commercial firewall exists to prevent. A KOL map built by medical affairs must serve scientific exchange, not target it. The system design has to enforce that boundary, which means the data it is allowed to ingest and the outputs it is allowed to produce are a compliance decision, not an engineering convenience. Any Indian pharma enterprise building this also inherits DPDP 2023 obligations on the personal data of the clinicians being profiled, since a KOL profile is personal data about an identifiable person.

Medical information: the clearest case

Medical information is the function that fields unsolicited questions from healthcare professionals. A clinician asks whether a drug has data in a specific patient subgroup, or how it interacts with another agent, and the medical information team answers from the approved evidence base and standard response documents.

This is the cleanest AI fit in the entire function, because the work is retrieval against a controlled, approved corpus. A model connected to the standard response library and the approved label can draft an accurate, sourced response to a routine inquiry in seconds, with every statement traceable to its source document. The human medical information specialist reviews and sends. For the volume of routine, repeatable inquiries, that is a large time saving with a small risk surface, provided two conditions hold: the model retrieves only from the approved corpus, and a qualified human signs every outbound response. Volume pressure across pharma is real and rising. Overall US medicine spending grew 10.2 percent in 2024, driven mainly by higher utilisation and new drugs. (Source: ASHP national drug expenditure report, 2024.) More drugs in more hands means more questions arriving at the medical information desk.

What AI must not do here

The line is the same one that governs every regulated deployment. AI does not carry accountability, and scientific exchange is an accountable act.

An MSL's scientific conversation with a clinician is a peer exchange between two qualified people. A model does not have that standing and cannot hold that conversation. A medical information response that reaches a treating physician carries the company's scientific credibility and, downstream, patient consequences. A named professional owns it. And the medical-commercial firewall is not a guideline a deployment can soften for efficiency. A system that lets promotional intent leak into medical affairs work is not a faster medical affairs function. It is a compliance failure waiting for an inspection.

The correct architecture treats the model as preparation and structuring for a qualified human, wrapped in the observability any regulated deployment needs: what the model retrieved, what it drafted, what the human changed, and who approved the output. Get that right and medical affairs is one of the highest-return, lowest-drama places to put AI in a pharma organisation. Get it wrong and it becomes the reason legal shuts the whole programme down.

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