Medical affairs has hit an evidence-volume wall. Congresses now produce hundreds of abstracts per indication, MSLs are expected to track competitor data as closely as their own, and clinicians arrive at meetings having already processed an AI summary the MSL hasn’t seen. Manual literature review and insight tracking simply cannot keep pace anymore.
Executive Summary (TL;DR)
The Problem: Medical affairs has hit an evidence-volume wall. Congresses now produce hundreds of abstracts per indication, MSLs are expected to track competitor data as closely as their own, and clinicians arrive at meetings having already processed an AI summary the MSL hasn't seen. Manual literature review and insight tracking simply cannot keep pace anymore.
The Strategy: AI already does real, measurable work in medical affairs - literature surveillance, insight aggregation, KOL briefing prep, medical information drafting. But clinical reasoning, scientific exchange, and KOL relationships remain human work. The goal isn't to automate medical affairs - it's to free MSLs and medical information teams from the volume problem so they can spend more time on the parts of the job that were always the actual value.
The Imperative: In 2026, AI literacy inside medical affairs starts to function like scientific training - a baseline competency, not a specialism. Teams that treat AI as an optional add-on will find their MSLs arriving to meetings less prepared than the clinicians they're meeting. Teams that build it into how the function works will free up real capacity and that capacity becomes the case for further investment.
Pharma & Life Sciences Practice • Brand Strategy Intelligence
Fig 1. A medical affairs / MSL team reviewing a dashboard showing AI-flagged literature and KOL insights on a large screen, mid-discussion
Ten years ago, an MSL covering a therapeutic area could realistically keep up with the published literature, the major congresses, and the handful of KOLs who mattered in their territory. That is no longer true, and it isn’t because MSLs have gotten less capable – it’s because the volume of evidence they’re expected to track has grown faster than any individual can absorb manually.
A single major oncology congress can now produce hundreds of abstracts in one indication alone. Real-world evidence publications have multiplied alongside randomised trial data. Competitor intelligence – once a quarterly briefing document – is now expected to be current within days of a new publication or congress presentation. And the audience on the other side of the table has changed too: clinicians increasingly arrive at meetings having already run a query through an AI tool that summarised the relevant literature for them, sometimes more current than what the MSL was briefed on that morning.
A 2026 global survey of 367 medical affairs professionals across 48 countries, published in Cureus, set out specifically to measure where the profession actually stands on AI adoption – not where vendors say it stands, but where MSLs, MSL leadership, and medical affairs executives report their organisations actually are. The fact that a study like this exists, and at this scale, says something on its own: this is no longer a speculative question for medical affairs. It’s an operational one.
This is the evidence wall. It isn’t a future problem. It’s the condition medical affairs teams are already operating under – and it’s the reason “digital transformation” in this function isn’t really about technology adoption for its own sake. It’s about closing a capacity gap that has already opened.
“A 2025 US-wide survey found healthcare's AI adoption rate was 8.3% - well behind finance (11.6%), education (15.1%), professional/technical services (19.2%), and information services (23.2%). But the same study found the adoption trend shifted sharply around late December 2024/early January 2025 - a 481.5% change in the rate of increase.”
The honest starting point is that AI in medical affairs is not, for the most part, doing anything exotic. It’s doing the high-volume, pattern-based work that used to consume the hours MSLs and medical information teams had left over after the actual scientific exchange.
This is the area where AI has moved fastest from pilot to production. Instead of an MSL manually setting up literature alerts and reading through them, AI tools now continuously scan new publications, conference abstracts, and real-world evidence sources, flag what’s relevant to a specific therapeutic area or product, and surface a summary. The MSL’s job shifts from finding the signal to deciding what the signal means – which is where their training actually adds value.
When an HCP submits a medical information request, a large share of the work involved – locating the relevant data, checking it against current labelling and approved responses, drafting a first version of the answer – can now be substantially accelerated by AI. The medical writer or medical information specialist still reviews, refines, and signs off. But the blank-page problem, which used to eat the most time, is mostly gone.
Before any meaningful conversation with a KOL, an MSL traditionally had to assemble a picture: recent publications, conference activity, public statements, areas of current research focus. AI-powered platforms now compile much of this automatically and continuously — not as a one-time profile but as a living picture that updates as the KOL publishes, presents, or comments publicly. Some platforms are now describing this as a “digital MSL” layer that sits on top of a CRM, monitoring an HCP’s activity and recommending what the human MSL should do next.
During and after major congresses, the volume of new data released in a short window is enormous. AI tools are increasingly used to triage this in near real time – flagging which abstracts are relevant to which brands, which competitor data points need a response, and which KOL commentary is worth following up on – work that previously took medical affairs teams days or weeks to fully process.In each of these cases, the pattern is the same: AI is absorbing the volume, so that the human in the loop spends their time on judgment rather than collection.
→ In a survey of 43 US health systems evaluating 37 AI use cases, 77% cited immature AI tools as a significant barrier to adoption - ahead of financial concerns (47%) and regulatory uncertainty (40%).
The risk in any digital transformation conversation is treating “AI can help with X” as equivalent to “AI should do X without oversight.” In medical affairs, that line matters more than in almost any other function, because the cost of getting it wrong isn’t just inefficiency – it’s a scientific or regulatory error reaching an HCP.
AI can summarise a study. It cannot reliably judge how that study’s findings should be weighed against a clinician’s specific patient population, prior treatment history, or local clinical guidelines – the kind of contextual reasoning that is the actual substance of a scientific exchange. An AI-drafted summary of new data is a starting point for an MSL’s conversation, not a replacement for it.
AI can tell an MSL what a KOL has published recently. It cannot tell them how that KOL felt about a contentious panel discussion last month, what informal concerns they raised over coffee at a congress, or how a relationship has evolved over years of engagement. These are the things that actually shape how a KOL receives a brand’s data – and they live in human memory and judgment, not in a database, however well-maintained.
AI-drafted medical information responses still need to be checked against current approved labelling, regional regulatory requirements, and the specific phrasing constraints that vary by market. An AI system trained on general medical literature does not inherently know the difference between what’s approved for promotional use in one market versus what would be considered off-label in another. This is precisely the kind of judgment that pharma organisations are now formalising – treating AI output review as a skill that requires the same rigour as scientific training, not an afterthought.
The practical implication is that “automation” in medical affairs is best understood as acceleration of preparation, not replacement of judgment. The MSL who used to spend three hours building a literature summary before a KOL meeting can now spend twenty minutes reviewing an AI-generated one – but the meeting itself, and the decisions about what to say in it, remain entirely human.
The phrase “digital transformation” gets used loosely enough that it’s worth being specific about what it should mean for a medical affairs function in 2026 – because the failure mode isn’t usually under-adoption. It’s adopting tools without redesigning the workflows around them.
A medical affairs team that buys an AI literature surveillance tool but keeps every other process unchanged hasn’t transformed anything – it’s just added a new input to an MSL’s inbox. The transformation happens when the time that tool frees up gets deliberately redirected: toward more KOL engagement, toward deeper preparation for advisory boards, toward the kind of proactive scientific outreach that used to get squeezed out by administrative load.
This also means digital transformation in medical affairs is as much an organisational design question as a technology procurement one. It requires deciding, explicitly, what MSLs and medical information teams should be doing more of once the volume work is automated – and then measuring whether that’s actually happening, rather than assuming efficiency gains automatically translate into better outcomes.
The teams that get this right in 2026 won’t necessarily be the ones with the most AI tools. They’ll be the ones that can clearly answer the question: now that our medical affairs team has more time, what are they spending it on and is that the right thing? Explore medical affairs solutions
A: Digital transformation in medical affairs refers to the adoption of AI and automation tools — for literature surveillance, medical information drafting, KOL profiling, and congress intelligence — combined with a redesign of team workflows so that the time these tools free up is redirected toward higher-value scientific engagement, rather than simply added capacity that goes unused.
A: The most established uses are literature surveillance and insight aggregation (AI continuously scans and flags relevant publications and congress data), medical information response drafting (AI produces a first draft that is reviewed and approved by a medical information specialist), KOL profiling (AI compiles and updates a picture of a KOL's recent publications and activity), and congress intelligence (AI triages large volumes of newly released congress data in near real time).
A: No, but it is changing what MSLs spend their time on. AI is well-suited to absorbing high-volume, pattern-based work like literature review and data aggregation. It is not well-suited to clinical reasoning in context, KOL relationship management, or the judgment required for regulatory and compliance review. The MSL role is shifting toward more time on these higher-judgment activities, not away from the role entirely.
A: The primary risks are over-reliance on AI-generated content without adequate human review (particularly for medical information responses, which must align with approved labelling and regional regulatory requirements), and adopting tools without redesigning workflows - which results in technology that adds noise rather than capacity. AI output in medical affairs should be treated as a draft requiring scientific and regulatory review, not a finished product.
A: Start with the highest-volume, lowest-judgment tasks - literature surveillance and insight aggregation are typically the easiest entry point because the output (a summary or flagged item) is naturally reviewed by a human before any action is taken. From there, the more important step is organisational: deciding what the team will do with the time that's freed up, and building that into how success is measured.
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