Executive Summary
Every major technology shift creates new winners, not because they adopt technology first, but because they adapt their operating models faster. AI is creating such a moment for Medical Affairs. As scientific information increasingly flows through AI-powered discovery platforms, organizations have an opportunity to rethink how evidence creates impact, how scientific engagement evolves, and where human expertise delivers the greatest value. This paper captures the perspectives of the Medical Affairs Digital Strategy Council on the emerging operating model that may define the next era of Medical Affairs.
The Medical Affairs Digital Strategy Council brings together over 15 Medical Affairs leaders from leading biopharmaceutical organizations across global and regional roles. Since its inception in 2019, the council has served as a forum for industry leaders to exchange perspectives on how digital innovation is reshaping Medical Affairs and to explore new operating models that enable greater scientific and business impact.
Every major technology shift creates new winners, not because they adopt technology first, but because they adapt their operating models faster.
The Discoverability Economy: A New Era for Medical Affairs
An emerging theme dominated a recent council discussion: the shift from a world where humans primarily searched for scientific information to one where AI increasingly determines how that information is discovered, interpreted, and presented.
For the past two decades, digital strategy has largely focused on helping people find content. The rapid adoption of AI-powered answer engines introduces a parallel challenge, helping machines find, understand, and accurately represent scientific evidence. Just as Search Engine Optimization (SEO) became essential during the rise of internet search, AI Visibility Optimization may become an equally important capability as Healthcare Professionals (HCPs) increasingly rely on AI systems to access scientific information.
This evolution extends beyond publication quality, journal impact, or evidence generation. Those foundations remain critical, but they may no longer be sufficient to ensure that evidence is surfaced, interpreted accurately, and incorporated into clinical decision-making. Several organizations are already beginning to evaluate how effectively their scientific content is represented across AI platforms, signalling the emergence of an entirely new performance discipline for Medical Affairs.
When AI Becomes the First Reader of Science
Historically, Medical Affairs measured content performance using metrics such as publication acceptance, citation volume, journal impact factor, website engagement, and medical information requests. These measures assumed that HCPs discovered evidence directly through journals, conferences, websites, or scientific exchange.
AI-mediated discovery fundamentally changes that assumption.
Increasingly, scientific information may first be retrieved, synthesized, and prioritized by AI before it reaches a clinician. Consequently, discoverability is no longer determined solely by publication, it is also influenced by how AI systems identify, interpret, and present scientific evidence.
This shift raises a new strategic question for Medical Affairs:
How visible is our evidence within AI-driven scientific discovery?
Council members discussed the emergence of AI visibility as a measurable capability that could eventually become part of routine publication planning. Rather than evaluating discoverability after publication, organizations may increasingly assess whether manuscripts are likely to be surfaced accurately by AI systems before they are submitted. Potential measures could include:
Discoverability Score
Frequency with which scientific content appears in response to relevant medical and clinical queries.
Citation Frequency
Frequency with which AI-generated responses reference scientific publications.
Representation Accuracy
Accuracy of AI-generated summaries in reflecting the original scientific findings.
Coverage Rate
Consistency with which key scientific messages are retrieved across AI platforms.
Source Attribution Rate
Extent to which AI identifies the original source of evidence.
Retrieval Consistency
Reliability of scientific findings across repeated searches and prompt variations.
Scientific Fidelity Index
Alignment of AI-generated conclusions with the underlying scientific evidence.
Although these measures continue to evolve, they point toward a broader shift in how Medical Affairs may evaluate scientific impact. In an AI-driven environment, success is no longer defined only by publishing high-quality evidence, but also by ensuring that evidence remains visible, accurately represented, and trusted when clinicians increasingly rely on AI-assisted discovery.
Orchestrating the AI-Native Medical Affairs Organization
Among the many ideas discussed during the council sessions, one emerged as a particularly compelling description of the future Medical Affairs leader; the Conductor.
Medical Affairs leadership has been defined by scientific expertise and excellence within individual functions such as publications, medical information, field engagement, or evidence planning. As AI becomes embedded across these workflows, leadership responsibilities expand beyond functional execution toward orchestrating integrated systems of people, processes, and intelligent agents.
Rather than managing isolated activities, future leaders may increasingly oversee interconnected workflows that span the entire Medical Affairs value chain. This shifts leadership from executing work to designing and governing how work flows across the organization.
Council members emphasized that this requires a systems-level understanding of how Medical Affairs functions interact, including:
How field insights and information influence evidence generation
How evidence informs publication strategy
How publications evolve into multichannel scientific assets
How content progresses through MLR review
How scientific engagement generates new insights
How knowledge moves across the enterprise
While organizations are rapidly developing AI capabilities, members observed that the greater challenge is rarely building individual AI agents. The more difficult task is integrating them into coordinated workflows that create measurable business value.
Rather than managing isolated activities, future leaders may increasingly oversee interconnected workflows that span the entire Medical Affairs value chain.
Within this emerging operating model, the Conductor performs three essential functions:
Workflow Design
Aligning AI capabilities with scientific objectives and business processes.
Workflow Orchestration
Connecting people, workflows, and AI agents into an integrated operating model rather than isolated point solutions.
Workflow Governance
Ensuring quality, accountability, compliance, trust, and appropriate human oversight across AI-enabled workflows.
As execution becomes increasingly automated, organizational value shifts toward designing, coordinating, governing, and continuously improving systems of work. The future competitive advantage may therefore lie less in deploying individual AI solutions and more in orchestrating them effectively across the Medical Affairs enterprise.
The Human Advantage in an AI-Driven Enterprise
One of the most distinctive insights from the council discussion centered on an important question: what kinds of signals are AI systems least equipped to detect?
Large Language Models (LLMs) excel at identifying recurring patterns, synthesizing large volumes of information, and recognizing dominant trends. Medicine, however, often advances through exceptions rather than averages.
Emerging safety concerns rarely begin as dominant patterns. Evidence gaps often appear first as isolated observations. Changes in physician behaviour may initially surface through a small number of unexpected questions before evolving into measurable trends. These weak signals are difficult to detect precisely because they are statistically insignificant until they become strategically important.
Council members reflected on situations where seemingly minor anomalies later proved to have significant scientific or clinical implications. In some cases, unusual insight patterns were identified early enough to inform development teams before becoming widely recognized issues. Such observations could easily be overlooked in AI-driven summarization because they represent outliers rather than recurring themes.
This discussion highlighted an important implication for Medical Affairs. As AI becomes increasingly effective at identifying common patterns, human expertise becomes more valuable in recognizing exceptions, uncertainty, and emerging risk.
Future Medical Affairs organizations may therefore invest deliberately in capabilities such as:
Rare Event Detection
Identifying low-frequency but high-impact scientific signals.
Weak Signal Monitoring
Recognizing emerging trends before they become widespread.
Scientific Anomaly Identification
Detecting unexpected findings that warrant further investigation.
Evidence Gap Detection
Identifying unanswered scientific questions requiring additional research.
Outlier Insight Review
Evaluating observations that fall outside expected patterns.
Contrarian Trend Analysis
Challenging prevailing interpretations when new evidence emerges.
Early Warning Signal Escalation
Bringing potentially significant findings to the appropriate stakeholders before broader recognition.
Rather than serving primarily as reviewers of AI-generated outputs, Medical Affairs professionals may increasingly become custodians of scientific uncertainty; interpreting exceptions, validating emerging evidence, and exercising judgment where algorithms remain least effective.
Paradoxically, the more capable AI becomes at recognizing established patterns, the greater the value of human expertise in identifying what falls outside those patterns. In the future, one of Medical Affairs' most important contributions may be recognizing signals that AI has not yet learned to see.
The Rise of Hybrid Scientific Engagement
The discussion around AI visibility naturally led to a broader question: how does Medical Affairs create value when scientific information becomes instantly accessible through AI?
Historically, Medical Affairs operated in an environment where scientific expertise was closely tied to information access. Publications, medical information teams, congresses, and Medical Science Liaisons (MSLs) served as the primary channels through which HCPs obtained evidence and expert guidance.
AI-powered scientific discovery platforms are beginning to reshape that model. As these systems become increasingly capable of retrieving, synthesizing, and presenting evidence in real time, the effort required to obtain scientific information continues to decline. The differentiator therefore shifts from providing access to helping clinicians interpret and apply evidence within increasingly complex clinical settings.
Council members expressed differing perspectives on the pace of this transition. Some believed clinicians would continue to value direct engagement for scientific dialogue, contextual interpretation, and nuanced discussion. Others questioned whether future generations of HCPs would distinguish between insights delivered by trusted experts and those delivered by trusted AI systems. Regardless of the pace of adoption, members agreed that these developments require Medical Affairs to reassess which aspects of its operating model create enduring value.
One area that generated particularly strong consensus was implementation science. While AI may become increasingly effective at answering scientific questions, healthcare systems continue to face a more fundamental challenge - translating evidence into routine clinical practice.
Within this environment, Medical Affairs creates value not simply by communicating evidence, but by helping stakeholders interpret uncertainty, navigate clinical trade-offs, understand local practice realities, and accelerate evidence adoption. This shifts the role of the MSL from being primarily a messenger of information to becoming an enabler of implementation.
Future MSL activities may increasingly focus on:
Clinical Contextualization
Helping HCPs understand how evidence applies across different patient populations and treatment settings.
Implementation Facilitation
Supporting the integration of evidence into clinical workflows and routine practice.
Scientific Problem Solving
Helping stakeholders navigate evidence gaps, unanswered questions, and therapeutic complexity.
Barrier Identification
Understanding why evidence is not being adopted and helping organizations address those barriers.
Practice Transformation
Supporting broader efforts to translate scientific evidence into improved patient care.
The Emergence of the Virtual MSL
Unlike content automation, which primarily improves operational efficiency, virtual scientific engagement has the potential to reshape a much larger component of Medical Affairs operations. As a result, organizations are likely to evaluate where AI can enhance scalability while preserving the areas where human expertise remains indispensable.
If AI systems can retrieve evidence, engage conversationally, support multiple languages, and operate continuously, many routine aspects of scientific exchange become technically automatable. Virtual MSLs could provide immediate responses to standardized medical information requests, support routine scientific inquiries, and extend access to evidence beyond traditional field engagement models.
Council members agreed that AI is well suited to supporting standardized medical information responses and routine scientific interactions. However, scientific judgment, contextual interpretation, relationship building, and implementation support continue to depend heavily on human expertise.
Rather than replacing traditional scientific engagement, AI is more likely to expand its reach through a hybrid operating model in which each contributes according to its strengths:
AI manages routine inquiries and first-level scientific engagement.
AI retrieves and synthesizes evidence at scale.
Human experts focus on ambiguity, interpretation, implementation, trust, and strategic scientific dialogue.
The future challenge for Medical Affairs is therefore not deciding between human and virtual engagement but designing operating models in which both reinforce one another to improve scientific impact.
Building an AI-Native Medical Affairs Organization: From Scientific Excellence to Scientific Impact
The discussions within the Medical Affairs Digital Strategy Council point to a broader transformation than the adoption of AI technologies alone. Collectively, they suggest that Medical Affairs is entering a period where scientific influence will increasingly depend on how evidence is discovered, interpreted, governed, and translated into practice.
Across the discussion, four interconnected shifts emerged.
First, as AI becomes an increasingly important gateway to scientific information, organizations may need to expand how they define the impact of scientific evidence. Beyond traditional publication and engagement metrics, understanding how evidence is surfaced and represented by AI systems may become an important dimension of scientific performance.
Second, as intelligent agents become embedded across Medical Affairs workflows, leadership may increasingly shift from managing individual activities to orchestrating integrated systems of people, processes, and AI. Designing, governing, and continuously improving these workflows may become a defining capability for future Medical Affairs organizations.
Third, the discussion highlighted that the growing use of AI does not diminish the importance of human expertise. Instead, it changes where that expertise creates the greatest value. As AI becomes more effective at recognizing common patterns, Medical Affairs professionals may increasingly contribute through scientific judgment, contextual interpretation, and the ability to identify uncertainty, emerging risks, and signals that require deeper investigation.
Finally, scientific engagement itself may continue to evolve. While AI has the potential to support routine scientific interactions and improve access to evidence, the role of Medical Affairs may increasingly center on helping HCPs interpret evidence, navigate complexity, and accelerate its application in clinical practice. Rather than replacing human engagement, AI is likely to reshape how organizations combine digital and human capabilities to deliver scientific value.
Taken together, these shifts point toward a broader evolution in the Medical Affairs operating model. Success may no longer depend solely on generating high-quality scientific evidence, but also on ensuring that evidence remains discoverable, trusted, connected across workflows, and translated into meaningful clinical impact.
Many of these capabilities are still emerging, and their pace of adoption will vary across organizations. Nevertheless, the discussion suggests that Medical Affairs leaders have an opportunity to begin shaping the operating models, governance approaches, and measurement frameworks that will support this next phase of transformation.
Ultimately, the question is no longer whether AI will influence Medical Affairs, it already is. The more important question is how organizations will design Medical Affairs functions that combine scientific excellence, human judgment, and AI-enabled capabilities to remain trusted partners in an increasingly AI-mediated healthcare ecosystem.
Acknowledgements
We thank the members of the Medical Affairs Digital Strategy Council for their active contributions towards this paper.
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