How AI Redefines The Functional Service Provider (FSP) Opportunity In Medical Affairs
By Robert Stevens, principal, RS Consultative
As large pharmaceutical companies confront intensifying financial pressures — from patent expirations and declining R&D productivity to constrained reimbursement environments — every function is under renewed scrutiny. Medical affairs, long perceived as a support function adjacent to commercial and clinical development, has emerged as a strategic pillar of the biopharmaceutical enterprise. Its elevation is not incidental; it reflects the increasing value placed on scientific credibility, real-world evidence generation, and strategic influence across the enterprise. Yet with its rise in prominence, medical affairs now faces the same imperatives that have reshaped other core functions: the need to scale efficiently, validate its contributions, and deliver tangible value creation at the enterprise level.
At the same time, the expectations placed on medical affairs have grown markedly more complex. The function is now tasked with driving differentiated scientific engagement, producing globally resonant content, synthesizing vast and varied data streams, and generating actionable insights that are not merely responsive to the market, but actively shape and anticipate it. These expectations come with compressed timelines, escalating regulatory scrutiny, and increasingly lean internal teams. Nowhere are these pressures perhaps more acute than in medical communications, where rising demand collides with traditional operating and resourcing models that are no longer fit for purpose. In this environment, medical affairs can benefit by taking a page from clinical development and embrace FSP models — especially those powered by AI. Strategic partnerships with AI-enabled FSPs offer a pathway to long-term scalability, operational resilience, and a restructured model of medical affairs that is more adaptive, efficient, and sustainable in the face of continued industry disruption.
Compounding the challenge, medical affairs’ track record with digital innovation has been inconsistent. While some teams have experimented with omnichannel engagement and other digital initiatives — with pockets of success — most efforts have fallen short of meaningfully elevating the customer experience or driving measurable performance gains. This uneven progress reflects a deeper structural inertia: a persistent reluctance to reallocate budgets away from traditional, well-understood activities toward newer, unproven digital capabilities. Just as telling is the typical approach to digital leadership within the function, often assigning responsibility to digitally minded MSLs, publication leads, or medical directors already embedded in day-to-day operations. While well-intentioned, this model rarely delivers true transformation; in fact, it may be the very reason why progress has remained so limited. Driving innovation requires dedicated transformation agents with the mandate, expertise, organizational seniority, and innovative mindset to think differently. Given medical affairs’ historical reluctance to look outside its traditional talent pool, FSP partnerships — especially with AI-enabled providers — offer a compelling way to access the specialized skills, speed, and bold thinking required to break through the status quo.
In 2023 alone, more than 2.5 million biomedical articles were published on PubMed — on top of an accelerating flow of insights from real-world evidence, health economics, and digital endpoints. For medical affairs, and medical communications in particular, this represents both an expanding opportunity and an operational burden. Teams must help their peers synthesize, translate, and disseminate this avalanche of information — often across multiple markets and stakeholder groups — with speed, precision, and unwavering compliance. The question is no longer whether this work is essential, but whether existing models are structurally equipped to manage it. Can traditional in-house-only approaches keep pace? Can in-house teams, even when supported by conventional agency partners, truly meet the demands of this new complexity, scale, and velocity?
The emergence of generative AI and agentic AI — autonomous systems capable of performing complex cognitive tasks — represents a watershed moment for content-intensive, voluminous, transactional functions like medical communications. These tools are already capable of summarizing scientific literature, drafting medical information response letters, extracting claims from publications, monitoring literature and surfacing emerging signals, creating compelling scientific content, and automating HCP engagement and MSL follow-up. According to Deloitte’s 2024 Life Sciences Outlook, generative AI could reduce medical content development time by 40%–60%, and paired with an FSP model, deliver up to 50% cost savings.
In a cost-constrained environment, the case for rethinking delivery models becomes not just compelling — but imperative.
Three Reasons To Reconsider FSP For Medical Communications
For nearly two decades, FSP models have delivered measurable impact in clinical development, enabling pharmaceutical companies to scale operations, reduce fixed costs, and gain access to specialized capabilities across geographies. FSPs have become the default outsourcing model for data management, clinical operations, biostatistics, and regulatory writing — precisely because they combine flexibility, quality, and operational rigor.
Yet, despite similar needs for scale, compliance, and speed, medical communications teams have not fully embraced FSP in the same way. The time is ripe to close this gap. As medical affairs grows in strategic importance, the demands on medical communications teams mirror those long faced by clinical development teams: managing scientific complexity, maintaining high quality, and delivering under increasing time and budget constraints.
If FSPs have proven their value in clinical development — where patient safety and regulatory scrutiny are paramount — why wouldn’t they thrive in medical communications? With the right safeguards, governance, and AI augmentation, the FSP model can bring the same discipline, scalability, and innovation to medical affairs.
1. Faster Scientific Engagement at Scale
Medical communications is under mounting strain. What was once a content delivery function is now expected to generate timely, compelling scientific narratives across an expanding array of channels — with fewer resources and under intensifying regulatory oversight. Generative AI offers a step-change opportunity. With human-in-the-loop oversight, it can rapidly draft literature summaries, medical response letters, publications, FAQs, draft scientific communication narratives, patient journeys, and similar assets — significantly compressing production timelines and reducing pressure on internal teams.
But realizing this potential requires more than new tools — it requires structural change. By shifting executional activities such as content generation, communications management, and channel engagement to AI-enabled FSP partners, medical affairs can reduce dependence on high-cost internal FTEs, access scalable resourcing, and tap into pre-built AI infrastructure — where operational kinks have already been worked out. This offers a faster, lower-risk path to harness AI at scale. Freed internal capacity can then be redeployed or reprioritized to other higher-value areas for medical affairs.
2. Tiered Automation Enables Smarter Scope Management
Agentic AI unlocks new possibilities for automation, such as autonomously flagging relevant literature, generating structured evidence tables, and initiating HCP follow-ups. These capabilities make tiered automation a practical reality in medical communications:
- Tier 1: High-volume, low-complexity content (e.g., FAQs, medical response letters)
- Tier 2: Moderate-complexity content requiring SME oversight (e.g., advisory board summaries)
- Tier 3: High-risk, strategic content (e.g., scientific platforms, objection handlers)
AI-enabled FSP partners can absorb and operationalize Tiers 1 and 2 at scale, allowing internal teams to focus on high-impact Tier 3 deliverables. This division of labor reduces cost, accelerates turnaround times, and enhances accountability — while preserving strategic focus.
3. FSPs as Low-Risk Innovation Accelerators
Many medical affairs teams recognize the promise of generative AI but face internal barriers from regulatory, IT, legal, or compliance stakeholders. Others are simply paralyzed by organizational risk aversion. Here, FSPs can serve as safe sandboxes for innovation.
As an example, today’s leading FSPs could offer:
- Secure AI sandboxes that allow medical affairs to pilot generative workflows — like medical response letters or literature summaries — without putting core infrastructure or compliance at risk.
- Domain-trained large language models (LLMs) built on biomedical and regulatory content, enabling faster, more accurate drafting of scientific narratives, FAQs, and objection handlers.
- Tiered automation frameworks where AI handles high-volume, low-complexity content (e.g., FAQs), while SMEs focus on strategic, high-risk deliverables.
- Embedded insight engines that analyze scientific literature, congress outputs, and HCP interactions in real time — surfacing trends and unmet needs proactively.
- Audit-ready AI explainability tools that track model decisions, highlight flagged content, and support medical/legal/regulatory (MLR) reviews.
These capabilities make it possible to pilot, validate, and refine AI-enabled workflows without exposing core systems or enterprise infrastructure.
Addressing Three Key Contradictions
Despite the clear advantages, skepticism toward FSP models within medical affairs persists. Below, we explore three of the most common objections — and how today’s operating environment reframes them.
Contradiction 1: Outsourcing Dilutes Scientific Integrity
AI-assisted tools now embed safeguards such as version control, automated referencing, and scientific traceability — substantially reducing the risk of inconsistency or inaccuracy. Leading FSPs deploy therapeutic-area-aligned teams who operate within established SOPs, quality frameworks, and joint governance structures. Generative AI acts as a draft accelerant, with human oversight ensuring scientific rigor. As seen in clinical development, risks can be effectively mitigated through careful partner selection and strong governance.
Contradiction 2: FSP Models Are Too Rigid for Agile Needs
Modern FSP contracts are built for flexibility. Modular resource pools, 24/7 support models, and embedded AI tools allow for proactive responsiveness. Often, FSPs are faster and more responsive than internal teams, given their constant exposure to emerging scientific trends across sponsors.
Contradiction 3: AI Risks Compromising Compliance
Compliance is a valid concern. However, leading FSPs now deploy human-in-the-loop pipelines and run AI pilots in secure sandboxed environments. These setups allow for controlled validation and risk isolation, with increasing support from regulators such as the FDA.
The Strategic Imperative
Medical affairs can no longer afford to operate within legacy structures while the pace of science, regulation, and engagement accelerates. AI-enabled FSP models offer more than operational relief — they provide the blueprint for a medical affairs function built to scale, adapt, and lead.
To realize this future, medical affairs leaders must reassess the division of labor within their organizations. As AI becomes more sophisticated and more embedded, clarity is needed on which tasks should be automated, which should be AI-augmented, and which must remain human-led. This is not just about efficiency — it’s about precision in capability deployment.
Those who embrace this shift are not outsourcing value; they are re-architecting how value is delivered. The organizations that act now — redefining their operating models and workforce strategies — will set the pace for the next era of medical affairs. The rest will be left trying to catch up.
References:
- Deloitte. (2024). Life Sciences Outlook 2024: Disruption and Digital Maturity. https://www2.deloitte.com/global/en/pages/life-sciences-and-healthcare/articles/life-sciences-industry-outlook.html
- U.S. Food and Drug Administration. (2023). Discussion Paper: Artificial Intelligence and Machine Learning in Drug Development. https://www.fda.gov/media/166402/download
- Nature Medicine. (2023). Performance of Generative AI Models in Medical Content Creation. https://www.nature.com/articles/s41591-023-02556-8
- PubMed. (2023). Annual Biomedical Publication Count. https://pubmed.ncbi.nlm.nih.gov/?term=2023%5Bdp%5D
About the Author:
Rob Stevens is the founder of RS Consultative, LLC, a boutique advisory firm that partners with life sciences organizations to unlock digital, data, and AI-driven value at scale. A recognized leader in transforming how pharmaceutical companies operationalize innovation—and how technology companies refine their go-to-market strategies for life sciences—RS Consultative delivers measurable impact by aligning emerging technologies with strategic priorities.
A core focus of Rob’s consulting work is the successful implementation and scaling of enterprise AI strategies, bridging vision to execution through pragmatic, outcome-oriented roadmaps.
With over 20 years of combined experience at Novartis and Pfizer, Rob most recently served as Vice President & Global Head of Digital & Data Insights for Global Medical Affairs at Novartis. His prior roles include Head of Procurement for U.S. Clinical Development & Medical Affairs at Novartis and a sourcing leadership position in Global Development Outsourcing at Pfizer.