How Medical Affairs is Embracing AI to Drive Precision and Impact

Insights from Putnam’s Medical Affairs Leadership Council (Spring 2025)

Artificial Intelligence (AI) has moved from promise to practice in nearly every facet of the life sciences industry and Medical Affairs is no exception. During the Spring 2025 convening of our Medical Affairs Leadership Council (MALC), senior Medical Affairs leaders from across biopharma gathered to discuss how AI and data analytics are reshaping strategy, operations, and ultimately, patient impact.

Their conclusion? AI is no longer a futuristic concept, it is now a present-day imperative. However, adoption is still varied across the industry and challenges remain.

From Curiosity to Capability: The Growing Role of AI in Medical Affairs

Just one year ago, many Medical Affairs organizations were cautiously exploring AI with limited experience applying it in practice. Today, AI has become a routine partner throughout workstreams, from the development of publications, scientific communications, and still other digital solutions.

There is also a higher order evolution of AI applications towards both accelerating the synthesis of multiple data sources to generate medical insights more accurately and thoroughly, as well as facilitating the discovery of latent patterns and connections between data that were otherwise missed or difficult to detect in analog reviews of the past. Both emergent advancements help tailor engagements with healthcare providers that create a more customized set of insights that are specific, interesting, and well-metabolized – offering a novel and rich perspective for consumers. This boosts the impact of Medical Affairs interactions and products in a thoughtful way that is bespoke rather than a one-size-fits-all provision.

The human-technology interaction loop is also becoming well-established. However, determining where to apply AI and how to do so most effectively are still not fully unexplored. As one Chief Medical Officer of MALC expressed:

“[w]e talk a lot about insights, but how do we harness data and create simple AI-based methods, so everyone gets the same types of insights instead of living in silos?”

The sentiment was echoed across the council: AI has matured, but governance, consistency, and access remain critical hurdles.

The Rise of Precision Medical Affairs

Curated content through Precision Medical Affairs can be a differentiator – demonstrating value that unlocks highly effective patient care through data-driven, focused medical strategies. AI can offer advanced analytics capabilities that accelerate through the traditional learning curve planning teams face to offer comprehensive and accurate insights on new opportunity areas for healthcare teams. As a Medical Affairs VP of a large biopharmaceutical company put it:

“Imagine an election-style heatmap – only instead of voters, we’re mapping care gaps. AI enables us to get down to geographic and institutional granularity, revealing the drivers of disparity and tailoring [medical] strategy accordingly.”

The pace and caliber of data processing supported by AI enables teams to probe more granularly into data analyses with a broader range of existing datasets. Large datasets such as claims data can be analyzed alongside unstructured datasets like EMRs and EHRs to build holistic clusters of insights. These multi-source analytics powered by AI provide clustered insights that are far more detailed and descriptive than single-source analog analytics alone. Ultimately, the precision analytics value comes from the ability to tailor our strategies to patient and physician segments identified, and improve targeted messaging, strategic planning, and follow-up implementation considerations.

Accelerating the Feedback Loop with Data Analytics

A new opportunity area for AI in Medical Affairs is to incorporate integrated data analysis into the closed Medical Affairs feedback loop. The feedback loop is strengthened by integrating multiple data sources, enabling latent pattern recognition and reveals otherwise unforeseen connections that improve patient experiences and patient outcomes.

This feedback loop, from integrated data analysis to real-world outcomes; represents a shift from generic engagement to context-specific action. With the rise of tools that support personalization on a scale, the possibilities are growing daily. We note a greater shift from AI creating efficiencies in day-to-day rote tasks to leveraging its capabilities to pattern find and discover latent connections from synthesized data points that would have been otherwise been labor-intensive or time-consuming for analog teams alone. Trending data suggests that leveraging AI to understand multi-source datasets to create higher quality dialogue, materials, and understanding of patients and HCPs, among other stakeholders, is the next frontier. To get there, establishing strong core foundations of AI practice in our home organizations is imperative.

Working with AI in our Organizations: First-Step Action Steps

Not every organization is at the same stage of the AI journey. Despite enthusiasm, there are still challenges and lessons to be learned and shared about how to implement AI into Medical Affairs organizations. Some, especially smaller companies, are still experimenting cautiously. But leaders agree across the board: start small today with an eye to scale.

Some tips to design an AI-friendly organization that experienced MALC leaders found effective in their organizations include:

• “Everyday AI” applications, using AI in simple, routine tasks to test the waters and feel the impact quickly. Rote tasks like meeting transcriptions or first-draft documentation development repeat resources can immediately alleviate task burden while teams get comfortable using AI in their work processes.
• Governance frameworks that clearly define responsible AI use to remain compliant. Start conversations with your compliance folks early to understand and explore together rather than waiting.
• Training programs and use-case playbooks that demystify AI and share a roadmap for easy implementation. These “off the shelf” resources can help establish foundational knowledge about AI for all individuals to feel confident further applying it and experimenting in their roles.
• Secure platforms that respect data confidentiality while teams drive towards innovating working modes and ways of generating novel and dynamic insights.

Leading the Way Forward 

Plans need strong leadership to foster the right climate for transformation. Our MALC leaders emphasized the importance of both leadership support and organizational architecture to build a strong AI-augmented Medical Affairs functionality that is open to continuously learning new AI applications and ways of working together in a productive and compliant environment. Leading AI through connection with cross-functional team collaborations across the organization is also a key factor of organizational success with AI integration. These early engagements can bridge traditional silos between Commercial, R&D, and Medical Affairs, and in doing so, co-create shared mental models on how to harness AI for insight generation seamlessly and impactfully for shared value that multiples beyond the sum of our separate parts. These mental anchors and relationships help lift teams through the growing pains of AI literacy and AI adoption into little “i” innovation advancements and capital “I” innovation advancements alike over time.

Conclusion: What Comes Next

As one participant concluded, “If you’re not doing this now, you’ll be left behind. But the beauty of Medical Affairs is that we can lead, not just follow.” Medical Affairs is an apropos leader in the AI frontier as it keeps the heart of the biopharmaceutical industry mission front and center with transformations are grounded in strong, interdisciplinary collaborations, ongoing dialogue on best practices, and most importantly, a strategic spirit of experimenting fast and failing forward. When AI-augmented data analytics is executed well, it enables better patient care and ultimately, a healthier society. Looking ahead, the MALC community is focused on:

• Exploring the optimal strategy and value of agentic AI and chatbots in medical information and insights generation
• Building secure, scalable infrastructure for AI experimentation
• Driving AI adoption through access, advocacy, and action

As leaders, we can foster the growth of AI-augmented capability in Medical Affairs by developing clear, adaptive rules of engagement within our own organizations and sharing our uses cases with our community. There was consensus that Medical Affairs leaders themselves must evolve together. “Sometimes we get in our own way,” one executive reflected. “We need to become next-generation leaders, technologically fluent and strategically bold.”

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