Pharma marketing is entering a period of significant change. As AI creates new possibilities for customer engagement, organizations face a bigger challenge: rethinking how marketing teams operate, collaborate, and deliver value to healthcare professionals and patients.
At the Indegene Digital Summit 2026, Kate Duffy, Vice President, Commercial marketing AI at Pfizer, and Madelene Glomsten, Head of Global Marketing at Sanofi, explored what this transformation means in practice. Their discussion highlighted the changes needed in organizational structures, workflows, talent, and partnerships to build a more connected and effective commercial model to fuel the future of pharma.
Traditional Ways of Working Are Limiting What's Possible
Pharma marketing has long operated within established structures, with separate strategies across brands, therapeutic areas, and functions. While some boundaries remain essential for regulatory and compliance reasons, others can make it difficult to build a connected view of customers and their needs.
At the same time, organizations are investing in AI pilots and new capabilities without always establishing how these will work at scale. Teams may demonstrate improvements in individual activities, but integrating these changes into everyday operations remains difficult.
The discussion brought attention to a central challenge: How can pharma use AI to rethink its marketing model while building the capabilities, processes, and workforce needed to make that change sustainable?
Six important shifts emerged from the conversation.
1. Move Beyond Brand Silos to Understand the Customer Holistically
The first shift begins with how pharma thinks about its customers.
Marketing strategies are often organized around individual brands, therapeutic areas, or broader portfolios. Yet healthcare professionals do not approach patient care through these same organizational divisions. A physician may need to consider several treatment options across different therapeutic areas when caring for patients. AI creates opportunities to connect information across these boundaries and support more individualized HCP engagement.
Agentic workspaces, for example, can take on more of the data-intensive work required to bring together customer insights, opening possibilities for more personalized engagement strategies.
However, greater integration does not mean removing necessary safeguards. Boundaries between commercial, medical, and market access functions continue to serve important purposes. Medical, legal, and other specialists have a critical role in determining where information can be connected and where protections must remain.
2. Redesign Marketing Workflows Before Introducing AI
The traditional field-led model remains important, but the panelists challenged the assumption that established ways of working should continue unchanged. Introducing AI into existing marketing processes without questioning those processes may limit the benefits organizations can achieve.
The opportunity is to examine workflows more fundamentally. What activities should teams continue doing? What can be stopped or automated? Where should marketers spend more time on strategy, creativity, and decisions that require human expertise?
These questions extend to organizational design. As AI takes on more activities, responsibilities may change, new skills may become necessary, and teams will need to reconsider how people and technology work together.
3. Build for Scale, Not Just Successful Pilots
The panelists highlighted a familiar pattern: AI pilots generate excitement, attract committed teams, and demonstrate what is possible. The difficulty often begins when organizations attempt to extend those capabilities beyond the original group and embed them into everyday work.
A major consideration is data readiness. Kate highlighted that pilots and proofs of concept may begin before the underlying data is fully AI-ready. Scaling AI successfully requires organizations to rethink how data is organized and invest in the appropriate data and content pipelines, alongside the skills needed to support new workflows.
This calls for balancing two priorities. In the near term, teams can pursue achievable improvements that demonstrate value and build confidence. In parallel, they need to invest in AI-ready data, workforce capabilities, and workflow design to support longer-term transformation.
4. Make Change Management and Human Judgment Core Leadership Responsibilities
With employees having experienced multiple pilots, proofs of concept, and technology changes, the panelists acknowledged the risk of change fatigue. Introducing another capability is unlikely to create lasting impact unless people understand how it changes their work and why the change matters.
This places new expectations on leaders. Change management needs to become a continuous responsibility, supported by clear communication and ongoing opportunities to explore how emerging capabilities could improve work.
Kate also brought attention to the growing importance of judgment and accountability across different levels of the workforce. Traditionally, these expectations increase as employees move into senior roles. With AI becoming part of everyday work, even those early in their careers will need to develop the ability to evaluate AI-assisted outputs, apply professional judgment, and take responsibility for their decisions.
5. Reimagine Agency Partnerships Around Expertise and Capability Building
Traditionally, agencies have supported pharma organizations through a combination of strategy, creative development, and execution. With AI expected to take on more repeatable and tactical activities, the value partners bring may increasingly come from specialized expertise, fresh perspectives, and the ability to help organizations implement change.
This creates an opportunity to rethink the partnership model. Rather than relying on agencies primarily for individual deliverables, pharma organizations can look to partners to help automate recurring activities, build internal capabilities, and support the adoption of new ways of working.
At the same time, greater specialization introduces a challenge. Working with multiple niche agencies can create fragmented solutions unless the internal organization takes responsibility for coordinating their contributions across the end-to-end process.
6. Measure Success Through Business Outcomes and Employee Value
Traditional measures such as ROI, efficiency, prescription lift, patients treated, and time to treatment remain relevant. However, these measures alone may not fully reflect whether AI is improving how people work.
Adoption is another important indicator, but usage does not necessarily mean employees are experiencing meaningful benefits. The discussion highlighted the value of gathering direct feedback from users to understand whether new capabilities are useful and improve their day-to-day work.
Madelene shared how members of her team, including experienced marketers accustomed to established ways of working, had reported feeling more empowered, learning new skills, and taking greater ownership of their work. Rather than primarily coordinating activities across teams and agencies, they were developing the confidence to approach problems differently and resolve challenges themselves.
Looking Ahead: The Future of Pharma Marketing Will Depend On How Work Evolves
The discussion reinforced that AI offers an opportunity to rethink pharma marketing across customer engagement, workflows, talent, and partnerships. Realizing this potential will require organizations to connect near-term improvements with longer-term changes in how marketing operates.
As AI capabilities become more widely accessible, the ability to apply human judgment, challenge established practices, and continuously improve how work gets done could become an important differentiator.
The opportunity for pharma is to build marketing organizations that are better equipped to serve customers, empower their people, and adapt as technology evolves.
