Redesigning Engagement and Operating Models for the AI Era

Pharma’s century-old commercial model, optimized for stability and compliance, is facing an existential reckoning in the age of intelligent automation. The industry’s siloed structures are fundamentally at odds with AI’s potential to create seamless, data-driven engagement.

At the Indegene Digital Summit 2025, a keynote discussion featuring Juanjo Francesch, Chief Information Officer at Merck Animal Health, and Jared Josleyn, SVP, Global Head of Digital Health and Emerging Disruptive Growth Exploration (E.D.G.E) at Sanofi, challenged leaders to move beyond operational efficiency and begin a structural redesign of their engagement and operating models. The discussion made it clear that what’s at stake is not just competitive advantage but relevance in a rapidly changing healthcare ecosystem defined by AI in patient engagement and the consumerization of healthcare.

The traditional pharma commercial model has long been defined by disconnected teams in marketing, sales, and medical affairs. This fragmentation is a direct barrier to AI, which thrives on continuous, integrated data flows. Juanjo emphasized the need to replace these structures with multifunctional product squads aligned around measurable business and patient outcomes.

As he noted, success should no longer be measured by whether a system goes live, but by whether it drives ROI for marketing campaigns and engagement metrics. AI is most powerful when commercial, digital, and medical teams share accountability for results.

Why it matters: AI cannot transform a siloed organization. Breaking down barriers between functions is the first step toward a truly intelligent engagement model.

Reorganizing teams is not enough. Both leaders underscored that AI transformation demands a mindset shift, from risk avoidance to learning through experimentation. Pharma’s legacy systems were built for compliance and predictability, not for innovation.

Joselyn described how progress depends on a culture that celebrates iteration and even failure. “When was the last time your team sat around and celebrated a failure?” he asked. Learning organizations embrace failure as evidence of forward motion. Without that openness, AI will only automate old habits instead of enabling new ways of working.

Why it matters: Building an adaptive culture is central to how pharma can evolve from stability to continuous learning and improvement.

As AI scales decision-making and engagement, the role of the human-in-the-loop becomes more essential. Juanjo pointed out that AI delivers speed, scale, and consistency, but not trust. Trust still comes from humans, whether it’s the medical expert validating content or the field professional maintaining relationships.

Jared offered an example from Sanofi’s Taiwan operations, where sales representatives were measured not on prescriptions but on physician satisfaction scores. Their job was to help doctors find answers, even if it meant pointing to a competitor’s content. The result was stronger trust and higher engagement, achieved with a smaller field team.

This kind of model shows how to improve patient engagement in healthcare by blending automation with empathy and accountability.

Why it matters: AI should enhance, not replace, human connection. The future of engagement depends on reimagining human roles to complement intelligent systems.

Both speakers agreed that AI alone will not be a sustainable differentiator. Jared described most corporate efforts as “commodity AI”, i.e. tools focused on operational efficiency rather than transformation. The real advantage will come from how organizations use AI to combine unique internal and commercial datasets into proprietary insights.

Juanjo reinforced that the differentiator lies in culture and data ownership. Access to AI models is universal, but the ability to harness internal data that no competitor can replicate remains rare.

Why it matters: Competitive advantage in pharma’s AI era will depend on owning and integrating data that is unique, well-structured, and connected to business value.

With AI influencing customer engagement, content creation, and decision-making, accountability has never been more important. Jared argued that brand and marketing leaders will need to develop technical literacy to understand how models are trained, validated, and monitored. Humans must remain responsible for defining the “gold standard” for AI outcomes. Juanjo added that algorithms will never be accountable, and leaders must define clear oversight frameworks.

Why it matters: Governance and accountability are critical to trust. The companies that formalize human oversight in their pharma commercial model will gain the confidence to innovate faster and more safely.

The consumerization of healthcare is upending how patients engage with pharma and providers. Jared highlighted a striking fact: the average person spends only 84 minutes per year interacting with the clinical system. For a 12 trillion-dollar industry, that equates to a 0.01 percent engagement rate.

This engagement gap represents both a challenge and an opportunity. As consumers expect healthcare experiences that mirror retail and banking, AI can enable personalized, always-on interactions. From virtual health assistants to predictive outreach, AI in patient engagement allows pharma to stay present beyond clinical touchpoints.

Why it matters: Patient expectations are redefining how value is created. Continuous engagement is becoming central to the pharma commercial model, not peripheral to it.

The panelists converged on one central theme: the next era of pharma will be defined by patient-centric system design. AI enables the industry to look beyond products and toward the total experience of care. This includes new patient engagement ideas that connect caregivers, community health workers, and pharmacists into a unified support system.

Juanjo emphasized that the primary user of any healthcare system should be the patient, not the infrastructure that serves them. Jared added that the future belongs to those who design for people, not processes.

Why it matters: AI provides the architecture for a redefined, patient-first model that aligns every stakeholder around better outcomes.

Collectively, the session reinforced that AI transformation is not a digital upgrade. It is a complete organizational redesign that requires dismantling silos, fostering learning, redefining accountability, and rebuilding trust through patient-centered systems.

As AI in patient engagement and the consumerization of healthcare reshape the industry, pharma’s success will hinge on its ability to move from compliance-driven operations to intelligence-driven ecosystems. The leaders who thrive will be those who pair technology with cultural courage, using AI not just to improve efficiency but to redefine how healthcare works for people.