Building Future-Ready Commercial Models: The Shift to Precision Marketing and Agentic AI in Pharma

Pharma commercialization is undergoing a fundamental reckoning. At the Indegene Digital Summit 2025, Marion Dumas, Global Head of Omnichannel Transformation at Sanofi, outlined the architectural shifts required to move from mass promotion to hyper-personalized engagement, framing it not as a digital upgrade but as a complete redesign of pharma commercial strategy that positions the industry as a health partner rather than simply a drug maker.

Pharma must abandon the belief that sales representatives are the sole owners of customer intimacy and engagement. The future of pharma commercial strategy requires AI-powered orchestration that positions marketing back in the driver's seat, creating a true customer 360 view where the rep becomes one channel among many, both personal and non-personal. This shift fundamentally redefines pharma commercialization from a single-threaded, transactional model to a multi-channel ecosystem where each touchpoint is optimized for relevance and impact based on comprehensive customer intelligence.

Traditional next-best-action models are being replaced by agentic AI in pharma that triggers content automatically in response to real-time physician behavior, such as website visits or search queries. This represents a combination of extractive AI, large language models, and generative AI working together to produce content that resonates with a physician's most recent search, keyword, or question. For pharma leaders, this means moving from static campaign calendars to dynamic, on-the-fly engagement strategies that respond to intent signals when they occur, fundamentally transforming how precision marketing operates in the industry.

The future content model must move beyond traditional promotional messaging and shift to on-demand, scientifically driven information that addresses care gaps and treatment pathways. Building this requires content libraries, editorial strategies, and modular message templates that can be activated across multiple personas and channels, particularly in launch settings where precision marketing focuses on education regarding new therapeutic classes rather than promoting specific drugs. This demands that marketers evolve from agency managers into content editorial strategists and prompt engineers who can ensure tone, structure, and brand identity are embedded at scale.

Pharma commercial strategy must abandon the static annual exercise of defining priority segments and embrace dynamic retargeting that identifies prescribers with high-potential patient pools on the fly. Technology now enables real-time identification of territories and physicians where uptake can be accelerated, eligible patients can be flagged, and switching opportunities can be unlocked without waiting for the next planning cycle. This shift transforms targeting from a set-and-forget planning exercise into a continuous optimization process that reallocates resources toward the highest-value opportunities as they emerge in the market.

The traditional B2B prescriber model must expand to include a broader ecosystem of influencers, decision-makers, KOLs, payers, and patients. Successful pharma commercialization now requires facilitating peer-to-peer networking and connections between stakeholders, positioning the industry as a health partner rather than simply a drug maker. This requires mapping influence pathways across the care continuum and identifying moments that matter in treatment decisions, reimbursement processes, and clinical practice to unlock systemic barriers rather than push individual products.

Different lifecycle stages demand different pharma commercial strategy approaches: pre-launch and launch require bold, broad strategies emphasizing scientific evidence and care gap identification, while inline brands need optimized touchpoints and differentiation, and legacy portfolios must minimize cost-to-serve.

The winners in pharma commercialization will be those who can free up cash flow from mature brands and reinvest it into launch models, building enterprise scalability and sustainability rather than treating every brand with the same resource intensity. This financial discipline transforms agility from rhetoric into practice, ensuring that AI investments in precision marketing and dynamic retargeting drive measurable ROI improvements across the portfolio.

Success requires rethinking what sits at the global versus local level to avoid multiplying regulatory reviews and marketer headcount by 10x in pursuit of personalization. Centralization, automation, and AI should handle content validation and channel orchestration, freeing local teams to focus on customer intelligence and insight collection that informs the next best action and enriches the customer 360 view. For transformation leaders, this means investing 70% of effort in revisiting roles, processes, and mindsets, 20% in data infrastructure, and only 10% in algorithms, because most companies fail by chasing shiny AI objects without rebuilding the operating foundation beneath them.

This session reinforced that AI transformation in pharma commercialization is organizational redesign, not a digital upgrade, requiring leaders to rethink roles, reallocate resources, and rebuild commercial DNA for an era where precision marketing, agentic AI in pharma, and dynamic retargeting replace volume-based approaches.