AI is creating new opportunities across pharma, from improving scientific decisions to accelerating commercialization. Yet, the real challenge is translating these capabilities into meaningful business outcomes, particularly as organizations work to connect insights, decisions, and execution across functions.
At the Indegene Digital Summit 2026, Janice Griffiths, Executive Director, Commercial Strategy, Insights & Analytics at Regeneron, Somy Silvi, Sr. Director, Data Strategy and Products at Johnson & Johnson Innovative Medicine, and Steve Winawer, VP, Head of U.S. Commercial Operations Excellence at Sun Pharma explored what will distinguish leaders from followers when it comes to AI in pharma.
Their discussion highlighted the organizational, operational, and strategic changes needed to turn AI investments into sustainable value across R&D, commercial operations, and patient access.
The Challenge: Why AI Capabilities Alone Will Not Create Competitive Advantage
Scientific innovation has always been central to pharma’s success. But developing differentiated therapies is only part of the equation. Organizations must also make timely decisions, translate scientific insights into effective commercial strategies, and address the barriers that prevent therapies from reaching patients.
AI offers opportunities to improve each of these areas. However, disconnected data, established operating models, and difficulties scaling beyond initial pilots can limit its impact. Even promising AI solutions may struggle when they encounter the realities of enterprise-wide adoption, governance, and complex workflows.
Six key priorities emerged for pharma leaders looking to close this gap from this panel discussion.
1. Use AI to Strengthen Scientific Decisions Earlier
The first opportunity begins with science itself.
AI-enabled scientific tools, including AI co-scientists, offer new ways to improve understanding of biological mechanisms, identify promising targets, and evaluate drug candidates. This is particularly important in drug development, where advancing a candidate without sufficient understanding of its underlying biology can create significant downstream costs.
Somy emphasized the importance of using AI to make better decisions earlier, including identifying candidates with limited potential before further resources are committed. Steve reinforced that scientific differentiation remains fundamental, with AI serving as an enabler of faster and more effective decisions.
This changes how leaders should think about AI investments in R&D. Beyond accelerating individual activities, the opportunity lies in improving the quality and timing of scientific decisions.
2. Connect Scientific Insights to Commercial Strategy and Execution
Organizations must also understand how to position their products, identify emerging market signals, and translate insights into coordinated execution. Yet valuable information often sits across medical teams, commercial analytics, competitive intelligence, and other functions.
A more connected approach brings these sources together with insights from HCP and patient journeys. AI can help organizations detect relevant signals, shape commercial strategies, and connect those decisions to field engagement and digital marketing activities.
Janice highlighted the importance of creating a dynamic insights ecosystem that continuously feeds information back into strategy and execution. This allows teams to adapt their approach as new information becomes available, rather than relying solely on decisions made during initial planning.
The implication is significant. Even highly targeted execution may fall short if the underlying strategy or product positioning is not right.
3. Address the Organizational Barriers That Prevent AI From Scaling
One of the biggest gaps appears when successful pilots move into broader implementation. Early initiatives typically benefit from motivated sponsors, dedicated teams, and strong internal advocates. The wider organization may have different levels of AI literacy, readiness, and willingness to change established workflows.
The discussion identified three areas requiring particular attention.
First, organizations need to prepare employees for new ways of working. AI adoption involves more than training people to use tools; it requires helping them understand how their responsibilities and workflows will evolve.
Second, governance must remain both responsive and rigorous. Appropriate policies, oversight, and visibility into AI models are necessary to build trust, even when these requirements appear to slow implementation.
Third, organizations need a shared understanding of their data. Somy highlighted how information can carry different meanings across discovery, CMC, and commercial functions.
4. Replace Rigid Commercial Planning with More Agile Execution
Traditional brand planning, forecasting, and field strategies often follow established planning cycles. However, market conditions and customer signals can evolve throughout the year, creating a need for more frequent evaluation and adjustment.
Steve challenged organizations to rethink the commercial journey through the lens of agile working, drawing parallels with practices already used in software development and consumer goods.
AI can help connect data, measurement, and execution across commercial workflows. Instead of treating the brand plan as a fixed annual exercise, teams could evaluate performance continuously, learn from emerging signals, and adjust strategies through shorter planning cycles.
This does not mean every brand or commercial activity needs to adopt the same approach. The discussion emphasized selecting suitable areas to introduce agile practices, learning from the experience, and extending the approach where it delivers value.
5. Focus AI Investments on Outcomes That Matter Beyond Productivity
The panelists highlighted opportunities where AI could influence more consequential business outcomes, including clinical development speed and patient access.
In R&D, AI-assisted clinical trial design offers the potential to evaluate study parameters more effectively. By using relevant data to assess enrollment expectations, timelines, and endpoints, teams may be able to make better-informed trial design decisions and identify opportunities to accelerate development.
Patient access presents another important opportunity. Even when a therapy demonstrates strong scientific value, reimbursement and access-related processes can create barriers to treatment. Steve drew attention to the administrative burden faced by healthcare providers, including payment verification and the flow of access information.
Rather than simply developing better systems to track where patients encounter delays, AI could help improve the workflows that contribute to those delays.
These examples highlight why investment priorities should be tied to specific business problems and the outcomes organizations are trying to improve.
6. Start With a High-Value Opportunity and Build Toward Scale
The discussion highlighted that there is no single starting point for every organization. Where data is fragmented, inconsistent, or not ready for analytics, foundational investment may need to come first.
Janice shared how limitations in data readiness had made earlier proofs of concept difficult to scale, leading her to prioritize the core data foundation before expanding implementation. At the same time, organizations do not necessarily need to build capabilities across the entire enterprise before demonstrating value.
Steve advocated starting with one promising area, working with an engaged business team, and using early successes to build organizational confidence and capability.
Somy reinforced the importance of identifying transformational business use cases first and then determining which data domains and capabilities are essential to support them.
Together, these perspectives point toward a practical approach: define the business outcome, assess the readiness of the required data and workflows, invest in the necessary foundations, and begin with a focused implementation that can demonstrate measurable value.
Looking Ahead: Competitive Advantage Will Depend on How Effectively Pharma Connects Science to Action
AI’s greatest potential lies in enabling better scientific decisions, accelerating the path from insight to action, making commercial strategies more responsive, and addressing barriers that prevent therapies from reaching patients.
Realizing this potential will require pharma leaders to connect trusted data, business priorities, and redesigned workflows, with a clear focus on measurable outcomes. The real opportunity is to turn scientific innovation into sustained business value while improving how therapies are developed, commercialized, and made accessible to patients.
