Mature brands are often managed around efficiency and revenue preservation while investment shifts toward newer assets. At the Indegene Digital Summit 2026, Shaan C. Gandhi, VP and Head of Partnerships for PAVE at Pfizer, presented a different role for the mature portfolio: one that combines continued commercial value with a practical environment for testing new models, technologies, and ways of working. The discussion reframed pharma innovation as something that does not have to begin with a launch brand or a high-risk transformation program.
Rethink Pharma Lifecycle Management Beyond The Harvest Model
Pfizer's PAVE organization brings together established and mature brands across therapeutic areas and patient populations. Several of these products still have meaningful promotional opportunities, including brands that are already off patent. That diversity matters because a mature oncology brand in a competitive market has very different needs from a product serving a smaller, highly identifiable patient population.
That creates a broader view of pharma lifecycle management. Loss of exclusivity does not automatically have to signal the end of commercial innovation. In some markets, the opportunity may be to sustain evidence-led engagement and protect relevance. In others, the challenge may be making access and product use easier for a concentrated group of clinicians and patients. The commercial model still needs to reflect the market around the brand rather than applying a single harvest playbook across the portfolio.
Use Stability to Learn What Actually Works
A steady-state brand can sometimes create a cleaner environment for experimentation. Because fewer variables are changing simultaneously, teams can test individual interventions and better understand whether a particular promotional angle, content strategy, segmentation approach, or targeting decision affected performance. This makes the mature portfolio useful not because it is simple, but because it can make cause and effect easier to see.
In a rapidly growing inline brand, several forces may be driving results at once, making it harder to isolate what created the lift. By contrast, a more stable baseline can make controlled testing more practical. That gives mature brands a different form of strategic value: they can serve as a test-and-learn environment where commercial innovation is evaluated with greater confidence before being scaled to other parts of the portfolio.
Turn Commercial Innovation Into Enterprise Learning
The portfolio was described as an "amazing kind of laboratory" for testing new models that could ultimately benefit the wider organization. The value of that experimentation increases when teams can explain why an approach succeeded, where it may apply elsewhere, and how another brand could adopt it without starting from zero.
That is where commercial innovation can begin to support broader commercial excellence. Mature brands contribute not only through their own performance, but also by generating reusable evidence about content, targeting, segmentation, technology, and ways of working. A successful experiment becomes more valuable when it can be translated into a repeatable playbook rather than remaining a one-off case study.
Pair Evidence With Executive Sponsorship
Strong results alone do not guarantee that an experiment will scale. The discussion highlighted the importance of combining bottom-up evidence with senior leadership support. Teams need enough data to show that an intervention produced a meaningful result, but they also need a route into the broader organization so the learning can influence other brands and functions.
For transformation leaders, the distinction matters. Experimentation creates proof, while sponsorship creates the conditions for that proof to become a repeatable enterprise capability. Commercial excellence therefore depends not only on finding winning ideas, but also on having governance, sponsorship, and shared language that allow those ideas to travel across the portfolio.
Use AI to Learn Faster
When the discussion turned to AI, the focus was not on claiming that the return-on-investment question has been solved. Instead, the clearest near-term benefit was cycle-time reduction. AI can shorten analysis, reduce unnecessary handoffs, and allow teams to work through routine questions without drawing in multiple colleagues at every step.
That faster analysis has a second benefit: it can expand the number of ideas a team is able to explore. Concepts that do not hold up can be ruled out earlier, before substantial time and resources are committed. For mature brands, this combination of lower-risk experimentation and faster learning can make AI particularly useful as an enabler of disciplined commercial innovation.
Taken together, these ideas suggest a different role for mature brands in pharma innovation. Rather than treating them only as assets to manage efficiently, organizations can use them to test new approaches, generate evidence, and build commercial capabilities that strengthen the wider portfolio.