Making Technology Deliver Real Outcomes in Life Sciences

Across the industry, companies are investing heavily in data, digital tools, and life sciences technology. Yet many leaders admit that even when pilots succeed, the overall business impact often falls short. Why do so many initiatives work in isolation but fail to scale?

At the Indegene Digital Summit 2025, Ryan Steinberger (Pfizer) and Suneet Varma (formerly at Pfizer), shared what they’ve learned from leading major digital transformations. They discussed how focus, governance, and people-centric change can turn technology into real business and patient value.

This recap highlights five key takeaways for life sciences leaders working to make technology deliver measurable outcomes.

In recent years, generative AI in life sciences has moved from experimentation to enterprise-wide pilots – with many pilots often succeeding. Yet, only few have manged to deliver sustained long-term value. The challenge, as Steinberger reflected, lies in focus. Too often, teams prove that technology works without asking whether it matters.

“When generative AI first took off, we delivered twenty-two use cases in six months, and all of them worked,” he recalled. “But we ended up with a graveyard of successes. The real question is: what do you scale?”

That experience reshaped his team’s approach to innovation. The question shifted from “can we do this” to “should we do this”—choosing fewer, better-aligned projects with measurable business outcomes. The goal became proving value, not feasibility.

For leaders, this is a reminder that transformation is a resource game. The discipline to focus, prioritize, and prune is what separates energy from impact.

Technology delivers value only when it serves a clear business or patient ambition. Too often, organizations treat “digital strategy” or “AI strategy” as standalone goals rather than extensions of their commercial and patient strategies. The leaders in this discussion agreed that the real question is not what’s your AI strategy, but how does AI advance your business strategy.

As Varma reflected, business development isn’t the strategy - it’s an enabler to achieve it. The same holds true for technology. When life sciences technology investments begin with clear business outcomes—whether improving access, accelerating development, or deepening customer engagement —they become part of the operating model instead of a parallel track.

For life-sciences leaders, the shift is from building technology plans to defining value hypotheses. Start every digital discussion with what problem are we solving, and for whom? That discipline keeps organizations focused on purpose, not platforms, and ensures every investment moves the enterprise closer to meaningful outcomes.

The panelists agreed that progress in large organizations often stalls not because of technology, but because of the structures that surround it. Too many teams spend more energy navigating internal processes than driving external impact.

Slow governance, complex handoffs, and diffused accountability can dilute even the best innovations. These layers of process and slow decision-making are often the biggest barriers to AI adoption—not the technology itself.

Simplifying decision paths and empowering teams to act quickly unlock far more value than adding another platform or tool.

Leaders can start by asking one revealing question: how long does it take a good idea to turn into an approved action? The answer often exposes where modernization truly needs to begin.

Varma shared how testing end-to-end digital transformations within a single market can accelerate learning and reduce risk. His team piloted major operational changes in one country, treating it as a “live laboratory” for scale. “We tested in Australia first,” he said. “It was small globally but big locally—a safe way to work out the kinks before expanding worldwide.”

This approach bridges the gap between pilots and enterprise adoption. Testing transformation in a contained but complete market reveals the friction points of scale (data readiness, local compliance, cross-functional coordination) before they multiply.

For global organizations, country labs create a model of controlled scale: proving not just whether something works, but whether it can endure.

Steinberger shared that one of the biggest accelerators of progress was assigning a single accountable leader—the “pilot in command”—who could make decisions across legal, medical, commercial, and technology functions.

That model ensured every initiative had clear ownership and a direct line of accountability. With executive sponsorship behind it, the structure removed hierarchy bottlenecks and sped up decision-making.

This approach reframes how transformation should be managed. Instead of coordinating across multiple stakeholders, empower one cross-functional leader who owns the mission and has the mandate to deliver. It’s a simple shift that often turns stalled projects into momentum.

The panelists agreed that real transformation depends less on technology and more on how leaders design for adaptability and trust. Too often, organizations make a plan and treat it as fixed, when progress depends on learning as you go. As Varma noted, “In pharma, we make a plan and treat it like it’s written in concrete. A plan should be written in jello, so you can adapt and course-correct as you go.”

Yet flexibility alone is not enough. The human side of change matters just as much. Steinberger emphasized that people embrace transformation when they can see their place in the future it creates. The most successful leaders bring teams into the process early, letting them experience how new ways of working make their roles stronger, not smaller. That clarity and inclusion also help overcome the cultural barriers to AI adoption that often derail transformation programs.

Overall, the discussion underscored a truth often forgotten in life sciences technology programs: progress doesn’t come from technology or process alone. It comes from organizations that move with flexibility, lead with clarity, and build confidence in the people carrying the change forward.