Rebuilding Pharma R&D for a GenAI-Driven Future

At the Indegene Digital Summit, one message stood out with striking clarity: pharmaceutical R&D is entering a phase where incremental improvement is no longer enough. In a session led by Sanjay Parikh, Executive Director and Executive Vice President at Indegene, the discussion explored how rising scientific complexity, tighter budgets, evolving regulatory expectations, and increasing patient demands are converging at once. At the same time, Generative AI is rapidly maturing from an experimental tool into a serious lever for transformation.

What makes this moment different is not just the pressure, but the timing. Generative AI is moving rapidly from experimentation to enterprise readiness, just as R&D organizations are being asked to deliver faster, safer, and more patient-centric outcomes without the luxury of rebuilding from scratch. The question is no longer whether R&D needs to change, but how fundamentally leaders are willing to rethink structures, processes, and execution models to keep up.

The traditional R&D equation is no longer balanced. Clinical trial volume continues to rise year after year. Safety events are increasing. Clinical trial protocol design is becoming more complex, longer, and harder to execute. Yet budgets remain constrained, and teams are already stretched.

This imbalance has created an uncomfortable reality. Even with additional funding, many existing operating models cannot scale effectively. Sequential workflows, manual handoffs, and document-heavy processes slow progress and introduce risk. The result is delayed trials, extended timelines, and missed opportunities to bring therapies to patients faster.

Leaders are being forced to confront a hard truth: improving speed, quality, and resilience at the same time is no longer possible within the limits of traditional R&D models. It is this breaking point, where complexity outpaces capacity that has pushed many organizations to look toward Generative AI as a potential reset.

Generative AI has emerged as a powerful response to these challenges. Its ability to synthesize information, draft content, and automate repetitive tasks offers clear potential across R&D functions. From medical writing automation to safety case processing, early pilots have shown impressive efficiency gains.

Yet many organizations remain stuck in a familiar pattern. GenAI initiatives often live in isolated pilots. Tools are layered onto existing workflows without changing how work actually flows. Teams test capabilities but struggle to scale impact.

This gap between promise and reality has created what many leaders recognize as pilot fatigue. The issue is not the technology itself, but the absence of deeper process re-engineering.

Until organizations address how work is structured end to end, GenAI remains an accelerator of isolated tasks, not a driver of meaningful R&D transformation. That realization shifts the conversation from what AI can do to how R&D processes must change to unlock its value.

GenAI delivers real value only when embedded into redesigned, end-to-end processes. Bolting AI onto legacy systems limits its impact and often amplifies existing inefficiencies.

True R&D transformation requires rethinking how work moves across the lifecycle, from early research through clinical development, regulatory submission, and post-approval activities. When processes are redesigned with AI in mind, several structural changes become possible:

This shift reframes GenAI from a productivity tool into an orchestration layer, one that connects data, decisions, and documentation across functions.

However, redesigned processes alone are not enough. Once work begins to move differently, organizations quickly encounter a second challenge: the way teams are structured, and decisions are made must evolve as well. That realization brings the focus to people, roles, and collaboration models in an AI-first R&D environment.

Technology change inevitably drives organizational change. Future-ready R&D teams will look different from traditional functional silos. The emerging model centers on small, cross-functional, AI-enabled pods that combine scientific expertise, regulatory knowledge, and intelligent agents.

In these teams, AI supports routing work, pre-populating content, validating outputs, and highlighting decision points. Human experts remain firmly in control, focusing on scientific judgment, risk assessment, and regulatory engagement.

This structure reduces bottlenecks and accelerates collaboration. It also makes R&D roles more sustainable by removing the burden of repetitive, low-value work.

One of the most visible impacts of this model is the shift from linear to parallel execution. Traditional R&D workflows often move step by step: draft, review, revise, approve. Each stage waits for the previous one to finish.

AI-enabled processes change this dynamic. Protocol drafting, data checks, and formatting can happen simultaneously. Feedback loops shorten. Teams reach near-final outputs much earlier in the process.

For areas like clinical trial protocol design, this approach reduces rework and improves consistency—two critical factors for accelerating trial startup and improving patient recruitment.

Behind the scenes, global standards are laying the foundation for this transformation. Frameworks such as ICH M11 and USDM are moving R&D toward structured, machine-readable content across the lifecycle.

These standards enable automation at scale. Content can be reused, validated, and assembled dynamically from protocol through submission. The long-discussed vision of streamlined, low-touch regulatory workflows is becoming technically achievable.

For R&D leaders, aligning transformation efforts with these standards is no longer optional. It is a prerequisite for sustainable automation and long-term efficiency.

Another critical shift is happening beyond organizational boundaries. The traditional build-versus-buy debate is giving way to ecosystem thinking.

Leading organizations are identifying which capabilities define their competitive edge, such as scientific strategy or trial design and which can be orchestrated through external, AI-native partners. Functions like patient recruitment, medical writing automation, and data curation are increasingly delivered through specialized ecosystems.

Success in this model depends less on owning every capability and more on governing integration, quality, and accountability across partners.

As GenAI adoption accelerates, priorities for R&D leadership are becoming clearer:

These actions move organizations closer to meaningful R&D transformation rather than incremental automation.

At its core, the future of R&D is not about tools or platforms. It is about restoring focus on patients. Faster trials, clearer protocols, and more reliable submissions directly translate into earlier access to therapies.

GenAI offers a rare opportunity to realign R&D execution with its original purpose. When applied with discipline and intent, it allows scientific expertise to rise above administrative burden and complexity.

The organizations that succeed will be those that rebuild while moving forward, using technology not as a shortcut, but as a catalyst for smarter, more humane R&D.