Speed, Safety, and Trust: Rethinking Pharma Innovation

What happens when the pressure to move faster collides with the responsibility to never get it wrong? At a time when artificial intelligence in pharma and biotech is accelerating decisions, reshaping data interpretation, and compressing timelines, life sciences leaders are facing a new kind of tension - speed without shortcuts, innovation without erosion of trust.

This question took center stage at the Indegene Digital Summit 2025 Virtual Edition, in a keynote conversation featuring Dr. Aida Habtezion, former Chief Medical Officer at Pfizer and Adjunct Professor at Stanford, and Robert V. Brown, SVP, Digital at Pfizer, moderated by Sameer Lal, SVP, Enterprise Medical at Indegene. Their discussion went beyond technology trends to examine how science, digital platforms, regulatory rigor, and human judgment must work together if faster innovation is to deliver value, not risk.

The pandemic permanently changed how pharma thinks about innovation. Timelines that once stretched across years were compressed into months. Decision-making structures became flatter. Teams were empowered to act with urgency.

What emerged was not reckless acceleration, but purpose-driven agility. Science-led organizations leaned on strong fundamentals: deep domain expertise, clear governance, and a shared sense of responsibility toward patients. Technology investments that once felt optional suddenly became mission-critical.

Just as critical were the human dynamics. Clear communication, active listening, and tight alignment across clinical, regulatory, safety, and digital teams became non-negotiable. Innovation moved faster not because guardrails disappeared, but because trust inside organizations allowed decisions to be made with confidence.

That trust set the stage for a deeper shift. Once speed was proven possible, the question became how to sustain it - at scale, across functions, and without burning out teams. The answer would not come from process changes alone. It would require technology to step out of the background and into the front line of pharma operations.

This shift continues to influence how pharma innovation is structured today.

Technology is no longer a back-office function in pharma. It has become a frontline enabler of scientific exchange, regulatory execution, and operational scale.

Cloud platforms, advanced analytics, and AI-driven workflows are now embedded directly into R&D, regulatory submissions, and pharmacovigilance processes. Business teams increasingly co-create digital solutions rather than waiting for them. This shift has driven a sharp increase in AI fluency across functions.

Rapid prototyping cycles that once took years are now measured in weeks. Agentic AI in pharma is beginning to support tasks such as document preparation, data harmonization, and workflow orchestration—while still operating within defined guardrails.

The result is faster experimentation, quicker feedback loops, and more informed decision-making across the product lifecycle.

In a highly regulated industry, speed has no value if safety is compromised. Innovation must always serve patient protection and scientific integrity.

Regulatory frameworks already offer pathways to balance urgency with rigor. Risk-based approaches, adaptive trial designs, and expedited regulatory programs allow innovation to move forward responsibly. However, these approaches depend on one critical factor: data quality.

Pharma regulatory AI and advanced analytics are only as reliable as the data that feeds them. Clean, traceable, and well-governed data enables AI systems to scale without losing compliance. Cloud-native architectures support transparency, version control, and audit readiness, essential requirements for global submissions and safety monitoring.

Rather than slowing innovation, strong data foundations make responsible acceleration possible.

As AI becomes more embedded in decision-making, trust becomes harder and more important to earn. Black-box models, unclear training data, and unexplained outputs create hesitation among regulators and internal stakeholders alike.

Human-in-the-loop governance is emerging as a non-negotiable principle. AI systems must be designed with explainability, validation, and continuous monitoring from the start. Transparency around how models behave, where data comes from, and how outputs are reviewed builds confidence across regulatory and quality teams.

Quality-by-design principles are no longer limited to manufacturing. They are increasingly applied to digital and AI systems. When transparency and oversight are built in early, trust follows naturally.

One of the most valuable aspects of the session was the contrast in perspectives that ultimately converged on a shared conclusion.

A clinical and scientific lens reinforced the importance of keeping patients and communities at the center of innovation. Ethical considerations, real-world performance monitoring, and context-specific validation were emphasized as essential safeguards.

A digital transformation lens highlighted the need for simplicity, scale, and AI fluency across global teams. Cloud-native platforms, standardized workflows, and interoperable systems were positioned as enablers of both speed and compliance.

Together, these perspectives underscored a powerful truth: technology and science are not competing forces. When aligned, they strengthen each other.

Innovation at the speed of trust is not a slogan. It is an operating model.

For R&D, Regulatory, PV, Medical Affairs, and transformation leaders, several implications stand out:

As AI capabilities expand and agentic AI in pharma becomes more prevalent, organizations that lead will be those that integrate innovation with responsibility, clarity, and purpose.