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Over the last three years, artificial intelligence (AI) has become a major trend and area of focus in pharmaceutical research and development, with machine learning expected to deliver greater insights and understanding of diseases and patients.
However, AI is not just a technological step-change; it has the potential to radically transform core drug discovery and development models—and power the journey toward intelligent therapy solutions. This perspective was confirmed when I recently talked about “revolutionizing clinical development” during this year’s Clinical Innovation Partnership conference in Zurich, Switzerland. The vast majority of clinical operations leaders there understood the change AI can unleash in the way we design and execute clinical research. It is now about demonstrating that value and powering ahead!
Historically, pharma R&D has grappled with three fundamental questions:
How can AI help to answer them? Clearly, AI can help pharma companies win the productivity race by increasing speed and cost efficiency while maintaining high levels of compliance and reducing “human error.” But—more importantly—it can help drive companies toward a more personalized and outcome-driven value proposition.
Industry executives already see AI as essential to a successful R&D operating model both today and in the future. In a recent article I wrote for the Harvard Business Review about “Intelligence-Driven Therapy Solutions,” Badhri Srinivasan, Head of Global Development Operations at Novartis, contributed his thoughts. He mentioned that “…60 percent of all the development cost resides in the design and execution of clinical trials where implementation of AI and machine learning brings a considerable opportunity on productivity and cost to saving….” The Accenture survey, “AI: the momentum mindset” also stated that 51 percent of respondents identified productivity as a benefit of deploying AI within their organization; in addition, they pointed to AI’s value in improving patient and clinical outcomes.
To seize the truly transformative power of AI, R&D leaders need to see the bigger picture. Taking a view that’s too narrow and focuses on simply optimizing R&D processes might result in missed opportunities for competitive advantage.
Across the industry, we see three stages of AI awareness and adoption in pharma R&D organizations:
To prepare for this transformation, we recommend companies focus on three main pillars:
The transformation from accelerated productivity, to compound, to therapy, to living services is one that will require years to reach maturity, which is why I recommend organizations get a head start now. As one of my clients recently said, “You want to have your surf board in the water when the wave is coming.” I deeply believe that R&D departments of innovative pharma companies are uniquely positioned pioneer this broader enterprise transformation. The time to act is now.