One of the more common narratives surrounding AI is the idea that automation will gradually reduce the importance of scientists and technical experts. Personally, I believe the opposite is true. As AI becomes more capable, the value of scientific expertise increases.
AI can process vast amounts of information, identify patterns, and accelerate workflows at remarkable speed, but it doesn’t understand why any of that matters. Experienced researchers do. The future of drug development should not be viewed as AI replacing scientists. It should be viewed as scientists becoming more empowered through AI-enabled tools and technologies, using AI as a co-scientist to support better decisions.
As scientists, we must do what we’ve always done—build the future instead of letting it happen to us. We must harness AI and build processes that leverage its strengths and constrain its limitations. We must evolve our understanding of our own roles and how our human intelligence, curiosity, and creativity contribute to the research and development of new treatments.
(If you’re not sure where to begin, my colleague Priyata Kalra wrote a post that I highly recommend.)
Scientific progress has never simply been about generating outputs. It is about asking the right questions, understanding biological relevance, challenging assumptions, and interpreting results within the broader context of human health and disease. These are fundamentally human capabilities. AI may support decision-making, but scientists determine whether those decisions make sense. They understand the limitations of datasets, the complexity of biological systems, and the nuances that sit behind every model or simulation.
The rise of AI makes scientific leadership more important than ever, because the volume and speed of information generation are increasing dramatically.
Without experienced scientists guiding that process, there is a real risk of overconfidence in AI outputs that may not fully reflect biological or clinical reality. The industry must be careful not to rush towards a future of fully AI-developed drugs before the science, regulatory confidence, and real-world evidence are truly there to support it.
I was recently asked whether I thought AI would replace clinical trials over the next 10 years. My immediate response was simple: would you personally take a drug fully designed by AI? Without human insight and oversight guiding the process, I doubt many of us would say yes.
The organizations that achieve the greatest success with AI over the next few years will be those that keep scientists firmly at the center of innovation. Technology alone is never enough in this industry. What drives meaningful progress is the combination of scientific expertise, domain knowledge, regulatory understanding, and advanced computational capability working together.
As scientists, we should not feel threatened by AI. It has the potential to remove inefficiencies, accelerate insight, and support more informed decision-making. But the direction, interpretation, and responsibility must still come from people with deep scientific understanding.
As companies race to integrate AI into their workflows and build increasingly data-driven platforms, the organizations that create the most value will not simply be the ones adopting AI the fastest. They will be the ones ensuring their scientists are leading how it is applied, interpreted, and trusted.
If you’re interested in learning how your organization can leverage a combined AI and MIDD approach, schedule a call or join us for our upcoming webinar, AI in MIDD: From Hype to Practical Application.