This episode of ByteSight features Dr. Grant Coren, Managing Director of PharmaSearch Limited, with 30+ years of global executive search experience across the pharma and life sciences value chain. Trained as a molecular biologist and geneticist (oncology), Grant brings a grounded perspective on where AI is already shifting drug development, and where human judgment, empathy, and governance still matter most.
In this conversation, Manasi and Grant discuss the reality behind the AI hype: why people define AI in wildly different ways, why pharma can simultaneously invest billions and resist change, and how the next breakthroughs depend on what we choose to measure, share, and include. They explore not only what AI can do, but what it should do, and for whom.
About Dr. Grant Coren
Dr. Grant Coren is a molecular biologist by training and a long-standing leader in executive search within the pharma, biotech, and life science sectors. Over a 30+ year international career spanning Europe and the USA, he has built global networks across the entire pharma lifecycle, led diverse teams, and partnered with organizations from research through development. Entrepreneurial and delivery-focused, Grant is passionate about creating high-quality, creative solutions and adding meaningful value to candidates, clients, and the wider life sciences ecosystem.
AI’s Promise Depends on Data Diversity
AI is often positioned as the ultimate accelerator of drug discovery and personalized medicine. But its effectiveness is entirely dependent on the data it learns from. Clinical trials and genomic datasets still reflect limited populations, raising a critical concern: without representative input, outcomes will remain unequal.
As Grant puts it:
If you’re not putting diverse data in, you’re not going to get diverse outcomes.
And the more… the more that we can collect data from different subpopulations… and the more we can analyze and understand that… the more we can understand what will and won’t work with different groups of people.
This isn’t just a science issue, but it’s also a business and trust issue. Better diversity means better signal detection, more accurate stratification, and fewer costly failures. It also means treatments that are more likely to work across real-world populations, not only those represented in traditional trials.
The discussion highlights a post-COVID reality: regulators are demanding more diversity, but organizations may still be doing “the minimum required.” The opportunity now is to use digital workflows and AI-enabled screening to expand inclusion without exploding cost, time, or risk.
Recruitment in Life Sciences: Where AI Helps and Where It Harms
AI isn’t only transforming drug discovery; it is already reshaping hiring.
Grant highlights a difficult reality: automated screening can be “brutal,” and can unintentionally reinforce bias. One example discussed is age bias, where candidates who are clearly qualified may be rejected early due to proxies that correlate with age.
At the same time, candidates are responding by optimizing CVs and applications for machines, often using AI tools to “match” what they think the algorithm wants. The unintended consequence is a flood of generic applications that remove nuance, context, and individuality.
For life science companies, this creates a paradox: AI is meant to improve efficiency and talent matching, but it can also reduce diversity of thought by selecting for the most “machine-readable” profiles rather than the strongest real-world contributors.
The most important conclusion from the conversation is not “AI vs humans,” but “AI with humans.”
Grant’s point is direct:
I think where we need to get to is utilizing the technology, which is really super smart… but utilizing it in conjunction with human beings… these things need to be in tandem, not either-or… We seem to be in a bit of an either-or place now…
The message carries across both themes:
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In drug development, AI can accelerate discovery, but humans provide clinical judgment, ethical framing, and patient-centered decision-making.
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In recruitment, AI can speed up screening, but humans are needed to interpret nuance, recognize potential, and ensure fair evaluation.
In short: AI becomes transformative only when paired with empathy, expertise, and governance.
Final Takeaway
Equity in healthcare isn’t optional and neither is the responsible use of AI.
The “big shiny thing” only matters if it reaches beyond the subset of people already included in datasets, trials, and hiring pipelines. The future will belong to organizations that embrace AI and commit to diversity, transparency, and human-centered decision making.
