The failure of the ‘usual suspects’ approach to life science recruitment

One of the most successful films from the 1990s was the Usual Suspects, a crime thriller that introduced the world to Keyser Söze, one of cinema’s most iconic characters. Both crime lord Söze and the inspiration for the film – a line from the police chief played by Claude Rains in the film noir classic Casablanca – have become synonymous with the futility of unquestioning reliance on blind orthodoxy. For anyone facing an intractable challenge, rounding up the usual suspects is now shorthand for being seen to do something which everyone accepts will inevitably fail. I was reminded of this at a recent meeting with a potential unicorn – a billion-dollar AI diagnostics business under pressure from investors to make strategic hires to ease the business’s path to a main market listing. An interim team was running the place until the nominations committee – which included the former and current chief executives of a couple of high-profile life science companies – found a suitable replacement. In the meantime, investors watched nervously from the sidelines in the manner of those who have bet the farm on a pair of sevens. What made the meeting remarkable was not the size of the prize, but what it revealed about the failure of the ‘usual suspects’ approach to life science recruitment in the AI age. All the global executive search giants which currently dominate the C-suite recruitment landscape had failed to convince the investors that they could find suitable candidates. On review, their proposals showed that they were struggling to understand the scale – or even the nature – of the challenge. That’s why they ended up in a room with me, a small European consultancy with specialist knowledge of the AI sector and 25 years of dirt under my fingernails of placing experts in medical technology. The proposals I was up against offered the ability to find well thought of pharmaceutical and biochemistry generalists. This was fine, except the company needed actual AI diagnostics specialists. The usual suspects making the pitch were looking to shoehorn in candidates they already knew as generic solutions and failed to show that they had listened to what the client needed or any empirical knowledge of the AI clinical diagnostics sector. The narrow pool This failure is not entirely down to those well-known recruitment companies. The number of people, globally, who can run a billion-dollar AI clinical diagnostics business – who have experience of managing a listing or even a $100million-plus exit in the sector and, whilst they’re at it, run a 250-person enterprise going gangbusters on commercialisation – is perhaps only a couple of dozen. This is not hyperbole; it is empirical reality. The global AI diagnostics market is projected to reach $10.12billion in 2026, growing at a compound annual rate of 46 percent over the next decade to $209.64billion by 2034. Yet the talent pool, unsurprisingly, isn’t keeping pace. has not kept pace. AI, in its current commercial form, has been around for less than a decade, and the number of people with demonstrable AI success in the medical technology sector is a vanishingly small fraction of the total employed. This scarcity creates a fundamental problem for traditional search firms. Their model depends on volume, on ‘bench strength’, on having a Rolodex of names they can trot out for any assignment. The usual suspects approach of offering the same polished, pedigreed executives who talk the talk and operate in all the right circles, the inconvenient truth that looking good and being good are not the same thing. This critique is not merely anecdotal. Research published in JAMA found that ‘health systems are deploying unproven algorithms with little evidence they improve outcomes – or even do no harm’ . Former FDA commissioner Robert Califf said: I do not believe there's a single health system in the United States that's capable of validating an AI algorithm that's put into place in a clinical care system. The same gap between appearance and reality that plagues AI algorithms plagues executive talent. The usual suspects look like they know what they are doing. That does not mean they do. External voices Don’t take my word for it, a 2025 report on talent strategies for precision diagnostics notes that ‘the competition for specialised talent is intensifying across biotech and pharma hubs’ and that ‘job postings for digital pathology, AI in diagnostics, and computational biology have doubled in the past two years’. Rember that ‘job postings’ in this context are a single digit percentage of actual need. The report emphasises that ‘hybrid roles’, combining laboratory and computational backgrounds, are increasingly sought after, and that ‘navigating the regulatory landscape is crucial’. Similarly, a 2025 analysis of the GenAI healthcare talent market found that finding talent isn't the real issue anymore. Managing it is where everything breaks down . The report notes that ‘healthcare context is hard to learn’ and that ‘generic contractors don't bring this context – you spend weeks explaining basics only to churn through them after three months’. This is precisely the problem with the usual suspects: they bring generic executive credentials but not specific domain expertise. A recent peer-reviewed paper in the Journal of Laboratory and Precision Medicine emphasised that successful AI integration in laboratory medicine requires ‘multidisciplinary collaboration and change management’, noting that ‘building trust towards AI-assisted patient care’ was a crucial challenge Conclusion: the empirical advantage The usual suspects approach is no longer fit for purpose because the traditional recruitment model is fundamentally misaligned with the reality of AI clinical diagnostics. It offers scale when what is needed is specificity. It offers bench strength when what is needed is deep, longitudinal knowledge of a narrow pool. It offers polished executives from central casting when what is needed is someone who has done this specific thing before. A different approach is needed. In the age of AI, companies with specific requirements need specialist recruiters with deep, hard-earned experience, who do not claim to know everything about everybody, but who know a few things about the people that matter. This is not knowledge that can be acquired quickly. It is not knowledge that can be bought off the shelf. It is knowledge that has been earned, year by year, placement by placement in a sector where the talent pool is tiny and the stakes can be huge. The writer is Chief Executive of Snedden Campbell, a specialist recruitment consultant for the global medical technology industry.

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The failure of the ‘usual suspects’ approach to life science recruitment