The Generalist Paradox: Why the People We Need Most Are Being Screened Out
Dr Simon Leigh takes a deeper look at what he’s calling the Generalist Paradox.
The future of work is often described with a few familiar buzzwords: adaptable, agile, AI-savvy. And increasingly, there’s a consensus emerging, generalists will be the ones best equipped to thrive in this new world.
These are the people who don’t fit neatly into one lane. They’re the connectors, the translators, the problem solvers. They’ve worked across teams, functions, and even industries. They can switch context, bring fresh thinking, and help businesses navigate change, not just endure it.
But there’s a growing problem. One that few are talking about.
We’re building hiring systems that exclude the very people we say we need.
The Rise of the Generalist – and the Systems Working Against Them
AI is transforming how we work and it’s also transforming how we hire.
As job seekers face uncertainty about automation and industry disruption, many are responding by ramping up their job search. Tools like AI-generated CVs, automated applications, and job tracking platforms are making it easier than ever to apply at scale.
Generalists, by nature, tend to be proactive. They’ve learned how to experiment with new tech, sell their breadth, tailor their experience, and pitch themselves across roles and sectors. In a high-volume hiring environment, this makes perfect sense.
But the surge in applications has triggered another reaction: recruiters and hiring teams are turning to their own AI tools to cope. Applicant tracking systems (ATS), resume parsers, and AI-powered screeners are being used to quickly sift through hundreds, sometimes thousands of applications per role.
And those tools? They’re built on pattern recognition.
They look for certain titles, industries, keywords, and sequences of experience. They’re built to identify the clearest match to a predefined profile.
Which is exactly where the paradox emerges.
Generalists Don’t Follow Patterns
Generalists rarely take the traditional path. They haven’t spent 15 years climbing the same ladder. Their resumes often look unconventional, non-linear career moves, hybrid roles, shifts across sectors. I’m one of those people myself.
To an unbiased human reader, this might signal curiosity, versatility, and a broad skill set. But to a screening algorithm, it often signals risk or irrelevance.
When a tool is trained to prioritise a project manager who’s held three project manager roles in a row, the candidate who’s been a designer, then a strategist, then a product manager doesn’t even get surfaced, despite potentially being the better fit for a complex, interdisciplinary team.
The irony is painful: the people best suited to navigate uncertainty and change are the ones most likely to be filtered out before a human ever sees their application.
The Talent Is There. The Tools Are Broken.
This isn’t just a hiring inefficiency. It’s a deeper systemic issue.
The more we lean on AI to filter people, the more we risk reinforcing narrow definitions of what a “good candidate” looks like. Over time, companies start to feel like the “right talent isn’t out there.” But that’s not true.
The right talent is out there.
They just didn’t have the exact job title.
They just didn’t use the exact keyword.
They just didn’t take the exact career path the algorithm was trained to prefer.
And so they get overlooked. Again and again.
Why This Matters More Than Ever
Generalists will be essential in a world increasingly shaped by AI. Not just because they can learn new tools, but because they can ask better questions. See across silos. Connect the dots. Imagine what doesn’t yet exist.
If we build systems that punish that kind of thinking, we don’t just harm individual careers. We harm our ability to solve the big, messy problems the future is throwing at us.
Innovation doesn’t come from pattern matching. It comes from pattern breaking.
What We Can Do About It
Solving this doesn’t mean throwing out AI. It means being more intentional in how we use it.
Here are a few places to start:
Rethink your filters: Look at your screening criteria. Are you filtering out people based on narrow patterns or rigid keywords? Consider human-first reviews of a subset of "non-traditional" candidates.
Train your tools on outcomes, not roles: Focus on what candidates have delivered, not just what job titles they’ve held.
Value range and adaptability: Design job ads and evaluation frameworks that acknowledge the value of breadth, not just depth.
Create alternate paths to visibility: Portfolios, case studies, and project-based applications can help generalists show, not just tell, what they can do.
Challenge the myth of the perfect fit: Perfect fits are easy to manage, but they don’t drive change. Generalists bring friction—but it’s the productive kind.
Final Thoughts: Don’t Let the Tools Fool You
The generalist paradox is one of our own making.
We say we want adaptable, creative, cross-functional thinkers, but we’ve built the very systems that filter them out. It’s not a talent shortage. It’s a visibility problem. A design flaw. A gap between what we claim to value and what we actually select for.
As we move further into an AI-driven economy, companies that recognizse and resolve this paradox will be the ones that build stronger, more resilient teams.
So if you’re a generalist who feels invisible right now, know this: you’re not the problem.
And if you’re hiring for the future, make sure you’re not screening it out before it even gets through the door.