The economics of knowledge work are being transformed by AI. Information is now abundant, shifting value towards judgment, adaptability and the ability to solve problems. Along with these changes, it is also, perhaps, creating a much more open and exciting labour market.
For decades, organisations have said they struggle to find qualified people, and the trend continues, with Manpower Group’s survey showing that 75% of employers reported difficulty filling vacancies in 2025. The study concludes by describing the UK as having experienced a talent shortage for “almost 20 years.”
Yet, so many employers remain heavily reliant on CVs, degrees, traditional professional qualifications and career history as the only real filters for deciding who is worth interviewing. The result is that “qualification” can become confused with capability, which is what we call ‘credentialism’ – the tendency to treat formal qualifications, degrees and professional credentials as proxies for a person’s ability or suitability for a job.
AI is making knowledge cheap, which exposes the weakness of this credentialist thinking. Now, organisations are rethinking how they assess, develop and credential professional talent. For example, a major City of London financial institution, Schroders, recently said it has “moved away from hiring Oxbridge history graduates” and tells The Economist it is “casting its net wider” looking for data and tech skills.
Do qualifications really prove capability?
For much of the modern economy, the answer was fairly straightforward: a degree or professional qualification was a placeholder, an indication of ‘acquired knowledge’, providing employers with a relatively convenient signal that someone had mastered a defined body of material and could apply it to a professional task.
But AI is rapidly changing the value of even four years of BA/BSc and Master’s-level knowledge. Information that once took years to acquire can increasingly be retrieved, summarized and applied with the assistance of AI; regulations, policies, technical guidance and established bodies of knowledge are available almost instantly.
Credentials worked because they solved a genuine problem for employers: comparing generally similar candidates at scale. It is difficult to assess the capabilities of thousands of applicants individually. A degree, qualification or prestigious career history provides a simple way of narrowing the field. The problem is that this signal became more revered than what it was supposed to represent.
This is particularly relevant in professions where the work is not primarily about recalling a fixed body of knowledge. Some aspects of HR, for example, absolutely require detailed knowledge of employment law, regulation, payroll and established processes. But, in reality, much of the value of an experienced HR professional lies elsewhere: in navigating difficult situations, balancing competing interests, understanding an organization’s commercial context, making judgments about people and solving problems for which there is no textbook answer.
That is not the same skill as passing an examination. And AI makes the distinction increasingly important. If I can ask an AI system to find and explain the relevant employment legislation in seconds, my real contribution now lies in knowing what question to ask, assessing whether the answer is credible, understanding its implications, weighing alternatives and deciding what the organization should actually do.
In other words, AI doesn’t just change the work itself. It potentially changes the way people can demonstrate their ability to do the work.
How will employers adapt now that the question has changed from “what do you know?” to “what can you do?”
The translation is simple: knowledge now has a remarkably short shelf life; capability does not. That’s why many employers we talk to are investing heavily in internal learning programmes designed around real business challenges, not abstract theory. Their goal is to develop judgment, confidence and practical decision-making, not simply to add another certification to your résumé.
If recruiters begin asking, “Can this person solve the kinds of problems our business actually faces?” rather than simply looking at the letters after someone’s name, then L&D will need to evolve alongside that shift too.
Rather than serving as evidence that someone once mastered a fixed body of knowledge, the most valuable credentials of tomorrow will demonstrate the ability to apply knowledge in uncertain, technology-rich environments. They will assess judgment as much as recall, collaboration as much as individual performance, and adaptability as much as technical expertise.
This does not mean closing universities or abandoning professional development pathways. Traditional degrees and industry-recognized qualifications will continue to play an important role. But they often require significant investment of time and money from both learners and sponsoring organisations.
As a result, practice-based learning models focused on demonstrable capability and focused on creating faster, more targeted pathways for talented professionals are likely to become increasingly attractive. For many employers and candidates, real-world experience and proven ability may become just as valuable as academic history alone.
Toward a practical, business-centric, AI-era standard of excellence
What is needed is a move beyond rote, fact-based certification towards a practical, business-centric standard of excellence built for the AI era.
We are clearly at an inflection point in the adoption of AI across organizations, and the advantage will no longer belong simply to those who know the most, but to those who can adapt fastest and apply their knowledge most effectively.
In the age of AI, that is what professional credentialing should be designed to embrace and support.
