What does Credible AI Competence Actually look like, and who Stands Behind it?

As AI adoption outpaces the systems meant to recognise it, the question for the sector is not how to teach AI, but how to certify it in a way employers, learners and centres can actually trust.

Stanford’s 2026 AI Index made the scale of change hard to ignore: generative AI has reached population-level adoption faster than the personal computer or the internet, yet policy, assessment and formal education are struggling to keep pace. Capability is racing ahead; the frameworks to recognise it are not.

The policy signals point the same way. In February 2026 the government named Barnsley the UK’s first government-backed Tech Town – a national blueprint for rolling out AI across public services, health, business and education, with Barnsley College and the South Yorkshire Institute of Technology delivering free AI training at its heart. Alongside a commitment to upskill 10 million UK workers, the direction of travel is clear: AI skills are now treated as core infrastructure for growth, and further education is squarely in the frame to deliver them.

However, initiatives like Barnsley create something important the moment they succeed – demand. When a town, an employer or a whole region commits to building AI capability, it raises a question that funding and ambition alone cannot answer: once people have been trained, how is that capability recognised, and who stands behind it? Training people is one thing. Certifying their capability in a way employers, learners and centres can trust is another – and it is here that the system is least developed.

Learning is everywhere. Trusted capability is harder to find.

The AI skills market is crowded. Courses, certificates, micro-credentials and platform badges are appearing everywhere, and much of it is good, but volume is not the same as assurance. When every provider defines AI competence differently, employers cannot easily compare what a certificate means, learners cannot be sure the skills they gain will carry weight, and centres cannot always tell which provision will stand up to scrutiny.

This is familiar territory for further education. It is precisely the problem regulated qualifications and awarding organisations exist to solve in every other discipline: turning a noisy market of learning into recognised, comparable, portable achievement. An awarding body’s value has never been simply to issue certificates – it is to provide quality, national comparability, clear progression and assessment employers can rely on. AI is no different, except that the pace of change makes those foundations more important, not less.

Why AI deserves qualifications in its own right

Too often, AI is treated as an extension of computing or digital skills rather than a discipline in its own right – a framing that is increasingly hard to justify. Working effectively with AI now cuts across almost every role and sector, and it draws on capabilities a general digital qualification was never designed to assess: judgement, oversight, ethical reasoning and responsible application.

Crucially, AI competence extends far beyond knowing how to use the latest tools. Ethics, transparency, privacy, bias, accountability and safe application are not optional add-ons; they are fundamental principles that should sit at the heart of how AI capability is taught and assessed. As AI becomes embedded in everyday work, responsible use has to count as much as technical skill – and assessment has to be designed to evidence it. There is a strong case, too, for treating AI as a progression rather than a single course, with recognised pathways from foundational literacy through to advanced professional capability that let learners keep developing as the technology, and their responsibilities, evolve.

A shared standard is a sector effort

None of this is the work of any single organisation. A standard that employers trust, learners value and centres can adopt with confidence has to be shaped with the sector, not handed to it: qualifications informed by employers, educators and practitioners, with responsible AI built into the model rather than bolted on, and kept current as tools, roles and skills needs change.

It also means being honest about who delivers it. Trusted AI qualifications only reach learners through the centres that offer them – FE colleges responding to employer demand, independent training providers building future-skills provision, universities extending higher-level pathways, and schools laying the early foundations of AI literacy. Barnsley is a case in point: a college placed at the centre of a national AI programme, delivering the training on the ground. Giving centres like these a recognised, regulated framework to certify what learners can do is what turns local ambition and free training into capability that carries weight beyond any single town.

This is the space The AI Board works in, as an awarding organisation focused specifically on AI and data. But the wider point matters more than any one organisation: the AI skills market does not need more noise. It needs a clearer standard – one that people can recognise, centres can adopt, employers can trust and learners can use to move forward.

The opportunity for the sector

Bridging the AI skills gap is about more than issuing certificates. It is about confidence: for employers, that a qualification reflects real workplace competence; for centres, that they are delivering something relevant and future-focused; and for learners, that the skills they build today will still hold value tomorrow. The opportunity for further education is not to compete on volume with an already-crowded market. It is to lead on trust – to bring the sector’s hard-won expertise in quality, assessment and progression to bear on the defining skills challenge of the moment, and to make sure that as AI becomes part of everyone’s working life, the qualifications that recognise it are ones the whole system can stand behind.

By Aman Bedi, Business Development Manager, The AI Board