AI governance needs everyday judgement, not just a policy, says Skillsoft

Conceptual paper-cut illustration of a hand using a magnifying glass to examine branching paths with check and stop symbols.

Skillsoft’s Greg Fuller calls for role-specific training and clear accountability so employees can judge AI outputs and recognise when human intervention is needed.

Access to AI is only part of the challenge for employers. Staff also need the judgement to evaluate its outputs, understand its limits and know when a person needs to intervene, according to Greg Fuller, VP Technology Skills Suite at Skillsoft.

In commentary supplied to Onrec, Fuller argues that governance needs to become part of everyday work as employees use AI to analyse data, generate content and support decisions.

“AI can accelerate work, but it cannot be accountable for the consequences of its decisions.”

Greg Fuller, VP Technology Skills Suite, Skillsoft

Turn policy into practical decisions

Fuller says organisations need people who can design, verify and govern the use of increasingly capable AI systems. That means practical training tailored to individual roles, clear boundaries around data, security and quality, and managers who can turn policy into action.

His argument puts critical thinking and technical judgement alongside access to the technology itself. Written policies are unlikely to be sufficient if employees cannot apply them when reviewing an output or deciding whether to escalate a problem.

Why it matters for recruitment and HR

For teams using AI in recruitment or people management, the practical implication is to make responsibility clear: who checks an output, what needs human review and where an employee can ask for help. Training can then address the decisions people actually face in their roles, rather than treating governance as a separate compliance exercise.

See also Onrec’s report on the UK national standard for recruitment technology.

Image: conceptual editorial illustration created for Onrec.

Key takeaways

  • Teach employees how to evaluate AI outputs and recognise when to seek human review.
  • Make training relevant to the decisions and risks in each role.
  • Give managers clear responsibilities for applying data, security and quality rules.

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