Your boss might be bluffing about how much AI knowledge they really have

What happened

A new workplace survey reported by TechRadar finds that 23% of managers admit to overstating their own understanding of artificial intelligence, even as organisations increasingly treat AI fluency as a hiring priority. The same research shows that close to half of managers now weigh AI skills more heavily than formal qualifications when assessing candidates.

The findings point to a widening gap between the AI expertise employers expect staff to demonstrate and the AI literacy that leadership itself can genuinely claim. While AI competency is being fast-tracked up hiring criteria, the survey suggests a share of the people doing the assessing may not have the depth of knowledge to back up the standard they are setting.

Why it matters

This is fundamentally a workforce and talent-management story about how fast AI fluency is being formalised as a job requirement — and how unevenly that fluency actually exists inside organisations. When managers overstate their own AI knowledge, hiring decisions, performance reviews and AI adoption roadmaps risk being shaped by perception rather than verified capability.

For leaders driving digital transformation, the gap matters operationally: AI tools are only as effective as the judgement applied to deploying, governing and coaching their use. If the people responsible for guiding AI adoption are not confidently equipped themselves, training investments, change-management plans and even recruitment pipelines can end up built on an inflated baseline.

By the numbers

• 23% of managers surveyed admit to exaggerating their own AI knowledge.
• Nearly half of managers say they now rate AI skills above formal qualifications when hiring.

The Renascence take

The headline risk here isn't that some managers are bluffing — it's that organisations are quietly outsourcing AI governance to confidence rather than competence. Experience and transformation leaders should treat this as a signal to audit perceived AI capability against actual, demonstrable skill before it shapes hiring, training budgets or customer-facing AI rollouts.

Confidence is not a proxy for capability, and the cost of pretending otherwise compounds quietly — in bad hires, misjudged AI rollouts and employees coached by people who don't actually know what they're coaching. Any organisation serious about AI maturity needs to separate who sounds fluent from who is fluent, starting with an honest skills audit at management level, not just entry-level hiring filters. The real differentiator in the next phase of AI adoption won't be who claims expertise loudest, but who has built the verification and upskilling discipline to make that expertise real.