disruption and the process of replacing them. But the subtler cost is what the cycle does to talent strategy over time.

When screening is automated and optimised for pattern-matching, organisations end up narrowing their own talent pools. They hire people who look like the people they’ ve always hired. The unconventional thinkers and candidates who bring genuinely different experience are precisely the people most likely to be filtered out. This is a strategic issue dressed up as an operational one.

There’ s also the question of employer brand. Candidates who experience a hiring process that feels opaque, automated and impersonal don’ t forget it. In a market where employer reputation increasingly shapes who you’ re able to attract, a poor candidate experience is a slow leak in the pipeline.

What breaking the cycle requires

The answer isn’ t to reject AI in hiring – that ship has sailed, and used well, AI genuinely improves recruitment. The answer is to be deliberate about what AI should, and shouldn’ t, be doing.

AI earns its place in recruitment when it removes administrative friction: drafting job descriptions, pre-screening against specific and transparent criteria, reducing the time recruiters spend on scheduling and co-ordination. Our own data suggests this kind of targeted automation can reduce shortlisting time from around three hours to 10 minutes per role. That’ s real capacity returned to hiring managers.

What AI should not be doing is making decisions about people, or creating the conditions in which

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