How managers can use AI to augment their capability

AI is no longer a future possibility. It is already shaping how decisions are made, tasks are prioritised and performance is evaluated. Managers are not preparing for disruption. They are already managing it.

While AI can generate, predict and optimise quickly, it cannot reflect, empathise or lead. Machines analyse data. Humans interpret meaning, apply judgement and remain accountable for outcomes. That distinction is essential to responsible leadership.

This article offers guidance on how managers can balance the strengths of AI with the human capabilities that remain essential.

When AI supports and when humans must lead

AI is well suited to structured, rule-based tasks. Using it to summarise meetings, screen CVs or segment customers saves time and increases consistency.

But most work involves nuance, incomplete data or shifting goals. AI may produce results that appear correct but lack depth.

Take hiring. AI might filter CVs based on past patterns. But if those patterns contain bias, such as penalising career breaks or excluding certain groups, it can reinforce unfairness. A human manager must step in and assess with fairness in mind.

Even in performance reviews, AI might highlight metrics, but it cannot detect low morale or interpersonal friction. Only a human leader can interpret the bigger picture and respond appropriately.

AI should support. Humans must decide.

Oversight must be active, not symbolic

Having a ‘human in the loop’ is often used as a safeguard, but in practice this can become a passive approval step. Managers may be asked to sign off on AI outputs without the understanding or authority to question them.

Meaningful oversight means knowing how the system works, recognising its limits and feeling confident to revise or reject results.

This is especially important with AI hallucinations – false yet convincing information generated by the system. If accepted without scrutiny, these can undermine trust and decision-making.