Yet algorithms aren’t automatically fair. A poorly designed system can reinforce existing prejudices or create new ones. If a recruitment algorithm is trained on data where certain groups are under-represented or portrayed negatively, it learns to discriminate. If performance evaluation relies solely on quantitative metrics, it favours easily measurable work whilst penalising qualitative and innovative contributions.

Technology demands caution. Transparency, privacy, and fairness must form the foundation. Whilst algorithms can be more impartial than humans, they still reflect their creators’ values and assumptions. This is where their potential lies—a potential often underestimated in leadership discussions.

When a human makes a biased decision, proving or challenging it is nearly impossible. With algorithms, we can examine the logic, test different scenarios, and correct flaws. A transparent algorithm is easier to hold accountable than an opaque human. Research confirms that lack of transparency significantly weakens workplace atmosphere and trust, diminishing commitment and motivation (Parent-Rocheleau & Parker 2022; McParlja & Connolly 2019).

Problems remain, however. In the OECD study, two-thirds of managers cited at least one ethical or administrative issue with algorithmic management. Common concerns include accountability in error situations, decision transparency, and employee wellbeing. Who bears responsibility when an algorithm errs? Many organisations have yet to answer this question.

The positive news: most companies are taking action. According to the OECD report (2025), 89 per cent of organisations have guidance for using algorithmic tools. Common practices include user guidelines, employee consultation, and regular audits. Awareness exists, and organisations are attempting to anticipate problems.