Digital tools have long influenced how managers plan, analyse and organise their work. What feels different in 2026 than, say, 2010 or even 2022, is the speed at which AI-enabled tools are being adopted and the breadth of tasks they now support. From drafting documents to summarising information and interpreting data, AI software is increasingly present in everyday management activity.
This growing presence does create opportunities for organisations, but it also requires discerning leadership. While some applications of AI can meaningfully support managers, others risk intruding into areas where leadership depends on judgement, intuition, context and human connection – which software cannot do. Essentially, Large Language Models (LLMs) – including ChatGPT and other chatbots – are a specific class of machine learning software focused on statistical language analysis. Extremely useful tools, certainly, but not ‘intelligent’ in the way that ‘Data’ from Star Trek is intelligent.
In some ways, the term ‘Artificial Intelligence’ itself obscures the true value and applications of these tools, making the technology feel either more threatening or risky than it genuinely is, or leading to misunderstandings of its capabilities. Understanding these nuances and distinctions is becoming an important part of modern management practice.
AI’s value for managers lies primarily in its ability to handle data at scale, in a structured format, faster than a person could feasibly do unaided. AI tools can process large amounts of information quickly, summarise complex material and reduce the time spent on administrative tasks. Research discussed in People Management in April 2025 looked at the ways in which AI is already reshaping mid-level managerial work, particularly in the fields of decision support and information flow. When applied appropriately, AI has the potential to enhance preparation and insight, supporting more informed leadership rather than replacing it.
This potential is reflected in business sentiment. According to the Institute of Directors (in an article actually dealing with blockers to AI adoption by British businesses), nearly half of UK business leaders had already adopted AI across some of their functions and processes, and of these, 78% reported improvements in productivity and operational efficiency, particularly in data-driven and process-heavy activities.
These gains highlight why AI has become attractive to managers operating under pressure. However, efficiency alone is not a sufficient measure of leadership effectiveness… The same tool that strengthens leadership in one situation can weaken it in another, and effective use largely depends on recognising the nature of the task at hand.
AI tools are particularly well suited to activities that involve repetition or large volumes of data. In these contexts, they can improve efficiency and support better preparation without displacing human judgement. For example:
Used in this way, AI acts as an input rather than an authority, providing information that leaders still need to interpret, prioritise and act on.
AI becomes more problematic when it is used in situations that depend more on nuance, emotion and context. The tools themselves aren’t usually the problem – tools are just tools at the end of the day. However, managers are often encouraged to “use AI” without clarity on boundaries, expectations or risks. Real world leadership often involves ambiguity, competing perspectives and interpersonal dynamics that cannot be fully captured by algorithms. Areas requiring particular care include:
While AI may assist with preparation; for example, helping a leader reflect on several possible approaches, substituting it directly into these interactions, risks flattening complexity and weakening connection. The trouble is that without shared expectations or development support, managers are left to make individual judgements about their AI use, often through trial and error.
If organisations are not careful, unintentional misuse of AI can erode trust and credibility in leadership, and hinder adoption of AI tools and processes. A worrying 43% of UK workers say they feel ‘deceived’ when their senior leaders rely on AI-generated communications. This places increased importance on leadership development that explicitly addresses the when and why of AI use, not just the how.
Clear principles, reflective practice and opportunities to explore real leadership scenarios can help managers build confidence in using AI as a support tool, while continuing to strengthen the human capabilities their role depends on.
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