Leadership and Management in the AI Era

Risk and uncertainty are different. With risk, you know the possible outcomes and can estimate their likelihood. With uncertainty, even that is impossible. AI brings both: errors in an application can be measured, but nobody knows how work will change over the next few years. Leaders who handle uncertainty well choose a direction for the coming months, state what they do not know and agree when to review it. They distinguish between commitments, such as values and customer promises, and things that can change, such as methods and systems. A trial can then stop without abandoning the direction. New evidence changes the approach without immediately changing the vision.

A vision sets out where an organisation wants to be in a few years and why: its customers, role and work. An AI vision limited to systems overlooks what the organisation wants to do differently or better with AI. Scenarios test that vision. Each is a coherent picture of a possible future, not a prediction. One might feature widely available, low-cost AI; another, strict rules and little trust. Testing a vision against three or four scenarios reveals which choices hold up across all of them and which depend on one particular future. Leaders can then judge which steps already make sense.

Taking initiative means starting before all the answers are in. In many organisations, middle management waits for a strategy while executives wait for ideas from below. A leader who starts breaks that deadlock. It can begin with several weeks of using AI for a regular task, such as a monthly report or preparing a performance review. Experience reveals where AI helps and where it does not. A subsequent team trial tests its value in shared work. The discussion about AI is then grounded in experience within the organisation rather than a supplier's presentation. Even a trial that achieves little reveals the gap between expectations and practice.

An AI-first organisation starts with a leader who makes choices about work. AI and new technology do the work people need not do, including the repetitive and predictive work: compiling reports, updating schedules, forecasting next quarter's demand. People focus on what they do better: exercising judgement, making choices and working with customers and colleagues. A team that leaves the gathering of figures to AI has more time for the customer who has doubts and the colleague who is stuck. Whether that time really materialises depends on how the leader divides the work anew.

More teams work alongside AI agents: AI systems that take steps independently within an assignment, such as compiling a case file or updating a schedule. Leaders reallocate work on the AI-first principle, identifying what an AI agent can do and where a person is needed. They set its authority and decide who checks its output. Performance assessment changes because some of the work no longer rests with one person alone. New roles emerge, including people who configure and monitor AI agents. The human work of leadership remains: coaching, exercising judgement and having difficult conversations. The Agentic AI keynote examines authority and oversight in more depth.

Psychological safety means a team believes people can ask questions, express doubt or report mistakes without punishment or humiliation. This matters when AI changes work, because it affects people in ways no project plan covers. An experienced employee wonders what their expertise is still worth; a colleague sees extra work. When people are afraid to voice doubts, they try little that is new. In customer service, a leader can let the team choose which enquiries AI drafts responses to and which staff answer themselves, with time set aside to practise. The leader also highlights what remains the same: professional judgement, contact with customers and knowledge of the work.

As AI takes on more tasks, the definition of a good employee changes. Expertise still matters, alongside the ability to give clear instructions, assess results critically and redesign one's own work. Talent is increasingly judged by what someone can do, with less emphasis on job titles or qualifications. That affects every career conversation a leader has. Learning goals shift with the work. Recruitment places more weight on how candidates use AI. Retaining good people means discussing how their work is changing and the development that fits it. The start of a career changes too, as AI takes over some entry-level work.

Executive teams and boards deciding on AI need enough understanding to ask good questions. Learning starts at the top: regular time to try applications, conversations with specialists and honest assessment of their own knowledge. Leaders also take responsibility for learning across the organisation. Education often lags behind changes at work, and one-off training dates quickly. More organisations are building a future academy, where tomorrow's employees and leaders learn through programmes based on their own processes, taught by colleagues. This changes the leader's position too: they make decisions about technology whose capabilities keep changing even as they learn.

In many organisations employees use AI on their own initiative, often with a personal account. That signals two things: there is demand, and there are risks, from company data leaving the building to answers nobody checks. A ban only pushes that use further out of sight. It works better to provide good tools, make clear which data may and may not be used, and stay curious about what people have already discovered. Those employees are often the best guides: they know where the work gets stuck and what AI is already changing there. Leaders who ask about it and share the good examples have something to build on.

Innovation in an established organisation means connecting something new to existing systems, rules introduced for good reasons at the time, and habits nobody questions any more. AI makes this tangible: a new application has to work with old data and procedures. Leaders making progress in that setting do not start by replacing everything. They choose a process with a clear owner, build a working solution alongside the old system and demonstrate the result. Retiring the old method then becomes a smaller step. They also remove things deliberately: an approval stage that adds nothing, a report nobody reads. Making room for something new often starts with removing something old.

It is tempting to hand AI to IT, an innovation team or a newly hired AI manager. But the work that changes sits in the line: with the department that writes quotations, the team that helps customers, the planners in the warehouse. Specialists build; line leaders apply it and own the result. They decide how the work changes, who checks what and when an application is good enough. An innovation team that builds a pilot without an owner in the line produces a demonstration, not a new way of working. When ownership sits in the line, a pilot can grow into business as usual, and the knowledge is more likely to stay when the project team leaves.

AI makes a lot of work cheaper and faster. But cheaper is not always better. The payments company Klarna let AI take over a large part of its customer service. Its chief executive later admitted that the focus on cost had led to lower quality; the company is investing in human staff again, so customers can always speak to a person if they want to. The lesson is not that AI fails in customer contact, but that leaders must choose deliberately where people make the difference. That is often in moments of doubt, emotion, complaints and big decisions. Organisations that get this right work out for each customer moment what the customer really values, and automate the rest.

No organisation can hold all AI expertise in-house. AI is developing in many places at once, through the work of start-ups, researchers and suppliers. Organisations in other sectors are often tackling the same questions as yours. A leader's ecosystem helps determine how quickly an organisation learns, and extends beyond national borders. Building it takes effort: visiting organisations further ahead, finding partners for joint trials and talking openly with peers about failures. A healthcare organisation can learn from scheduling at a port; a local authority can learn from how commercial businesses handle customer enquiries. Who you spend time with as a leader is a strategic choice, not a minor detail in your diary.