The Organisation of the Future

In an AI-first organisation, the division of work changes. AI and new technology do the work people need not do, including the repetitive and predictive work, and people focus on the work they are better at, such as weighing things up, persuading others and taking responsibility. For every process, the same question comes back: what does AI do, and what does the person do? At an insurer, a straightforward claim then runs through systems from report to payment, while the claims handler assesses the doubtful cases and helps the customer who has just lost everything. A redesign like this also changes the size of teams and the nature of entry-level work.

Most organisations are divided into departments, and the first AI projects follow that division: an assistant for marketing, a chatbot for customer service, a tool for finance. Separate projects can save time, but they do not automatically improve how the departments serving the same customer work together. Take an installation company. The customer asks for a quote, waits for an engineer, gets an invoice and calls when something goes wrong. When AI supports that whole chain, with the same data from first question to last service visit, staff need to ask for the same information less often and handovers become simpler. Map that chain, from quote and scheduling to invoice and service visit, and you see where AI takes over the work and where people make the difference. The design question shifts from the department to the promise to the customer, with one owner for the whole chain.

When AI does part of the work, other work grows up around it. Three kinds of role recur in many organisations. People who check AI's work: they assess whether an analysis, recommendation or letter is right before it goes out. People who design the work with AI: they decide how a process runs, which tasks a system gets and where a person decides. And people who monitor the systems: they keep track of which AI runs where and with which data, and step in when quality slips. At an accountancy firm, for example, experienced auditors can check the analyses AI produces. Their professional knowledge helps them spot an unlikely result or missing information.

In a team of people and AI agents, an agent is a permanent member with its own tasks, not a tool someone opens now and then. People do what they are good at: judging, choosing, working with people; AI takes the repetitive and deeply analytical work. That changes the make-up of the team. A marketing team with separate specialists for copy, visuals, analysis and planning can work with fewer people, each overseeing a larger share of the work, while AI creates variants, tests them and reports back. The way an organisation values someone's contribution also changes when part of the work is done by AI.

In many organisations, decision-making is built on scarce information: data goes up, gets bundled and comes back down as a decision. AI changes that logic. A system delivers current figures, scenarios and counter-arguments where the work happens, at the moment they are needed. That puts the question of who decides what back on the table. In a retail chain, a branch manager with a forecast for each product can adjust stock and staff rotas without waiting for head office. Head office then sets the framework: which decisions are made locally, which a person always makes, and which a system prepares or carries out within fixed limits.

Many organisations earn their money from time or volume: billable hours, licences sold, cases handled. When AI speeds up the work, those models come under pressure and room opens up for other arrangements. Some consultancies and law firms charge a fixed price for agreed work or link part of their fee to a result achieved. Machine builders sell availability as a service, with predictive maintenance that limits downtime. Some software providers charge per resolved customer enquiry, alongside or instead of a fee per user. A switch like this touches the whole organisation: how work is budgeted, who carries the risk and how employees are assessed when hours are no longer the measure.

Leadership in an organisation that is always changing is less about control and more about setting direction and giving people room to act. Leaders of the future sketch how the work and the organisation could change, and why people would want to contribute. They lead by listening, asking questions and showing vulnerability, and they give teams room to make their own decisions. Change is as much about psychology as about strategy. People change along with it when they understand why and see their leaders learning too. An executive who uses AI personally and explores with employees which work it changes is building tomorrow's organisation together with them. The keynote on leadership and management goes into this in more depth.

Change is less about technology than about how people look at their work. A new mindset means letting go of habits that worked well for years, being open to ideas from outside and seeing uncertainty as a source of innovation. Procedures and habits give people something to hold on to and protect quality, but sometimes they also act like an immune system that rejects anything new. A culture of freedom and independence gives employees the confidence to question established ways of working, try things out and learn from mistakes, including where mistakes can have serious consequences. At a logistics company, a planner with an idea for smarter routes then runs a trial within a month, without three layers of approval.

Professional knowledge remains the foundation, but in an organisation that changes fast, curiosity, flexibility, passion and the ability to think beyond established approaches count too. AI skills come on top of that: knowing what AI can do, judging its results and organising your own work differently. Educational institutions are adapting their programmes, but organisations often need that knowledge sooner than new graduates arrive. That is why more and more organisations are taking charge themselves, with a future academy where employees and leaders learn from material drawn from their own practice. A bank trains customer advisers there to become specialists who work with AI systems. The result is an organisation of independent team players who keep learning.