The Organisation of the Future

In an AI-first organisation, the division of work changes. AI and technology do the work people need not do, including the repetitive and predictive work, and people focus on what they do better: weighing up options, persuading others and taking responsibility. This goes beyond adding AI to existing routines. At an insurer, systems handle a straightforward claim through to payment; the claims handler assesses the ambiguous cases and helps the customer who has just lost everything. Redesigning work this way also changes team sizes and entry-level tasks, and with them how new employees learn the job.

Most organisations are divided into departments, and their first AI projects follow suit: a marketing assistant, a customer service chatbot, a finance tool. Each may save time without improving how departments serve the same customer together. At an installation company, a customer requests a quote, waits for an engineer, receives an invoice and calls if something goes wrong. When AI supports that whole process, using the same data throughout, staff need to ask for the same information less often and handovers become easier. The customer promise becomes the starting point for design, with one person responsible for the complete process across departments.

As AI takes on tasks, other work grows around it. Three types of role recur. Some people check AI's work, assessing whether an analysis, recommendation or letter is accurate before it leaves the organisation. Others design the work: how a process runs, which tasks a system handles and where a person decides. Others monitor the systems, tracking which AI runs where, which data it uses and when declining quality calls for intervention. At an accountancy firm, experienced auditors can review AI analyses. Their professional knowledge helps them spot an unlikely result or a missing piece of information.

In a human-AI team, AI is a core member with its own tasks. It is more than a tool someone uses occasionally. Tasks are allocated according to the AI-first principle: what a system can do is considered first, followed by what a person does better. People exercise judgement, make choices and work with others; AI handles repetitive tasks and deep analysis. This changes team composition. A marketing team with separate specialists in copy, visuals, analysis and planning may need fewer people, each overseeing more of the work while AI creates variants, tests and reports. How an organisation values someone's contribution also changes when AI does part of the work.

Decision-making in many organisations is built around scarce information: data travels upwards, gets consolidated and returns as a decision. AI changes that logic. A system supplies current figures, scenarios and counterarguments where the work happens, when they are needed. That reopens the question of who decides what. In a retail chain, a branch manager could use forecasts for each product to adjust stock and staff schedules without waiting for head office. Head office sets the boundaries: which decisions are local, which always belong to a person, and which a system may prepare or carry out within agreed limits.

Many organisations earn their revenue through time or volume: billable hours, licences sold, cases processed. AI speeds up that work, putting pressure on existing models and opening up different ways to charge. Some consultancies and law firms charge a fixed fee for agreed work or tie part of their payment to a result. Machinery manufacturers sell availability as a service, using predictive maintenance to reduce downtime. Some software providers charge per resolved customer enquiry, alongside or instead of a fee per user. The change reaches across the organisation: how work is budgeted, who carries the risk, and how employees are assessed when hours no longer measure their contribution.

Leadership in a constantly changing organisation depends less on control and more on direction and room to act. Leaders of the future describe how work and the organisation could change, and why people would want to contribute. They listen, ask questions, show vulnerability and give teams authority to decide. Change involves psychology as much as strategy. People change 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 Leadership and Management in the AI Era takes a closer look at this.

Change depends less on technology than on how people see their work. A new mindset means letting go of habits that worked well for years, welcoming outside ideas and seeing uncertainty as a source of innovation. Procedures and routines provide stability and protect quality, yet can also act like an immune system that rejects anything new. A culture of freedom and independence gives employees the confidence to question established methods, try things and learn from mistakes, including where mistakes have serious consequences. At a logistics company, a planner with an idea for better routes can run a trial within a month, without three layers of approval.

Professional knowledge remains the foundation. Curiosity, flexibility, passion and unconventional thinking also matter when an organisation changes quickly. So do AI skills: understanding its capabilities, assessing results and redesigning your work. Educational institutions are adapting their programmes, but organisations often need that knowledge before new graduates arrive. More organisations are taking charge through a future academy, where tomorrow's employees and leaders learn using material drawn from their own workplace. A bank trains customer advisers to become specialists working with AI systems. The result is an organisation of independent team players who keep learning.