Most boardroom discussions about artificial intelligence begin with productivity. How much time could it save? Which activities could be automated? Where might operating costs fall?

While these are reasonable questions, they only address part of the change taking place. AI systems are moving beyond tools that help an employee complete a task. They are executing work across business processes, monitoring what happens next and deciding when a person needs to step in.

An AI agent might interpret a customer request, check it against company policy, draft a response and pass sensitive cases to an employee. In finance, it could identify anomalies, request missing information, prepare a recommendation and record the reasoning behind it. As these capabilities spread, organisations will begin managing a workforce made up of employees and digital agents. That makes AI a CEO responsibility.

AI is entering the operating model

Many organisations still manage AI through technology teams, innovation programmes or isolated trials. This made sense when adoption centred on chatbots and productivity tools. AI agents, however, create a different management challenge. They can be assigned roles within a workflow, given access to business systems and allowed to act within agreed limits. Their work can affect customers, employees and commercial outcomes.

The central question therefore becomes one of organisational design: how should work be divided between people and machines?

That decision cannot be left to individual departments buying software. A series of disconnected projects may produce local efficiency while creating wider confusion. Teams could automate the same activity in separate ways. Employees may struggle to understand who is responsible when an AI-generated action causes a problem. Leaders may find that technology has reshaped roles before the organisation has agreed what those roles should become.

CEOs need to set the direction before these decisions accumulate and an effective AI workforce strategy should begin with work rather than technology. Organisations should identify where work slows down, where employees spend time correcting avoidable errors and where customers wait because information sits between systems.

This helps leaders understand which activities could be conducted by AI and which require human involvement. If done well, it can lead to partnerships between the two -yielding much better outcomes. The useful questions to consider are: what decision is being made, what information supports it, what could go wrong and who remains accountable? This assessment should be carried out process by process, so a broad objective of automating a department is unlikely to help.

For example, repetitive processing, high-volume monitoring and the preparation of routine materials can be delegated to AI. It can also support work that requires copious amounts of information to be assessed before a person then decides.

Other responsibilities should remain firmly human. Decisions involving significant personal consequences, ethical judgement or sensitive relationships need visible human ownership. The same principle applies when context matters more than pattern recognition or when a customer expects a person to understand the impact of a decision.

Accountability cannot be automated away

When an employee is responsible for decision making, organisations usually know where responsibility sits. Reporting lines, professional standards and management hierarchy provide a framework for accountability which means that digital workers need an equally clear framework.

Every AI agent should have a defined purpose and an accountable business owner. Its access to data and integrated systems should match the work it has been assigned. Also, leaders need to know how its decisions can be reviewed, how exceptions are managed and when its authority should be withdrawn.

This has practical consequences for the board because governance cannot focus only on whether a model is technically accurate. It must consider whether the work being performed is appropriate, whether customers are treated fairly and whether employees can challenge an AI-generated outcome.

A digital worker should never become an invisible participant in a critical process. Customers and employees need to know when AI is influencing a decision, and they also need a reliable route to a person when the system gets something wrong or fails to understand the circumstances.

Trust will depend on how well organisations deal with those moments. A smooth automated experience may save minutes, but a poor response to an exception can damage a relationship built over years.

Human skills will carry more value

As AI takes on more structured work, the contribution expected from people will shift. Judgement will matter because someone must assess whether an answer makes sense in the circumstances, and curiosity will matter because employees need to question what the system has produced. The ability to build trust will remain central wherever work depends on customers or colleagues.

Leadership will also become more important. Organisations will need people who can make decisions when the available evidence is incomplete, accept responsibility and explain their reasoning.

These capabilities should influence recruitment and development now. Waiting until roles have already changed will create a gap between the technology available and the organisation’s ability to use it well.

Employees also need practical experience with AI. Classroom training has a role, but people learn more when they can apply the technology to real work, see its weaknesses and understand where their expertise improves the result.

The strongest adoption programmes will give employees a voice in redesigning their own processes. They often know where work breaks down and which exceptions are hidden behind a simple task.

Measure value, not activity

Many early AI programmes are measured through adoption figures or hours saved. These metrics provide useful signals, but they do not show whether the organisation is performing better. An AI workforce strategy should link investment to business outcomes.

For a customer service process, this could mean faster resolution combined with fewer repeat contacts. In finance, success might include a shorter reporting cycle and better-quality analysis. For employees, leaders should examine whether administrative work has reduced and whether roles now offer more scope for worthwhile contribution.

The human impact matters just as much as the financial outcome. A CEO should be able to celebrate the employee who now has more time to support customers, and the customer whose issue was resolved quickly without being passed from one department to another.

Measurement should also capture negative signals. Rising complaints, frequent human overrides or declining employee confidence may reveal that a digital worker has been given the wrong role. Productivity gains achieved at the expense of trust are unlikely to last.

Strategize for the workforce you want to build

Within five years, access to capable AI systems is likely to be commonplace. Most businesses will be able to buy similar technology, connect it to similar systems and automate many of the same tasks, and competitive advantage will come from organisational choices.

The strongest businesses will know where digital workers create value and where people must remain in control. They will redesign jobs rather than merely remove tasks and will give managers the skills and authority required to oversee a mixed workforce.

This work needs to start before AI agents become embedded across the enterprise. Every CEO should be able to explain who is performing the work, who owns each decision, how exceptions are handled and how value will be measured.

That is what an AI workforce strategy provides. It turns a collection of technology projects into a deliberate plan for the organisation the business intends to become.