AI skills for managers: how to lead a team through AI adoption

A manager’s responsibility extends beyond learning to use an AI tool personally. You also need to decide where it belongs in your team’s work, how people will check its output and whether the overall process improves.

That requires enough AI knowledge to ask informed questions, combined with the management skills you already use: setting priorities, allocating resources and evaluating results.

Start with a small, measurable application. A team pilot will give you more useful information than a broad instruction to “use AI more”.

Learn to select useful applications

Ask your team which tasks consume time, create repeated rework or delay decisions.

Then assess whether AI is a suitable part of the solution.

A repetitive task is not automatically a good candidate. If checking the result takes longer than doing the original work, the benefit may be limited.

Sometimes the better first step is improving the process or source data.

Understand enough AI to question a proposal

You should be able to discuss what a proposed system does, which information it uses and how its performance will be evaluated.

For generative AI, that includes recognising unsupported statements, inconsistent outputs and missing context.

For a predictive model, it may include understanding the target being predicted, the quality of the training data and whether performance holds up on new cases.

You do not need to implement every system yourself. You do need to recognise when a proposal requires specialist input.

Give the team a clear working method

Training should explain what staff are expected to do differently.

For a reporting pilot, define which records may be used, what the generated draft should contain and which checks are mandatory before circulation.

Assign responsibility for the final output. Make it acceptable for staff to reject an AI-generated result or report that the tool is creating additional work.

Otherwise, adoption targets can reward usage while concealing poor results.

Run a four-week pilot

This is a suggested structure for a low-risk pilot using approved information.

For example, a team might trial drafting weekly project updates from approved records.

Measure preparation and review time as well as generation time. Check whether the draft accurately represents progress, preserves unresolved issues and avoids inventing commitments.

A faster draft is useful only if the complete process meets your quality standard.

Choose training for your responsibility

For understanding AI opportunities: Andrew Ng’s AI for Everyone is a non-technical course covering AI concepts, organisational opportunities and working with AI teams. It is relevant to managers who need a foundation for evaluating proposals.

For your own workplace use: Google AI Essentials offers a broader introductory route. Understanding the tools personally can help you assess the training and support your team needs.

For selecting a team programme: Our corporate AI training guide compares delivery approaches. Enterprise training needs differ from buying an individual course.

For further options, see our AI courses for business professionals and managers.

Turn the pilot into a management skill

Write a short decision brief explaining the application, results, costs, limitations and recommended next step.

Include licence costs, training time and ongoing review requirements. Describe who benefits and whether any work has simply shifted to another person.

That gives you a concrete example of managing AI adoption: identifying an opportunity, testing it and making an evidence-based decision.

Compare courses for your current responsibilities and learning goals.