What role does leadership play in successful AI adoption?

AI adoption is often treated as a technology challenge. However, technology alone does not create AI impact; sustained value requires full adoption from the organisation. While 72% of companies are investing millions in AI initiatives, only about one in four achieve tangible value.

In practice, many of the decisions that determine whether it creates value sit with leadership: where to focus, which initiatives to prioritise, how much experimentation to encourage, and what should move from pilot to production.

Our experience working with leadership teams across large organisations suggests that AI adoption works better when executives understand enough about the technology to make informed decisions about it. Enough to assess opportunities, recognise limitations and make informed decisions about where the organisation should invest.

Leaders set direction

Leaders are the catalyst of AI impact, working from individual productivity to organizational value.

Leadership clarifies what the organisation wants to improve and asks practical questions:

• What decision or workflow should change?
• Is AI appropriate for the problem?
• Is the necessary data available?
• How will success be measured?

Without this direction, organisations can accumulate disconnected pilots without creating value.

Executives also influence adoption through their own behaviour. Using AI on real tasks gives leaders a more realistic understanding of its strengths and limitations, making discussions with teams more concrete.

Employees help identify relevant use cases

The most useful applications often emerge from the people doing the work. They understand repetitive tasks, information bottlenecks and workflow constraints that central teams may overlook.

Hands-on experimentation can reveal opportunities in document analysis, information synthesis, data exploration and workflow support. Leadership should give teams room to explore, while creating a clear process to capture, assess, test and scale promising ideas.

Training must lead to practical use

Training alone rarely changes behaviour. Adoption also depends on confidence, access to appropriate tools, relevant use cases and managerial support.

Different groups require different levels of support: foundational knowledge for some, hands-on productivity applications for others, and more advanced work on AI workflows, decision support or process redesign for experienced users.

An assessment-led approach can help tailor these paths by considering AI literacy, role, tool usage, motivation and barriers. Projects based on real business challenges then help participants move from learning to prioritising and prototyping opportunities.

Governance should support experimentation

Leaders need to establish clear rules for tool usage, sensitive information, output validation and human accountability. Practical governance gives employees the confidence to experiment responsibly and clarifies how ideas move from individual tests to organisational solutions.

Adoption should also be tracked through behaviour, tool usage, implemented use cases and business impact.

Leadership’s role is therefore to set direction, model practical use, support experimentation and create the conditions to scale what works. That is what turns AI from a collection of pilots into a capability embedded in everyday decisions and processes.