AI training is often seen as the starting point for helping employees work effectively with new technology. But the value of that training is unlikely to come from a single course or intervention.

As AI becomes a more established part of working life, organisations must think about how employees build on what they have learned. This means helping employees apply their skills to real tasks, develop the judgement to use AI effectively and identify opportunities to improve how work gets done.

In practice, this involves giving employees time to experiment, helping teams share successful tools and approaches, and ensuring managers and leaders support ongoing learning.

In this article, we explore how organisations can move beyond initial training to develop the skills and working practices needed to make AI a more effective part of everyday work.

AI capability doesn't stand still

Traditional approaches to training often focus on giving people the knowledge they need to perform a particular task. With AI, that knowledge can change quickly.

New tools introduce different capabilities, existing platforms change how they work and organisations may introduce new policies around AI use. Employees themselves may also discover new ways of applying the technology.

This means AI capability cannot be treated as something that is established once and then left unchanged. Organisations must consider how people continue developing their understanding and adapting their skills as the technology evolves.

Developing AI capability also means building the human skills needed to work effectively with the technology.

Employees need critical thinking to assess AI-generated outputs, judgement to decide when and how to use them, and resilience to keep refining their approach when the results fall short of expectations.

Rather than assuming a tool is broken when it produces an unhelpful answer, they need to recognise when to adjust their instructions, try a different approach or draw on their own expertise to assess the result.

That does not necessarily mean completing formal training on a regular basis. Learning also happens through everyday work, sharing experiences and reflecting on what to try next.

Learning can happen as part of the working day

Some of the most useful learning happens when people are applying new skills to real situations.

In fact, 50% of employees identify on-the-job learning as the most effective way to build AI skills, according to Kaplan's 'From AI to ROI: Who owns the problem?' report.

For organisations, this highlights the value of giving employees opportunities to learn while carrying out their everyday responsibilities. Rather than relying solely on formal courses, teams can build learning into the way they work.

This means giving teams time to explore how AI can help with a particular task, or making space in team discussions to reflect on what has and hasn't worked.

For example, an employee might discover a more efficient way to use an AI tool for a repetitive task. Sharing that approach would help colleagues learn from the experience and adapt it to their own work.

Give people room to explore

Employees cannot always know in advance where AI will be useful. Giving them space to test ideas helps organisations identify opportunities that may not emerge through formal training or centrally defined use cases.

That does not mean giving people unlimited freedom to experiment. Organisations still need clear expectations around appropriate use, data, security and governance. Within those boundaries, however, there is value in allowing employees and teams to try out different approaches.

Line managers have an important role to play by giving employees dedicated time to experiment with AI, apply what they have learnt and explore how it could support their specific roles.

A team might test whether AI could simplify a time-consuming process, an employee could compare different ways of approaching a task or a manager might encourage their team to identify repetitive work that could be reduced. The aim is to move beyond simply trying AI tools and help employees understand how the technology could improve their day-to-day work.

Not every experiment will produce a useful result, and that is not necessarily a problem. Testing an idea can help employees understand the technology's limitations, as well as where it might offer genuine value.

What one team learns can benefit another

As AI becomes part of everyday work, different teams will develop approaches that could be useful elsewhere in the organisation. Making it easier for employees to share what they have learnt and the solutions they have created helps colleagues build on that experience.

For example, one department might develop an AI tool that simplifies a particular process, whilst another could be exploring a similar solution independently. Sharing successful tools helps teams build on existing work rather than duplicating effort or creating multiple versions of the same solution.

Sharing knowledge is also important because using AI effectively requires more than knowing which tools to use. A 2025 study by Jarrahi and colleagues, Generative AI and the augmentation of information practices in knowledge work, examines how generative AI supports information-related work, including evaluating, explaining and communicating information.

The researchers highlight the continuing importance of human expertise in critiquing, contextualising and integrating AI-generated outputs.

This reinforces the importance of employees being able to assess AI-generated information and apply their own judgement. Organisations can support this by encouraging teams to share their experiences, helping colleagues learn from different approaches to using AI effectively.

Organisations can support this by creating clear processes for reviewing, approving and sharing AI tools that have proved useful. This helps establish trusted solutions that employees can reuse, with appropriate oversight of how they are developed and applied.

Internal discussion groups, peer learning and knowledge-sharing sessions can also help employees understand how existing tools work, where they add value and how to use them effectively.

Pay attention to how AI is actually being used

Providing AI tools is only part of the challenge. Organisations also need to understand whether employees are using them effectively and where further support might be needed.

Kaplan's 'From AI to ROI: Who owns the problem?' report found that 45% of HR and learning leaders say AI tools are being underutilised by the teams expected to use them.

This highlights the value of looking beyond whether a tool has been introduced and considering what happens after implementation. Organisations can ask:

• Which tools are employees finding useful?
• Where are teams struggling to get value from them?
• What approaches appear to be working well?
• Are employees finding new applications that the organisation had not anticipated?

The answers can help organisations identify where additional guidance, learning or practical support may be needed.

Managers can help turn learning into a habit

As AI changes how work is carried out, organisations need to continually assess how their processes can evolve to make the most of the technology. Understanding how to use AI is only part of this; knowing how to adapt existing ways of working to realise its potential is equally important.

Managers are well placed to do this because they have regular visibility of how work is changing within their teams.

They should use existing conversations to review how tasks are completed, identify opportunities to improve processes and encourage employees to apply what they have learnt through experimentation. This helps connect AI learning directly to the work itself, rather than treating it as a separate responsibility.

That might involve asking questions during regular team discussions:

• Which tasks are repetitive or involve significant manual effort?
• Are our existing processes as efficient as they could be?
• Where could we adapt our processes to use AI to add value or reduce manual intervention?
• What have we learnt through experimenting with AI that other teams could benefit from?

The role of the manager is not to have all the answers. It is to create space for employees to reflect, challenge existing ways of working and identify where changes or further support may be needed.

Leadership sets the direction

Leadership plays an important role in helping employees understand how AI fits into the organisation's wider direction.

Leaders do not necessarily need detailed knowledge of every AI tool available. Their role is to provide clarity about what the organisation is trying to achieve, how AI fits into that ambition and what employees should expect as ways of working evolve.

They also need to establish the conditions for effective adoption, including clear expectations around responsible use, support for employees who need time to develop their skills, and direction on which AI opportunities matter most to the business.

This does not require leaders to predict exactly how AI will affect every role. Instead, they can acknowledge that the organisation is learning too, communicate what is known and where there is uncertainty, and provide a sense of direction as that picture develops.

Build feedback into the process

An organisation's approach to AI should be able to change in response to what it discovers.

Rather than setting a fixed approach at the outset, organisations should use evidence from their experience to review whether their priorities, support and ways of working are still appropriate.

The process can be thought of as a simple cycle:

Try → learn → share → adapt → try again

Over time, this feedback can help organisations decide which tools to scale, which approaches need refining and where further investment in skills or support is needed. AI capability becomes an ongoing part of organisational development, informed by what employees learn through using the technology.

From AI training to lasting capability

AI will continue to evolve, and so will the ways organisations use it.

For businesses, the question is therefore not simply whether employees have received AI training. It is whether the organisation has created the conditions for people to keep developing their capability as the technology and their work change.

Training can provide the foundation, but lasting capability is built over time through experience, experimentation, knowledge sharing and adaptation.

The technology may keep changing, but organisations that support continuous learning will be better placed to change with it.

Explore Kaplan's AI training and development options to help your organisation build lasting AI capability.

Methodology

The research was conducted by Censuswide, among three samples of:

• 1000 UK employees within transformation-heavy sectors and functions in companies with 50+ employees where AI deployment is active (excludes Business owners and C-Level Execs) between 10.07.2026 - 21.07.2026
• 600 UK HR and L&D decision makers (HR directors, L&D managers, People and Talent leads) with 50+ employees, whose organisation is using AI between 10.07.2026 – 22.07.2026
• 600 (Aged 25+) C-Suite and Director level; in specific sectors; whose organisations use AI; and at companies of 50+ employees between 10.07.2026 – 22.07.2026

Censuswide is a member of the Market Research Society (MRS) and the British Polling Council (BPC), and a signatory of the Global Data Quality Pledge. We adhere to the MRS Code of Conduct and ESOMAR principles