Build AI capability by tying learning to real work: identify what each role needs, offer an appropriate learning path, give employees bounded projects to practise on, and protect time for learning and feedback. Mentors or peer champions can help people apply new skills, but the available evidence does not establish a best mentoring cadence or prove a specific performance gain.
Start with the work employees need to do
Do not begin by assigning everyone the same advanced AI course. First identify tasks where AI may help, then define the knowledge and judgment employees need to develop, implement, manage, or interact with AI systems. The UK Department for Science, Innovation and Technology defines AI skills in those broad terms in its research evidence and methodology.
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Translate that definition into role-specific needs. An employee who uses an approved AI tool in daily work needs a different learning path from someone configuring a system or overseeing its use. Focus on the task, the decisions the employee must make, and the support they need to do it responsibly.
Choose a learning path that fits each role
Internal AI development can combine formal instruction, workplace learning, and informal learning. The UK government’s evidence programme treats employer-led training as including in-house and workplace-based learning connected to roles or tasks, rather than limiting it to a course. Its evidence and methodology report also considers both formal and informal learning.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesA structured, modular pathway can help employees progress from foundational knowledge to more specialized skills. The government’s employer guide gives modular learning pathways as an example and emphasizes training that is practical, usable, inclusive, and sustainable. A learning platform may supply useful material, but it should complement manager support and hands-on work—not replace them.
Turn learning into bounded workplace projects
Give learners a small, real task where they can practise applying what they have learned. Set clear boundaries: what the project is meant to improve, what tools or data may be used, who reviews the output, and what would count as a useful result. Keep the scope manageable enough for employees to get feedback and learn without making an untested workflow a dependency.
Projects tied to real tasks follow the evidence programme’s emphasis on workplace-based learning, but a particular project format is a practical design choice, not a universally tested model. Use the work to surface questions and gaps in understanding, then adjust the learning path or project scope accordingly.
Make time and support part of the plan
Learning time needs to be treated as work, not an optional activity employees must fit around an already full workload. The UK government’s 2026 executive summary identifies limited time and staff pressure among employer-reported barriers to AI upskilling, alongside cost, unclear provision, and fear of failing in technical areas. These findings support planning for capacity and psychological safety; they do not establish a universally effective number of learning hours.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →- Agree with managers when learning and project work will happen.
- Check in regularly on workload, obstacles, and whether the project remains appropriately scoped.
- Make it acceptable to ask basic questions, report an unsuccessful attempt, and revise an approach.
Use mentorship for questions and feedback
Pairing a learner with an experienced colleague or peer champion is one practical way to provide timely feedback, help interpret course material, and share lessons from workplace projects. Make the mentor’s role specific—for example, reviewing an approach or helping the learner find the right internal guidance—so that support is useful rather than an undefined extra responsibility.
The evidence cited here does not establish an optimal mentor-to-learner ratio, meeting cadence, or measured causal benefit for AI mentorship. Treat mentoring as a support option to test against your organization’s needs, not as a proven intervention with a guaranteed outcome.
Review capability and adapt the programme
Check whether employees can apply what they learned to their assigned work, where they still need help, and whether the project produced a useful and appropriately reviewed result. Use those observations to update role-based pathways and project choices as tasks and AI tools change. The government guide stresses practical and sustainable training, but the sources do not prescribe a single measurement framework.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the UK figures do—and do not—show
UK survey figures provide context for why internal development matters, but they should not be read as current global rates. In a survey of 801 UK employers, with fieldwork from 19 March to 7 June 2024, 31% said they currently used AI and 11% said staff had undertaken AI training in the prior 12 months. The training figure was 48% among employers with AI specialists or implementers. These results appear in the Department for Science, Innovation and Technology’s employer survey findings.
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The department’s 2026 evidence programme drew on 23 workshops, 10 case studies, and a survey with 536 responses. Its executive summary also reports that over 44% of surveyed organisations use AI tools daily, but the summary does not provide enough detail to state a sample denominator here. That figure should not be generalized to all organizations. The broader evidence supports linking learning to workplace roles and tasks; it does not quantify the causal effect of mentorship, a particular project design, or a set amount of protected learning time.
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