The available UK evidence does not establish that 60% of workers cannot find time for AI upskilling. A 2024 survey found that 47% of employers cited lack of time as a barrier to AI training, while a separate survey found low worker confidence and little recent training. Those findings point to a workplace access problem as well as a skills gap—but they measure different populations and cannot be combined into a single worker statistic.
What the surveys actually say about time and AI training
The UK Department for Science, Innovation and Technology’s employer survey, fielded from 19 March to 7 June 2024, asked businesses about barriers to engaging with AI-related training or upskilling. Among 801 employers, 47% named lack of time. This is an employer-reported barrier, not the percentage of workers who personally say they cannot find time. The survey was UK-wide, excluded sole traders, and was representative across business sectors except the public sector. Read the employer survey findings.
The separate UK general-public survey asked people in work how confident they felt using AI tools in the workplace and whether they had taken AI-related training. Fielded from 29 February to 7 March 2024, it found that 21% of people in work felt confident using AI tools at work and 84% had not taken AI-related training in the previous 12 months. These are worker-level findings, but the survey did not establish that lack of time caused low confidence or lack of training. Read the general-public survey findings.
Why workplaces may struggle to make room for AI learning
Time is one reported barrier, but it is not the only one. In the same 2024 employer survey, 50% cited uncertainty about what AI training was relevant to their business, and 41% cited cost. These employer responses suggest that training can be hard to schedule when leaders have not decided what skills are needed or how learning fits operational budgets.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsTraining activity was also limited: 11% of employers reported staff undertaking AI training in the preceding 12 months, while 36% expressed interest in future AI training. Interest does not necessarily translate into scheduled learning. If the training is not linked to actual tasks, employers may struggle to prioritize it; if work demands leave no protected time, employees may be expected to learn around their existing workload.
The surveys describe a pattern, not a proven causal chain. The employer and worker findings come from separate studies with different questions and populations, so they should not be treated as directly comparable measures of the same group.
What workers want—and what competent AI use involves
Workers’ reported training interests cover both practical tasks and foundational understanding. In the general-public survey, people in work said they were interested in learning to use AI to find information (31%), understand AI (30%), use AI to automate (30%), and understand AI ethics (29%). These figures describe stated interests, not course completion rates or proof that any particular course works better.
Practical AI competence also includes judging when not to trust an output. Workers identified understanding AI risks and threats, keeping information safe and private, and judging AI accuracy as important workplace skills. That makes a training plan focused only on prompts or tool features incomplete: employees also need guidance on what information they may enter, how to check outputs, and when human review is essential.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →How to make AI upskilling workable at work
- Start with a real task. Identify a recurring activity in the employee’s role where AI might help, such as finding information or automating part of a workflow. This makes it easier to judge whether training is relevant rather than teaching tools in the abstract.
- Set aside protected learning time. Treat training as scheduled work, not an optional extra to fit around a full workload. The employer survey identifies time as a common barrier, though it does not test a particular scheduling solution.
- Teach safe use and evaluation alongside operation. Include privacy and information-handling rules, risk awareness, and a method for checking accuracy before an AI output is used or shared.
- Give learners a chance to practise. Use a role-relevant workflow and let employees apply the guidance to realistic tasks, with a clear route to ask questions or escalate uncertain results.
- Check whether the training transfers to work. Look for appropriate use of the workflow, careful handling of sensitive information, and reliable checking—not simply attendance or familiarity with tool features.
When comparing courses or internal programmes, assess relevance to actual duties, whether learners receive protected practice time, and whether privacy, risk, and accuracy are taught as core skills. The survey evidence identifies needs and barriers; it does not rank courses or establish a best provider.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence can—and cannot—support
The figures support a cautious conclusion: UK employers report obstacles to AI training, and many workers report limited confidence or no recent AI-related training. They do not support presenting “60% of workers can’t find time” as an established statistic. The 47% figure has a different denominator—employers—and answers a different question. The surveys also do not prove that scheduling alone explains the worker skills gap.
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The Department for Science, Innovation and Technology also published a drivers analysis alongside the survey findings, offering additional context on factors associated with AI skills and training.
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