Original LinkedIn edition published
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The short answer
A usage gap does not establish an AI skills gap. Measure foundational knowledge consistently, then examine access to suitable tools, relevant tasks, protected learning time, manager support and clear rules as a separate employer diagnostic. Choose the response that matches the gap you actually find.
Find out whether people lack knowledge, opportunity or both
PwC's 2026 workforce survey reports more AI use alongside less access to needed learning resources. These are self-reported, population-level findings. They show an uneven experience of AI and development, but they do not measure any respondent's AI knowledge or prove that learning access caused a change in use.
A common Core-6 knowledge baseline, delivered by Sova, can identify areas where foundational understanding needs development. It does not measure an employee's opportunity to use AI or score performance in a particular role. An employer can separately ask teams whether they have an approved tool, a suitable task, time to practise, useful feedback and understandable data and escalation rules. Keep those answers apart from the assessment result.
Compare patterns by function or working arrangement before drawing conclusions about individuals. Where knowledge is sound but opportunity is weak, make the work and support accessible. Where opportunity exists but knowledge is developing, target learning and feedback. Where both are limited, address both together. The separate A/A/H and PRIME companion resource can help a team discuss a task, but it is not a Core-6 module or scored exercise.
Illustrative example: two teams with the same low usage
An operations team and a central strategy team both report little AI use. The operations team understands the basic risks but has no approved tool for its documents or protected time to test a suitable task. The strategy team has access and time, but its members are unsure how to check plausible outputs. A single 'low adoption' label would send both teams to the same course. The first needs a usable, governed opportunity; the second needs targeted learning and feedback.
Questions to take into your next meeting
- Separate measures of knowledge from measures of tool use and access.
- Ask each cohort about approved tools, relevant tasks, time, manager support and clear rules.
- Review patterns across functions and working arrangements without ranking individuals on usage alone.
- Assign an owner and a next action for each identified knowledge or opportunity barrier.
Put the idea to work
Run a small knowledge and opportunity review
Choose one cohort with low reported AI use. Review its Core-6 knowledge pattern, if available, and run a separate short opportunity pulse with the five conditions above. Meet with its manager and employees to validate the picture, then remove one practical barrier or offer one targeted learning intervention. Review whether the change helped before widening the programme.
Who might look less AI-ready simply because they have had less chance to learn and apply it?
Sources and further context
- PwC Global Workforce Hopes and Fears Survey 2026 Primary survey findings on AI use, development access and workforce cohorts; based on self-reported responses
Core-6 measures common AI knowledge through Sova. The activities discussed here are separate ways to explore work and behaviour.
What evidence belongs in an AI literacy programme? Try the separate A/A/H and PRIME worksheet See what Core-6 measures