A workforce is ready for AI when people know how to use it well, understand the rules, trust how the organisation will treat them, and have roles that make sense once AI does the routine work. Boards should treat workforce readiness as part of AI readiness, with a plan for skills, roles and communication before large-scale deployment.
In a recent interview, LinkedIn co-founder Reid Hoffman discussed AI and jobs. Rather than predict what will happen to employment, this article sets out the AIPEX view of what a board can control: whether its own workforce is ready for the AI it is about to fund.
Why is workforce readiness a board issue?
Because AI investments succeed or fail through people. A tool that staff do not trust, do not know how to use or fear will cost them their job will be used badly, quietly avoided or worked around with unapproved alternatives. The technology budget is wasted, and the risks grow, because unmanaged use is where data leaks and poor decisions happen.
The scale of change is significant. Employers surveyed by the World Economic Forum expect 39% of workers’ core skills to change by 2030, and on the Forum’s figures, if the global workforce were 100 people, 59 would need training by 2030, of whom 11 are unlikely to receive it (World Economic Forum, Future of Jobs Report 2025, January 2025). Those are global averages. The board needs to know what they mean for its own organisation.
What does a ready workforce look like?
We look at four dimensions. A gap in any one of them slows adoption.
| Dimension | Ready looks like | Warning sign |
|---|---|---|
| Skills | Staff can use approved AI tools well and check their output | Training was a single launch session, then nothing |
| Rules | People know what data and uses are allowed, and why | Staff use personal AI accounts for work tasks |
| Trust | People understand how AI will affect roles and believe what they are told | Rumours of job cuts; low uptake of tools |
| Roles | Jobs redesigned around judgement, oversight and exceptions | AI added to old roles without changing expectations |
Where should reskilling effort go first?
To the people whose work will change most, and to managers. Broad awareness training has its place, but the return comes from deeper skills in the roles where AI will do a large share of the routine work: operations, customer service, finance, analysis and software. Those staff need to know how to direct AI, check its output and handle the exceptions it cannot. Skills alone are not enough if the structure around the work stays the same; see Is Your Organisation Built for AI? Redesigning the Operating Model.
Managers need particular attention. They decide whether their teams use AI well, and they are often the people whose own work changes most, as review and reporting tasks shrink. Leaders also set the tone by example; our article on why AI adoption starts in the boardroom makes the case for executives using the tools themselves.
How honest should the board be about jobs?
As honest as it can be. People can tell when they are being managed rather than informed, and uncertainty does more damage than clear news. Where the organisation intends to redeploy people, it should say so and show how. Where it expects some roles to shrink, it should say that too, with a plan for how affected staff will be treated. The organisations that build trust are those whose words match their decisions.
The board’s role is to make sure a workforce plan exists alongside the AI plan, and that the two are consistent. An AI business case that assumes savings from roles nobody has discussed with the workforce is a plan to lose trust. Where employee representatives or works councils exist, the plan should also show how and when they will be consulted.
The board will know whether this is working through a small set of measures reported regularly: the share of staff using approved AI tools each month, the share trained beyond an introduction, the level of unapproved tool use detected, and staff confidence in how AI is being introduced, taken from engagement surveys. Movement in these numbers, tracked quarter by quarter and compared across business units, tells the board more than any launch announcement. Falling use after an initial peak, or rising unapproved use, is an early warning that skills, rules or trust need attention before the next investment is approved.
What should the board ask management?
- Which roles will change most as we deploy AI, and what is our plan for the people in them?
- What share of staff have been trained on our approved AI tools, beyond an introduction?
- Do people know which AI uses and data are allowed, and are they using unapproved tools?
- What have we told staff about how AI will affect jobs, and does it match our business cases?
- How are we preparing managers to lead teams whose work AI is changing?
- How will we know whether the workforce trusts what we are doing?
Where to start
Start by measuring where you stand on skills, rules, trust and roles before the next major AI investment. Our briefing on whether your business is ready for AI and what boards should check before funding it covers the six readiness domains, including digital capability and change. The step on the AIPEX roadmap that fits most boards here is Step 2, Assess: the AI Readiness Assessment (£999 + VAT) scores 30 areas across six domains, with risk flags, a prioritised roadmap and a 60-minute debrief.
Frequently asked questions
How do you know if a workforce is ready for AI?
Check four dimensions: skills to use approved tools and check their output, understanding of the rules on data and use, trust in how AI will affect roles, and roles redesigned around judgement, oversight and exceptions. A gap in any one slows adoption.
Where should AI reskilling start?
With the people whose work will change most and with managers. Broad awareness training helps, but the return comes from deeper skills in roles where AI will do much of the routine work, and from managers who can lead teams through the change.
How open should a board be with staff about AI and jobs?
As open as it can be. Uncertainty does more damage than clear news. Make sure the workforce plan and AI business cases are consistent and communicated. See why AI adoption starts in the boardroom →
Correct as of 28 September 2026. This content reflects the law, official guidance and deadlines as published on that date. The EU AI Act and its guidance, standards and deadlines are still being amended and clarified. If you are reading this after 28 September 2026, check for later amendments, European Commission guidance and harmonised standards before relying on it. It is general information, not legal advice, and AIPEX accepts no liability for decisions taken on it without specific professional advice.