Reskilling for AI: Build Change Capability Before the Tools Arrive

Most people using AI at work were never taught how. The Conference Board’s Skilling for AI study, a global survey of nearly 1,300 workers supported by interviews with 35 enterprise leaders, found that 55% of workers use generative AI or AI agents regularly, while only 33% have taken part in employer-provided AI training. Nearly a third, 28%, say their employer provides no AI training at all.¹ The capability sitting inside most organisations today was assembled privately, from public tools and personal experiment.

The scale of what that capability now has to become is already visible. IBM’s 2026 CEO Study reports that between 2026 and 2028, executives expect 53% of employees to need upskilling to perform their current role effectively, and a further 29% to need reskilling for a different role altogether.² On the estimate of the people running these organisations, more than four fifths of the workforce requires a deliberate change in capability within two years.

What organisations are teaching, and what they are not

Where training does exist, it tends to stop at the foundations. The Conference Board found organisations concentrating on AI literacy and basic prompting techniques, with far fewer helping workers develop advanced capabilities such as managing AI agents, integrating AI into workflows, or applying AI to strategic business challenges.¹

The same pattern appears in what people choose for themselves. Revelio Labs reported that 31% of all newly recorded certifications on professional profiles in June 2026 were AI certifications, and that 47% of those related to generative AI and large language models.³ Individuals are investing in their own foundational fluency at considerable scale, which is welcome, though it produces a workforce that can prompt a model and cannot yet supervise one.

That distinction matters more as agentic systems arrive. Preparing an operating model for agentic AI depends on people who can oversee, question and override a system that acts on its own. Prompting is a skill for using a tool. Supervision is a skill for governing a process, and it is the one the training is not yet reaching.

Why the training that exists does not land

Provision is only part of the problem. The same study found that only 48.0% of workers agree their organisation gives them sufficient time during working hours to build AI capability, and only 47.6% agree they have the tools, access and resources to do so.¹ Training offered without the time to take it is a budget line rather than a capability.

Ownership is also unsettled. SHRM’s State of AI in HR 2026, a survey of 1,722 HR professionals, found the absence of necessary skills named as a primary barrier to expanding AI, while only 15% of respondents supported HR leading on training employees to use AI tools.⁴ The function closest to workforce capability is not widely regarded as the one accountable for building it.

Upskilling is not reskilling

The Conference Board’s central finding is that training investment remains heavily concentrated on upskilling people within their current roles, and that most organisations are not yet preparing for large-scale reskilling.¹ The two are different undertakings. Upskilling assumes the role survives and the person needs more capability within it. Reskilling assumes the role does not survive in its present form and the person needs a route to a different one.

IBM’s figures place roughly three in ten employees in the second category by 2028.² Reskilling on that scale takes longer than upskilling, requires redeployment paths designed in advance, and depends on people believing the destination is real. An organisation that begins it in the quarter the roles change has begun too late.

The South African position

The local starting point sits below the global one. A survey of 426 Western Cape businesses, published in Development Southern Africa, found more than 70% struggling to find workers with the right skills. At least 55% judged their workforce to have only basic digital skills, 70% rated their workforce’s technology skills as poor or very poor, and 52% found it difficult to recruit people prepared for digital transformation.⁵

The same study found most firms spending only 1% to 4% of their wage bill on staff training.⁵ A thinner skills base and a thinner training investment together mean South African organisations have further to travel and less momentum with which to travel it. That is an argument for starting earlier rather than waiting until the tools force the question.

What the capability actually has to cover

Fluency with a model is the entry requirement rather than the objective. Capability for an AI-enabled operating model has to reach further:

• Supervision. Reviewing what a system produces, recognising when it is wrong, and holding the authority to act on that judgement.
• Verification. Knowing how to check an output against something other than its own confidence.
• Escalation. Recognising the boundary of one’s own competence and the point at which a decision belongs to someone else.
• Domain judgement. Knowing the work well enough to notice when a plausible answer is a wrong one. This deepens in value as automation spreads.
• Learning capacity. The tools will change again. Capability built around today’s interface will need rebuilding, whereas the ability to absorb a new way of working will hold.

Building it before the tools arrive

This is work CCG does, and the sequence matters as much as the volume:

• Build the judgement, not only the fluency. CCG Learn develops the confidence and capability people need to supervise, question and override these systems, which is precisely the capability the survey evidence shows to be missing.
• Establish where you actually stand. CCG Analytics measures readiness, adoption and sentiment, so that capability decisions rest on evidence rather than on assumptions about who can already do what.
• Design the roles the capability serves. CCG Consult translates an AI ambition into changed processes, escalation paths and defined human roles, so that training has a destination.
• Settle the governance. CCG Advisory works with Boards and executives on the oversight and accountability model, which determines what people must be capable of deciding.

Reskilling is the half of an AI programme that cannot be bought in the quarter it is needed. Tools arrive on a procurement timetable and capability arrives on a human one, and the second is slower. Organisations that build it before deployment will have people ready to supervise the systems they are given. Those that begin afterwards will be training people to catch up with decisions the technology is already making.

If you are building change capability ahead of an AI programme, we would welcome the conversation. Get in touch with CCG.