- AI readiness is about continuous learning, ethical awareness, and practical capability, not simply access to digital tools.
- South Africa’s current AI preparedness remains limited, with serious gaps in workforce enablement and coordinated investment.
- Employers, educators, and workers must treat reskilling and lifelong learning as essential to future employability.
When you hear “AI readiness”, what comes to mind? For many people, it is access to the latest tool, or using the office-mandated licence, or even creating a fun AI image or video. But that is too narrow for the moment we are in.
AI readiness is not only about whether people can access new tools and systems. Recent studies in the era of AI note that employability in the future will depend less on what people learned once, and more on whether they can keep learning as work changes.
A key issue is that too much of the current conversation still mistakes exposure for preparedness. Just because you have access to a tool does not mean you can use and apply it to create value.
Real readiness is more demanding. It includes digital foundations, critical judgment, role-specific capability, ethical awareness, and the right institutional support to keep learning. Research on AI literacy makes this plain. AI literacy is not only about knowing what AI is. It includes using and applying it, evaluating it, and understanding its ethical implications.
In South Africa, an AI Maturity Assessment Framework has been designed to judge national AI adoption preparedness. What we are seeing is sobering. A recent study by the University of the Western Cape, funded by the German development agency GIZ, found that, on the Framework’s Education and Workforce Enablement dimension, South Africans scored only 1.9 out of 5.
This is categorised as “limited” to “emerging.” More broadly, South Africa is assessed as still being in the early stages of AI maturity, with seven of the eight domain ratings sitting at Levels 1 to 2. Progress is described as “fragmented,” reflecting limited coordination, weak targeted investment, and gaps in capability building.
Why workforce learning matters now
That should concern everyone focused on the future of employment, because the education-to-work pipeline is no longer enough on its own. The Framework makes an important point that deserves far more public attention. AI has implications beyond formal education.
It impacts post-school education and the continuing education of the workforce. It also identifies indicators that should matter in any serious readiness conversation. This includes AI vocational training, micro-credentials, and novel continuous professional development.
This means we need to drive organisational spending on digital and AI-related upskilling of the workforce. In other words, readiness is not only about what institutions teach before employment, it is also about whether they, and employers, continue building capability after entry into the labour market.
Workplace evidence reinforces the urgency of this. A 2025 Boston Consulting Group survey shows that AI is now part of daily work, with over 70% of respondents identifying as “regular users.” Yet adoption among frontline employees (those who sit closest to daily operations, service delivery, and routine execution) has stalled at 51%.
The same study finds that 41% of respondents think their jobs may disappear entirely within the next 10 years. The most revealing result is not the fear itself. It is the weakness of the support for workers. Only 36% of employees say they have been trained on the skills needed for AI transformation, and only 25% of frontline employees say they receive sufficient leadership support on how and when to use AI at work.
This tells us something essential about what AI readiness means in practice. It is not a matter of telling workers to catch up by themselves. Readiness is structured. Across corporate, government, and educational settings, readiness depends on whether institutions provide the right tools. It also depends on whether managers can provide clarity and whether training is deep enough to matter.
Let us assume, though, that you get the training needed. You now need to consider what you are going to do with the time saved. If you do not allocate it well, this is a strategic failure. Saving time is not the same as creating value. Value comes when that time is redirected into better work, more strategic work, or deeper learning.
The strategy problem is therefore just as important as the skills problem. A 2025 McKinsey & Company survey finds that workflow redesign has the biggest impact on whether organisations see bottom-line gains from generative AI. Workflow redesign means rethinking work end-to-end instead of adding new tools to old routines. Yet only 21% of respondents say their organisations have fundamentally redesigned at least some workflows.
Preparing people to keep working through change
If employers are redesigning work, then post-school institutions cannot prepare learners only for fixed roles. Institutions across the post-school system must prepare them for changing roles. It also means that continuous professional development can no longer be treated as an administrative add-on. It is becoming part of employability itself.
But what do the AI giants say? Anthropic’s latest Economic Index suggests that people become more effective in their AI use through experience. People in occupations with higher levels of AI use show better outcomes, and the report argues that this pattern is consistent with learning by doing. People become AI-ready through repeated use, learning where tools work, fail, and fit into complex tasks.
This is why the debate now needs more than excitement and anxiety; it needs measurement. One of the strongest recent arguments comes from a Massachusetts Institute of Technology study named Project Iceberg, which insists that every major economic transition eventually requires a new metric. Industrial economies needed measures of physical productivity. Digital economies require measures of online value. The AI economy needs tools that can show where human capability, institutional support, and technological change are aligned, and where they are not.
And that is why South Africa needs more than commentary on AI readiness. We need a way to see it clearly, baseline it honestly, and act on it deliberately. The good news is that we are no longer starting from nothing. There are already tools available to help workers worried about the future. One of these is a tool being tested by the Centre for the Future of Work at the University of Pretoria, which takes 15 to 25 minutes of your time but provides you with free, clear, and personalised feedback on your AI readiness.
The most urgent Workers’ Day question we should be asking in the age of AI is, “Are we preparing people only to find work, or to keep working through change?”
AI readiness should mean more than adoption. It should mean the ability to move from education into work, from work into new forms of work, and from one cycle of reskilling into the next, all while doing so without losing dignity, agency or opportunity.
The Centre for the Future of Work AI readiness tool can be accessed here.
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