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This week, Mozisha founder Dr. Kenechukwu Ikebuaku published a commentary on UCL Institute for Global Prosperity's The New Alignment, the policy hub feeding into the UK's 2027 G20 Presidency. It's a direct response to a provocation from Gabriela Ramos and Emilija Stojmenova Duh, two of the leading voices in the digital sovereignty debate, and it makes an argument that sits at the center of what Mozisha has been building since 2020.
Nine in ten employees now use AI at least sometimes. Only one in six feels fully prepared to use it well. That's the headline finding from Study.com's 2026 State of AI Jobs and Skills Report, drawing on two combined 2026 employee surveys totaling roughly 2,000 respondents, and it sits inside an even sharper pattern buried in the same data.
McKinsey's newest global survey, published August 2026 and drawn from 1,719 executives across 97 countries, found that 80% of individual workers report real productivity gains from AI. Only 37% of organizations report any positive impact on enterprise-level earnings, essentially flat from a year earlier. Just 6% qualify as genuine "AI high performers," attributing 5% or more of EBIT to AI with significant impact. That gap, between what individuals feel and what shows up on the balance sheet, isn't a story about whether AI works. It's a story about who's using it right.
For decades, a company's org chart was also its theory of work. Growth, Revenue, Product, Operations, each box a function, each function a place where a certain kind of task lived. If you needed something done, you knew which department to walk into. That theory is breaking, not because the work disappeared, but because the boxes were never really about the work. They were a proxy for something else: where judgment happened to concentrate.
On International Youth Day this year, MTN launched an AI-powered Job Board across the eleven markets where its Skills Academy operates. The feature does something the platform hadn't done before: it connects a learner's training record directly to job listings, flags the skills gap between what a candidate has and what a role requires, and routes them to a free course to close it.
For the past two years, the loudest claim about AI and work has been simple: AI is coming for entry-level jobs first. Junior roles are the easiest to automate, the story goes, so they will be the first to disappear. The data looks contradictory at first. Some of the largest recent studies show entry-level hiring rising at companies that use AI seriously. Another finds employment for young workers in AI-exposed occupations falling sharply. Both are true. The contradiction disappears once you look closely at what AI is actually doing inside these jobs, not to the jobs as a whole, but to the tasks inside them.