In our first article, we showed that AI’s impact will be felt most acutely in office work, not just tech jobs. But awareness alone won’t protect your organisation. The companies and regions that thrive in the years ahead won’t be those with the lowest AI exposure — they’ll be the ones that prepare now. Here’s how to start.
The AI disruption everyone’s talking about? You’re probably looking in the wrong place. New research from MIT shows the real impact won’t hit tech jobs first—it’s already reshaping the office and administrative work that exists in nearly every organisation. This article reveals where AI’s hidden risk zone really is. Our next article shows you how to prepare before it’s too late.
What if young people’s phone phobia isn’t a problem at all, but rather a sign that the workplace is changing? When a third of Gen Z avoid phone calls and half trust AI more than their own manager, perhaps algorithmic management isn’t a threat anymore — but a solution to workplace transformation.
The future of work may not be about replacing humans with machines, but multiplying human capacity through digital twins that work simultaneously across multiple tasks. This transformation promises unprecedented productivity gains, but raises profound questions about compensation, ownership, and labour rights when one worker can do the work of five.
AI was supposed to make knowledge work easier and more efficient. Instead, three-quarters of employees report increased workloads, and nearly half admit they have no idea how to extract the promised productivity gains. What went wrong – and more importantly, how can we fix it?
Enlightenment philosophers believed that knowledge liberates. The EU AI Act relies on the same conviction, imagining that an enlightened citizen can identify deepfakes. In the third and final part of this blog series, we examine the transparency paradox and three alternative paths forward.
The EU AI Act seeks to bring order to a field that has transformed into the Wild West of artificial intelligence. Yet its transparency requirements collide with a fundamental question about the boundary between deception and artistic freedom. Humanity has always used artificial means to enhance storytelling, and synthetic media can, at its best, enrich culture, if regulation can distinguish context and purpose from the technology itself.
Algorithms allocate breaks, determine routes, and evaluate performance—management is shifting from human to machine. But what does algorithmic management mean for workers, society, and human leadership in a world where machines increasingly perform supervisory roles with greater efficiency?
