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.
Despite tremendous advancements in AI, most organizations still struggle to turn it into real, repeatable value. Many companies experiment with tools like ChatGPT or Microsoft Copilot, run a few pilots, then hit a wall when they try to connect AI to their actual processes and data.
Only 26 per cent of Finns feel they possess sufficient skills to utilise AI – at the same time as the EU AI Act makes AI literacy mandatory. The question is no longer whether personnel should be trained, but how to do it effectively before supervision intensifies in August 2026.
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.
Algorithmic management raises fears, but at its best it can make leadership more equitable than traditional human management. As routine tasks become automated, managers gain time for what truly matters: leading people and thinking strategically.
Algorithmic management raises fears, but what if it could make leadership fairer? A nationwide study reveals that one in two Finnish professionals has already experienced machine-directed management—and younger workers’ attitudes towards it are surprisingly positive.
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.
The European Union is responding to the deepfake crisis with transparency: synthetic content must be labelled and users informed of its use. But is this enough when AI systems themselves don’t know which of their content is true? When laws change slowly and AI exponentially?
For many small and medium-sized enterprises, data security feels both essential and daunting. With limited budgets and technical expertise, where should you begin? And how can you establish a secure operating environment in a systematic way—without costs spiralling out of control?
