🔒 AI 2026: a maturity test?
Practical implementation, tighter cost tracking and better quality. We’re heading into a year where what matters isn’t AI’s technological promises, but whether it makes sense to use it and whether organizations can actually transform.
After a period of intense experimentation, organizations are cutting back their AI projects to the ones that actually make money or save time. McKinsey data shows that the biggest hurdle isn’t access to models anymore, but weaving AI into how companies’ real operations: its processes, decisions and day-to-day life.
So that means the era of scattered “because everyone’s doing AI” initiatives is over. Only use cases you can run day to day and that make financial sense will stick. That shakeout will drive AI’s growth in 2026.
Share
You might be interested in
-
🔒 The illusion of a digital fortress. How the foundations of security crumbled
Artificial intelligence can already independently bypass two-step verification and generate attacks in fractions of a second. We’ve entered the Machine Speed era, in which even the most advanced defense systems are doomed…
-
🔒 The end of the free silicon illusion. AI priced like electricity
The first half of 2026 is the moment when Silicon Valley stops subsidizing access to artificial intelligence. The magical $20-per-month barrier is no longer a flat rate for everything, and the AI…
-
🔒 The new king. Who’s the most important person in a digital company?
As the EU Artificial Intelligence Act (EU AI Act) approaches full effect, the traditional IT governance model is proving inadequate. Companies are no longer seeking just algorithm creators, but above all their…
-
🔒 The Productivity Paradox 2.0. Where is AI generating profits in 2026?
Enthusiasm for AI is giving way to hard-nosed reckoning. Data from logistics and procurement show that outsized returns on investment appear only where algorithms take autonomous control of processes, not where they…
