Articles and webinars

With all the success stories and hype surrounding AI, we often forget that it doesn't magically solve everything. In fact, it's quite bad at a few things. Those of us who've spent enough time wrestling with AI have learned to spot the traps or work around them. But for new users, there are some common scenarios where AI fails spectacularly. Hitting one of these early on can leave you questioning why anyone thought this stuff was useful in the first place.

Right now, there's a gold rush for AI use cases. Some offer companies a sharp competitive edge. Others? They have the potential to lift entire industries.

Lately, I've been watching conversations unfold across industries where professionals seem to compete in who can mock AI the hardest. The tone is usually the same: AI will never be useful in our field, it could never match human skill, and so on. But what if?

The year 2025 looks very much like the year of AI agents. But what is it really about, and why is this development important for organisations?

The preparations for the games have been going on for months, the equipment is at its peak, and the job is all but done. The air is sparkling with excitement and anticipation. The starting gun sounds! But nothing happens. The foot doesn't move, and the runner remains frozen at the starting line, watching the others run. Sometimes the deployment of AI can feel like this without the initial support to turn the initial friction into a a spot-on performance.

All industries are associated with different claims or at least certain perceptions, and IT is no exception. We decided to debug the situation and start a series of articles going through them one by one. In this blog, we'll look at whether it's the job of coders to focus on the programming side of things, rather than getting bogged down in business jargon. Let's have a look!