The AI bubble takes us from hype to practical use – but is your company ready?

We've seen it before with the internet and smartphones. When the technology bubble bursts, we begin to feel the real value, because the hype is over and the technology becomes tangible and usable. AI is no exception. The initial fascination has gradually subsided, and now it's about translating AI potential into concrete benefits. But how? We'll take a closer look at this as we give you three practical steps to shift gear from testing to scalable AI solutions.     

The expectations, investments and hype surrounding AI have gathered such momentum that we've moved into what some would call an AI bubble. However, not a bubble in the sense that we'll be left with nothing on the other side. Because even though unrealistic demands are sometimes placed on what AI should be able to do, it doesn't mean that AI should be written off as a trend. Quite the opposite.    

Amazon founder, Jeff Bezos, has expressed this very aptly. He said that what we're dealing with is a technology bubble and not a financial bubble. The difference between the two is that whilst a technology bubble leaves behind usable and valuable technology, a financial bubble will simply let value evaporate.  

So even though AI is a bubble, we'll be left with useful technology on the other side. As we've seen many times before, new technology at some point moves into hyped bubbles, which is a sign that something big is coming, but that the market is rushing ahead of the technology. Think of the internet bubble in 2000, and when mobile apps took us by storm in 2010. After the bubble came maturation, and then the real practical value.   

So when we look back at the history of technology, what we're experiencing right now is actually quite normal. The first wave of a new technology is about fascination, the next is about utility. And that's where we are now. We've moved from AI as a gadget (and miracle worker) to AI as co-pilot. Many already find it hard to see how they managed to do their work without AI on the sidelines.  

It happens in the small everyday things, when we ask ChatGPT to write this year's Christmas card or structure and merge our notes into comprehensible subheadings. But it also applies to larger automations and concrete solutions that both save us time and enable the development of services and products we couldn't live without. And that's an important point when we need to consider what it means that AI may be a bubble.   

An AI strategy is therefore not about implementing technology for technology's sake. It's about creating clear competitive advantages, freeing up employee resources for more value-adding tasks and positioning the business for the market of the future.  

The biggest barrier to AI adoption isn't the technology – it's often the culture. If employees don't understand how AI can help them, or if they feel threatened by it, even the best solutions will fail. That's why early action is needed.  

What it requires: 

The businesses that succeed with AI are those where employees actively use and contribute to the development. This requires AI not to become an IT project, but to gradually become an integrated part of the organisation's DNA.  

Pilot projects give you insights into the potential, but the next wave is about building solutions that genuinely create value in production. This requires professional solution design, integration with existing systems and a clear plan for testing and implementation.  

What it requires: 

Businesses that ultimately succeed with scalable AI solutions will, through deep integration of AI in the organisation, be able to make faster and better data-driven decisions. But it requires both technical expertise and business understanding – and the ability to bridge the two worlds.