From AI Hype to Business Value

AI has advanced dramatically in a short period. How would you describe the current landscape?  

– We’ve reached a point where the technology is accessible to everyone. You can no longer say, ‘We’ll wait until it gets better.’ AI is already here, and companies that want to stay ahead must act now. At the same time, we still see many struggling to generate real business value from their AI initiatives. 

Companies that were quick to experiment with AI are now facing a new challenge: how do we operationalize what we’ve learned? Many have tested AI in isolated projects, but moving from pilots to an integrated strategy is entirely different. It requires a thoughtful plan, the right skill sets, and a solid understanding of how AI can drive the business forward.

What are the most common reasons AI initiatives fail to deliver? 

– I see four typical causes. First, there’s too much focus on the hype rather than solving real business problems. Many are attracted to AI’s potential but fail to connect it to their operations. Second, AI often becomes a goal in itself – companies say, ‘We need to use AI’ without a clear reason why. 

The third pitfall is false AI maturity. Just because a company has licenses for AI tools doesn’t mean it’s AI-driven. Using a chatbot or a code assistant doesn’t mean AI is integrated into the business. Finally, many get stuck in the experimentation phase. They test AI, but the models are never fully operationalized and therefore don’t generate long-term value.

How can companies avoid these traps and instead generate real business value? 

– The most important thing is to start with the business, not the tech. Often we hear companies say they want to ‘work more with AI,’ but the real question should be: what business problem are we trying to solve? A good approach is to work backward – start with a clear need, identify what data and technology are needed to solve it, and then build step by step.

So it’s better to start small than to go big from the beginning? 

– Exactly. A common misconception is that AI requires huge investments before delivering value. But AI works best when you start small, solve a real problem, and build from there. Companies that work iteratively and take small, value-driven steps move forward much faster than those trying to build a perfect AI solution from day one.

How can AI transform business models and create new revenue opportunities? 

– This is a fascinating area that many still underestimate. AI is often seen as a tool to automate processes and increase efficiency – but it can also enable entirely new business models. 

A great example is treating data as a business asset. Companies are sitting on massive amounts of valuable data, but few have a clear strategy for monetizing it. Some are starting to package their data and sell insights to partners and customers or use AI to create more personalized offerings in real-time. 

A concrete case is in retail, where data on purchasing patterns and product preferences can be refined with AI and become part of a new offering. In manufacturing, we see similar trends – AI is used to analyze production data and improve maintenance, but those same insights can also be offered as customer service. AI makes it possible to forecast demand, optimize resources, and even create new subscription models where customers pay for insights and data alongside the actual product.

Trends and the future – what’s next? 

– AI is evolving rapidly, but a few areas stand out. Multimodal AI models that can process text, images, video, and voice seamlessly will be a game-changer. Self-learning AI that can generate its training data will also push development forward. Additionally, we’re seeing an increase in AI regulation, particularly from the EU, which will influence how companies can use AI.

What does the new AI legislation mean for businesses? 

– Many underestimate how quickly the regulatory landscape is shifting. The EU AI Act sets higher requirements for transparency, safety, and responsible AI use. That means companies must think through their AI efforts from the start and ensure they’re compliant. 

It’s also important to understand that AI regulation isn’t just a legal issue – it’s a business strategy issue. Companies integrating AI governance and risk management early on will have a clear competitive advantage moving forward.

If you could give one piece of advice to companies wanting to succeed with AI, what would it be? 

– Make sure AI initiatives are driven by business value, not tech enthusiasm. Start with a clear problem, work step by step, and ensure your AI solutions are truly operationalized. AI can potentially revolutionize businesses – but only if we use it the right way.

Read more from Sofie in an interview with Breakit (in Swedish).  

Want to learn how we at HiQ help companies turn AI into business value? Contact us here!