Artificial intelligence is changing the rules of innovation faster than companies and countries are used to. If we don’t adapt, we risk being overtaken by the new wave of technologies — but if we master it, it can become the engine of our competitiveness. The key will be prudent deployment of AI, support for small and medium-sized enterprises, building ecosystems, and an emphasis on data security.
AI as a textbook disruptive innovation
Harvard professor Clayton Christensen, author of the Innovator’s Dilemma concept, warned that the risk of falling behind is enormous when a new technology starts to replace the old one. Disruptive innovations do not improve the existing product — they displace it. AI has exactly these traits, because it changes products, services, and processes from the ground up. Those who don’t speed up risk an entire industry — or a country — losing its competitive edge.
The Kodak example shows how quickly a seemingly immature novelty can change the market: the digital camera lingered on the sidelines for a long time, yet ultimately all but displaced film. Generative AI behaves the same way, having become mainstream in a short time. Some estimates suggest that by 2030 AI could deliver up to a 21% net increase in U.S. GDP. Tracking innovations around the world has produced thousands of new cases just in recent months — the revolution is happening now.
Who is leading the race, and where Europe’s opportunity lies
In the race for AI, the U.S. is in the lead, followed by China; the investment figures reflect this as well, estimated at roughly hundreds of billions of dollars in the U.S. and tens to hundreds of billions in China, while individual European countries lag behind. For example, figures often cited are roughly USD 250 billion in the U.S., 95 billion in China, and around 7 billion in Germany. Among the companies with the highest market capitalization, American tech names dominate. Europe will therefore struggle to compete at the level of foundational technology.
Our opportunity, however, lies in how we use AI to create value. Research (e.g., in Harvard Business Review) shows that AI can boost innovation performance across all sectors. In healthcare it supports more accurate diagnostics, personalized treatments, and predictive analytics; in agriculture, precision farming and crop monitoring; in industry, smart factories, predictive maintenance, and quality control. In other words, spend less energy chasing foundational models and more on discovering new AI-based processes, products, and services.
Three priorities: adoption, a new generation of business, secure data
The first priority is transformational adoption: enabling companies and institutions to test and roll out new technologies as quickly as possible. This is not just about pilots, but about systematically embedding AI into the business model, processes, and decision-making. Those who experiment earlier learn faster and increase both efficiency and innovation capacity. We need to incentivize and remove obstacles, not wait for "perfect" plans.
The second priority is to help a new generation of business emerge and extend support especially to small and medium-sized enterprises outside the tech sector. The third is data protection and digital sovereignty: many companies are wary of rules like the Cloud Act and do not want to move sensitive innovation data into environments without clear guarantees. We therefore need solutions with assured security and control over data within the organization or the country. And finally, let’s build ecosystems — connections among clusters, startups, universities, and public institutions — so that we can "innovate our innovation strategies" and maintain competitiveness.
