Catching up in the AI race: What Czech companies can learn from global adopters

Are Czech companies AI leaders or AI laggards? It’s an important question for Adastra, a Prague-based Data & AI professional services company, to evaluate as they help clients worldwide innovate and reduce costs through data and AI solutions. 

For David Kalab, Vice President of Data Management at Adastra, the case for adoption is straightforward: AI delivers real business value through higher revenue potential and lower operating costs. Yet Czech firms remain cautious. 

“Here in Central Europe we tend to be more conservative, with higher risk aversion, while in the U.S. there’s greater appetite to innovate faster,” Kalab said. Slightly over 11 percent of Czech firms used AI in 2024, below the European average of 13.5 percent, according to a Eurostat survey. (A Czech Press Agency report put the figure at 60 percent, highlighting how different methods of measurement can produce conflicting results.)

Scale also matters. Because the U.S. market is so much larger, companies there tend to be bolder in rolling out initiatives—and they have the funds to do so.

As Kalab and his colleagues point out, the challenge for Czech companies is not deciding whether to adopt AI, but finding the right way to do it. That means overcoming structural gaps in investment and collaboration, and ensuring leadership can guide employees through rapid change.

Privacy laws and regulations may slow down AI adoption across Europe, but in the Czech case, there are other obstacles.

“Compared to countries like Germany or the Netherlands, we are falling behind in investments, infrastructure, and the connections between companies, universities, and startups,” explained Ondřej Vaněk, head of the AI Division at Adastra, in a company blog post.

Vaněk, however, was slightly skeptical toward statistics concerning Czech AI adoption. His experience has shown that all Czech companies are experimenting in some way with AI, but they’re just not categorizing these experiments as adoption or bold, new projects. 

For Czech companies, the question is not whether AI is being explored, but whether these experiments will translate into the kind of large-scale adoption needed to keep pace with Europe’s innovation leaders.

If structural challenges slow adoption, leadership vision determines whether firms can overcome them. Kalab noted that the pressing question for companies is not whether to adopt AI, but how to responsibly approach AI integration.

Leadership vision, not technical know-how, is the key to AI adoption that boosts a business, he observed.

“Many employees see it as a threat to their jobs, so leadership needs to actively shape a culture that helps people embrace AI rather than fear it,” Kalab said. To combat this fear, he suggested leaders build organizations that can absorb disruption and embrace the kind of radical reinvention that AI adoption requires.

What does it mean for a leader to pivot during the AI revolution? They must have the vision and dexterity to dismantle silos, foster innovation, and institutionalize sustainability as a core strategic objective.

Sustainability means not just reacting to employee attitudes, but creating them. To promote consensus over AI within an organization, Kalab said leaders need to encourage individuals across the hierarchy to take risks and become innovators, even if it’s just for their own projects.

Recent research on AI adoption supports Kalab’s message. As an article in the Review of Managerial Science points out: “Leaders must revolutionize their roles and skills to exploit the full potential of AI and integrate it into the business decision-making process effectively. This includes fostering a culture of continuous learning and psychological safety, where employees are empowered to experiment with AI tools without fear of failure.”

But employees also need to take ownership of their own AI progress. “Look for ways AI can make your own job easier, your team more efficient, and your processes smarter. You don’t need to change the whole company to have an impact; small, local steps matter.”

However, many companies tend to use a “blanket approach” to AI adoption and take on too much at once, Kalab argued. “That approach is an obstacle for successful AI adoption.”

On the road to AI adoption, he continued, there will always be business and technical hurdles. “Sometimes the data you thought existed isn’t actually there, or the technology requirements are very different from what manufacturing firms have relied on for the last twenty years.”

Without these steps, Czechia risks falling further behind its European peers, missing out on the business value that AI is already delivering elsewhere.