The DOBA AI Maturity Index 2026 research shows that companies in Slovenia, Croatia and Serbia are entering an important new phase in their AI journey. Early experiments and individual successes now need to be connected with leadership, organisational practices and everyday ways of working. This will determine whether artificial intelligence becomes a source of lasting business improvement or remains limited to isolated initiatives.
Imagine a company where employees use artificial intelligence (AI) to prepare draft offers, summarise documents and generate ideas for new services. Management supports AI adoption, and some employees have developed advanced skills in using these tools. Does this automatically mean the company is AI mature? Not necessarily. We also need to understand how the quality of AI-generated results is checked, who decides how data is used and whether individual experiences are becoming part of the organisation’s shared knowledge and practices.
At DOBA University of Applied Sciences, we developed the DOBA AI Maturity Index / DAIMI, which assesses organisational maturity in the use of AI. The index combines five areas: strategic integration of AI, AI governance, employee competencies, use in business processes and perceived business effects. It therefore addresses questions that cannot be answered by simply looking at the number of users of individual tools.
The research was conducted between March and May 2026 in Slovenia, Croatia and Serbia. Results are presented on a scale from 0 to 100, with differences in company size structures taken into account when comparing countries. A higher score reflects a stronger organisational ability to integrate AI into everyday work in a structured and meaningful way.
According to the DAIMI 2026 results, Slovenia achieved 50.85 points, Serbia 49.73 points and Croatia 47.89 points.
Slovenia recorded the highest overall result and showed relatively balanced development across the measured areas. Serbia demonstrated strong development momentum and achieved the highest result in perceived business effects. The Croatian sample reflected a more pragmatic and experimental approach to AI adoption. The differences between countries are relatively small, and the broader message is that organisations across the region are gradually moving from experimenting with AI towards more systematic integration.
At the same time, the results should be interpreted carefully. They are based on self-assessments from participating organisations and describe the situation among surveyed companies. Small differences between countries should therefore not be understood as a final measure of business success. For leaders, the more valuable question is where their own organisation has opportunities to improve.
One of the important findings of DAIMI is that AI is still not fully embedded in core business processes. While many organisations express support for AI, this support does not always translate into clear responsibilities, established workflows or shared standards. The development of AI knowledge often still depends on enthusiastic individuals, while companies are working to better connect AI use with measurable outcomes.
I believe this is where leadership responsibility becomes especially important. Leaders need to define what business challenges AI should help solve and create the conditions for employees to develop new skills. They also need to decide who is responsible for reviewing AI results, managing risks and determining when a successful experiment is ready to become part of regular practice. Encouraging employees to use AI is only the first step; organisations also need clear direction and support.
A practical next step is to begin with a specific business process where improvement is needed. For example, preparing offers may involve many repetitive tasks and require significant employee time. Before introducing AI, a company can measure how long the process takes and how often corrections are needed. During a pilot project, it can define which data may be used, who is responsible and how results will be reviewed. After implementation, the company can compare the complete process, including the time required for checking and correcting AI-generated outputs. A faster first draft only creates value if the final result is also better.
This approach helps employees identify where AI genuinely supports their work and where human judgement remains essential. Successful solutions can then become part of agreed organisational practices, while lessons learned can be shared across teams. Over time, knowledge moves from individual experience into organisational capability.
When reflecting on their own maturity, organisations can ask four questions:
I therefore see DAIMI as a starting point for this type of conversation. It helps companies recognise where they already have strong foundations and where further leadership attention is needed. For us at DOBA UAS, the findings reinforce the importance of connecting technological knowledge with leadership skills and an understanding of how people work and learn.
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