Artificial intelligence is becoming increasingly present in Polish businesses. Companies are testing AI tools, looking for automation opportunities and planning further implementations. The problem is that simply having access to the technology does not yet mean that an organisation is ready to use it effectively.
The LUQAM report on AI maturity in Polish enterprises shows where businesses stand today and what will determine the success of future implementations.
What determines AI maturity in an enterprise
The average level of AI maturity among the companies surveyed was 1.81 on a four-point scale. As many as 80% of organisations are at a basic or intermediate level, while only 6% have reached an advanced level. Among industrial sectors, the automotive industry has the highest score, with an AI maturity index of 2.28.
Company size does not always have a decisive impact on AI maturity. Much more important factors are employee competencies, training, AI development strategy and the ability to measure the results of implementations.
This is an important insight for companies planning investments in artificial intelligence: technology is only one element of the transformation. People, processes, change management and measuring results are equally important.
Ambitious plans, but a long way ahead
Polish enterprises have increasingly ambitious AI plans. 26% of the companies surveyed report advanced AI development plans for the next 2–3 years, while only 12% already have a formal AI competency development strategy in place.
Currently, AI most often serves as an employee assistant. Only 7% of organisations allow AI to autonomously perform specific tasks. This shows that many companies are still at the early stage of testing AI tools and remain a long way from fully integrating AI into their business processes.
ChatGPT is just the beginning
ChatGPT remains the most popular AI tool – approximately half of the surveyed companies use it. At the same time, 21% of companies do not yet use AI in any form.
A much more promising direction, however, is the development of proprietary solutions and connecting AI models with internal company data. Meanwhile, more than 60% of companies do not use RAG technology (Retrieval-Augmented Generation), which enables AI to work with company documentation, procedures, knowledge bases and other data sources.
This is where some of the greatest potential for development lies. AI that understands an organisation’s context can become much more than a universal chatbot – it can become a tool supporting everyday work and decision-making.
AI in industry: from documents to production
The use of artificial intelligence is increasingly extending beyond text generation and document analysis. Companies are planning to use AI in areas including production, quality control, planning, maintenance, logistics, sales, procurement and R&D.
The most frequently cited benefits include better use of data and decision support, cost reduction through automation, and increased production efficiency and flexibility.
For industrial companies, this represents another step in the development of production process automation – from automating individual operations to solutions that use AI for data analysis, process optimisation and decision support.
The biggest challenge? Connecting technology and competencies
The report shows that the main barriers to AI development are not limited to funding. Companies more often point to challenges related to technology integration, as well as a lack of the necessary competencies and organisational readiness.
This is an important conclusion for industry as well. AI does not operate independently of existing infrastructure. It needs to work with control systems, databases, ERP, MES, SCADA and other solutions used within the plant.
Effective AI implementation therefore requires a holistic view of the entire process – from data and infrastructure, through automation and software, to the way people use the results.
AI as the next element of industrial transformation
Polish enterprises are only beginning to build their AI maturity. The coming years may, however, bring a shift from individual experiments to AI solutions integrated into production processes.
For industry, this means another stage of digitalisation: bringing automation, robotics, data and artificial intelligence together into a single ecosystem supporting production efficiency, quality, flexibility and competitiveness.