The development of AI in Poland – from curiosity to real business support 

Absolutely! It’s becoming increasingly clear that AI in Poland is no longer just a technological curiosity – it’s turning into a genuine business tool. What’s interesting is that companies are no longer just experimenting with one-off AI projects; they’re starting to invest in it systematically. This is evident in the growing number of budgets allocated to AI, the rising interest in generative solutions, and the fact that large companies – not just in the financial sector – are incorporating AI into their development strategies.

Polish businesses are catching up and are becoming more confident about AI as a core element of digital transformation. It’s no longer just a trend – it’s definitely a direction for growth. 

Well… Just two or three years ago, many companies were only experimenting with AI – testing chatbots or data analysis in one specific area. Today, a growing number of companies are moving towards deeper integration of AI into their business processes. We're seeing cases where AI already supports HR departments, marketing, production, and even research and development. 

It's clear that executive teams are beginning to understand that implementing AI is not just an „IT project” – it's a comprehensive organizational shift. It requires skills, a change in work culture, and often new models of data management. All of this contributes to a shift in perception – fewer companies treat AI as a nice-to-have, and more see it as a critical investment. 

Mainly because AI gives these companies an opportunity to gain an edge in areas where it used to be hard to stand out. In a world of intense competition, where customers expect highly personalized services and faster responses, artificial intelligence becomes a tool that helps automate repetitive tasks and free up human resources for more meaningful work.

Thanks to AI, companies can analyze data faster, automate customer communication, and make decisions based on smart recommendations. And it’s not just for tech giants – even mid-sized and smaller companies are already using AI, for example, to support customer service or manage social media. 

It’s actually quite simple, natural, and has remained the same for thousands of years. The main barrier is the human factor – fear of the unknown. Many companies worry that implementing AI will lead to job cuts, as processes currently handled by employees become automated. Others don’t trust the technology and see AI as a „black box” that’s hard to control. There’s also a lack of skills – not just technical, but also managerial. 

Another issue is data fragmentation, lack of cloud infrastructure, or fear of breaching data protection regulations. Media narratives about the „threats” of AI don’t help either. That’s why it’s worth approaching the topic calmly: start with small, well-planned implementations and clearly defined goals. 

In my opinion, the key is proper preparation – both strategic and organizational. The first step is diagnosis: identify the processes in the company that are the most repetitive, costly, or time-consuming, because those are the easiest to automate. 

Next, build a project team – it doesn’t have to be full of programmers, but it should include people who understand business processes, work directly with customers, and can point out the „bottlenecks”.  

Then comes the time to test the solution on a small scale. It’s important not to start with massive projects – small ones teach you a lot and help you avoid costly mistakes. And let’s not forget about education. Employees need to understand how AI works and what it can do for them, otherwise resistance will inevitably appear. 

Most often in areas where results can be seen quickly – for example, in customer service. Chatbots, voice assistants, systems that automatically respond to emails – all of these work well, deliver real time savings, and improve the quality of customer interactions. Another popular area is data analysis. AI can easily detect patterns, predict customer behavior, and support sales planning. Increasingly, AI is also being used in marketing: for content creation, communication personalization, or campaign automation. These fields are mature enough to offer ready-to-use solutions that don’t require building everything from scratch.

And that’s true – IT is one of those sectors that both develop AI and actively use it. Artificial intelligence supports developers in writing code, automates software testing, helps diagnose bugs faster, and even suggests solutions to technical problems. Tools like Copilot, in versions for developers, can genuinely accelerate team workflows. Importantly, AI is also impacting how IT projects are managed – it can analyze dependencies in the backlog, predict delays, or suggest schedule optimizations. It’s not just about saving time – it’s also a way to improve the quality of digital products. 

Absolutely! IT companies that are already integrating AI into their processes are gaining a real edge in the market. What’s more, many are beginning to offer their own AI-based products or modules, opening up new revenue streams. But the advantage isn’t just about the offering. AI also helps internally: in recruitment, team performance analysis, knowledge management, or building technical documentation. The faster an IT company learns to use AI as a permanent part of its ecosystem, the easier it will be to grow, scale services, and gain the trust of more demanding clients.

Hmm… I think the key is to present the business value – not just the technology. The board doesn’t need to be experts in AI – but they do need to understand how it will impact revenue, costs, risk, and competitiveness. It’s a good idea to prepare a brief analysis: what the business needs are, which areas can be improved with AI, what the costs will be, and what potential returns on investment to expect. It also helps to bring examples from within the industry – ideally ones that show the competition is already taking action. 

Today, AI is a necessity. And most importantly: AI doesn’t have to be a „multi-year project” – it can be implemented quickly, step by step, testing and learning along the way.