Curated insights on artificial intelligence, technology and digital transformation.

AI could increase Poland’s GDP, but technology alone will not be enough

According to a World Bank report, artificial intelligence could increase Poland’s real GDP by between 1.3% and as much as 12.1% by 2035. This represents significant potential, but it is not an automatic scenario.

The key conclusion is clear: access to AI tools alone will not be enough. The final impact will depend on whether companies, institutions and public administration can use AI productively — not only by testing new applications, but by genuinely changing processes, work organization and decision-making models.

At present, only around 8% of Polish companies use AI in at least one business process. This shows that the room for growth is substantial, but also that Poland is still at an early stage of practical AI adoption.

AI may become much more than a tool for automating individual tasks. Its broader importance may lie in improving the productivity of entire organizations: faster information processing, better data analysis, more efficient service design and the reduction of repetitive work.

However, the report also points out that the greatest benefits will appear where technology is combined with investment, managerial competence, education and a stable regulatory environment. AI does not replace organizational maturity. Rather, it reveals which companies and institutions are ready for change, and which still struggle with basic process discipline.

The labor market will be particularly important. AI does not necessarily mean the simple replacement of people by machines. A more likely scenario is a shift in tasks, the movement of employees into new roles and the growing importance of skills related to analysis, quality control, process management and human-AI collaboration.

For this reason, reskilling programs and support for workers during the transition period will be among the key conditions for success. Without them, the benefits of AI may become concentrated mainly among companies and owners of capital, instead of translating more broadly into better jobs and a higher standard of living.

This is an important signal for the Polish economy. AI can become a real source of growth, but only if it is treated as part of a broader transformation: technological, organizational, educational and regulatory.

The biggest mistake would be to assume that buying access to AI tools is enough. The greatest opportunity lies in teaching companies and institutions how to use AI consciously, measurably and responsibly — as a layer supporting productivity, not as another fashionable addition to existing chaos.

Digital Sovereignty, Quantum Technologies and the Infrastructure Behind AI

The article discusses Poland’s role in future technologies, including areas related to security, digital infrastructure and quantum technologies. In the background, it raises an important problem: full digital sovereignty of a single state is becoming increasingly difficult because technologies, suppliers and chains of dependency are international by nature.

This topic is also important for the development of AI. Artificial intelligence requires not only models and applications, but also infrastructure: data centers, networks, access to computing power, secure communication, competencies and trusted suppliers.

AI is often described through the lens of tools: chatbots, language models, image generators or coding agents. Underneath, however, there is an infrastructure layer without which no AI implementation can be truly stable.

Digital sovereignty today does not mean full self-sufficiency. More realistically, it means the ability to consciously manage dependencies: cloud providers, models, data, networks, hardware and regulations.

In this sense, AI is not only a technological topic, but also a strategic one. Organizations and states that want to use AI in a serious way must think not only about models themselves, but also about security, control, infrastructure and long-term digital resilience.

Jeff Bezos and the Opposite AI Scenario: Labor Shortage Instead of Unemployment

The article presents Jeff Bezos’s view that artificial intelligence does not necessarily make people unnecessary in the labor market. According to this perspective, AI may lead not to a lack of work, but to a shortage of people needed to pursue new opportunities.

This approach assumes that technology lowers the barriers to action. If AI makes it easier to create products, services, analyses, content and systems, it may also increase the number of things organizations want to do.

This is an interesting counterpoint to the narrative of mass replacement of humans by AI. In this scenario, AI increases productivity while also creating new areas of work, new needs and new organizational ambitions.

However, caution is still necessary. This scenario does not mean that every role will remain safe. A shift in work is more likely: from performing repetitive tasks to controlling the process, from manually creating content to evaluating quality, from simple coding to designing solutions, and from operational work to supervision, interpretation and decision-making.

The most important question is therefore not whether AI will take jobs, but which parts of work will be taken over by AI and which will become more important for humans.

Panic Around AI May Be More Dangerous Than the Labor Market Data Itself

The article presents a perspective suggesting that there is currently no clear evidence of a massive reduction in white-collar jobs directly caused by AI. At the same time, it raises an important warning: panic around artificial intelligence itself may trigger a wave of reduction decisions.

This is an important distinction. It is one thing for work to be genuinely replaced by AI systems, and another for companies to act under managerial, communication or investor pressure to show that they are adapting to the new technological reality.

The impact of AI on the labor market may therefore be indirect. Employees may not lose their jobs because AI already performs their duties better. They may lose them because organizations are trying to anticipate a trend, reduce costs or send a signal to the market.

This leads to an important question: is a company implementing AI because it has identified a process, metric and goal, or because management expects a quick response to the trend?

In a mature organization, AI should be evaluated through its real impact on the process, quality of output, implementation and maintenance costs, operational risks and impact on team competencies. Without that, it is easy to move from rational automation to organizational panic.

Cybersecurity First, Artificial Intelligence Later

The article highlights an important but often overlooked condition for implementing artificial intelligence in organizations: before a company starts using AI broadly, it should first organize the foundations of cybersecurity.

In practice, this means that AI should not be treated as a fashionable layer added on top of an unprepared organization. If a company already struggles with the security of traditional systems, processes, suppliers, data and access rights, AI implementation may only increase the scale of risk.

Artificial intelligence does not operate in isolation. It is another layer placed on top of the existing organizational architecture: data, processes, systems, permissions and responsibility.

If that base layer is chaotic, AI will not fix the problem. It may accelerate it, hide it or reproduce it on a larger scale. That is why the right implementation sequence should start with data security, access control, process management and clear rules for using AI.

This is a good example of an approach in which AI is treated as an element of organizational maturity, not as a standalone technological miracle.