Imagine your company employing thousands of invisible workers who never sleep, never make errors caused by fatigue, and are capable of making complex business decisions on their own. This is not a science fiction scenario, but a reality that is materialising right now within the world’s largest corporations. Agentic AI - autonomous artificial intelligence systems capable of independent action - is fundamentally changing how enterprises operate, moving from simple support for humans to taking over entire business processes. According to the latest Gartner research, by 2028 as much as 33% of enterprise software will include agentic AI elements, compared with less than 1% today. This shift is not a gradual evolution - it is a revolution that demands an immediate response and a considered strategy from business leaders. Companies that understand and harness the potential of AI agents in the next two years will build a competitive advantage that latecomers will be unable to close.
“We are witnessing a defining moment in the history of enterprise,” comments Jacek Bugajski, CEO of SNOK. “Over the past two years, the business world has been captivated by the possibilities of generative AI, but what is coming with AI agents represents an entirely new level of transformation. We are no longer talking about tools that support people, but about systems capable of autonomously managing entire business areas. Polish companies face a choice: either become leaders of this transformation, or be condemned to compete with organisations operating at a completely different level of efficiency. At SNOK we are seeing clients who took the plunge with their first deployments already achieving results that would have seemed impossible just a year ago.”
Gartner warns against illusions, but sees enormous potential
Gartner analysts present a fascinating, if challenging, picture of the future of agentic AI. By 2027, as many as 40% of AI agent projects will be cancelled due to unclear returns on investment or excessive costs. This is a brutal statistic that should give pause to every board planning investment in this technology. At the same time, those same analysts predict that by 2028, 15% of everyday business decisions will be made autonomously by AI systems, without any human intervention. This apparent contradiction perfectly illustrates the moment we find ourselves in - between enormous potential and real implementation challenges.
Gartner defines agentic AI as systems capable of acting autonomously, making decisions and carrying out complex, multi-step tasks without direct human intervention. This is a fundamental shift from today’s AI assistants, which require constant supervision and detailed instructions. AI agents understand business goals, create action plans, use available tools and adapt their behaviour in response to changing conditions. This can be compared to the difference between an employee who must be told exactly what to do step by step, and an experienced manager who is given a goal to achieve and independently finds a way to accomplish it.
Gartner’s predictions for specific business areas are particularly interesting. By 2029, 80% of typical issues reported to customer service departments will be resolved entirely autonomously, without human involvement, bringing a 30% reduction in operating costs in this area. In supply chain management, by 2030 half of cross-functional solutions will use intelligent agents to coordinate complex logistics and production processes. These figures are not abstract forecasts - they are based on analysis of current pilot deployments at the world’s largest corporations.
Gartner’s most alarming finding is a phenomenon it calls “agent washing” - of the thousands of companies claiming to offer agentic AI solutions, only around 130 actually possess such capabilities. The rest are rebranded chatbots, enhanced RPA tools, or traditional AI assistants that lack genuine autonomy. This is a warning for business decision-makers: verifying the real capabilities of solutions on offer is absolutely critical to project success. Companies that are taken in by marketing promises will lose not only money, but also valuable time and the organisation’s trust in digital transformation.
Major AI players are building armies of digital agents
Anthropic with its Claude model is leading the race to create agents capable of interacting with a computer the way a human does. Claude 3.5 Sonnet was the first publicly available model able to “see” a screen, move the cursor, click buttons and enter text. On the OSWorld benchmark it achieves 14.9% accuracy, which may seem modest, but is nearly double the score of the next-best system in the ranking. Companies such as Replit, Asana and DoorDash are already using this technology to automate complex, multi-step processes that previously required human intervention. Anthropic’s Constitutional AI provides built-in safety mechanisms, reducing the success rate of prompt injection attacks from 23.6% to 11.2%. This shows that the creators of AI agents are taking security and control over autonomous systems seriously.
OpenAI is changing the rules of the game by introducing ChatGPT Agent with computer-use capability and the Operator platform at 199 dollars a month. The system can interrupt a task in progress, accept new instructions and continue working while retaining context - something that was impossible in traditional automation systems. The new Responses API replaces the Assistants API, offering native support for web search, document analysis and Python code execution. OpenAI has cut token costs for GPT-4o by 83% since launch, making advanced AI agents economically accessible to a broader range of enterprises. Companies such as Hebbia, Navan and Unify are already building complex agentic systems using these tools to automate processes that previously required teams of analysts.
“From a technological perspective we are witnessing an extraordinary acceleration in development,” adds Michał Korzeń, CTO of SNOK, responsible for AI, Intelligent Automation and Custom Development. “What was the domain of research labs just a year ago is a commercial product today. At SNOK we have spent the last two years intensively integrating various agentic platforms - from Claude through GPT to our own solutions based on open source. Our advantage is that we are not tied to a single vendor. We can build a hybrid solution combining the best features of different platforms, tailored to the client’s specific needs. For example, for one of our clients in the financial sector we built a system that uses Claude for document analysis, GPT-4 for generating reports, and our own orchestration logic to manage the entire process. The result? A 70% reduction in the time needed to process credit applications, alongside increased accuracy of risk assessment.”
Google DeepMind is betting on a multi-agent approach with its Deep Think system, which uses parallel thinking and reinforcement learning. Gemini 2.0 Flash offers low latency and high performance, ideally suited to applications requiring a fast response. Deep Think reached gold-medallist level at the International Mathematical Olympiad - the first AI system to match the best human minds at solving complex mathematical problems. Google is integrating agentic capabilities across the entire Workspace ecosystem, offering them at no additional cost, which represents a significant competitive advantage over Microsoft. Vertex AI on Google Cloud enables enterprises to build their own agents using pre-trained models and ready-made integration tools.
Microsoft is leveraging its dominant position in office software, integrating AI agents directly with Microsoft 365. Copilot Studio allows agents to be created in a low-code/no-code model, democratising access to this technology. Every SharePoint site automatically receives its own agent, able to answer questions and perform tasks related to the site’s content. Microsoft offers two types of agents: declarative agents, which operate within Copilot orchestration, and agents with their own engine, giving full control over the logic of operation. At a price of 30 dollars per user per month for Microsoft 365 Copilot, the company is making agentic AI accessible to millions of office workers worldwide. Already 70% of Fortune 500 companies use Microsoft 365 Copilot, demonstrating the scale of adoption of this technology.
Amazon Web Services (AWS) approaches the topic from an infrastructure perspective, offering Bedrock AgentCore - a platform for building and deploying AI agents at enterprise scale. The system supports multiple models (Claude, Titan, LLaMA) through a unified API, allowing companies to choose the best model for a given application. AWS has invested an additional 100 million dollars in its Generative AI Innovation Center, supporting companies in building agentic solutions. Multi-agent collaboration, which reached general availability in March 2025, allows teams of specialised agents to be created, coordinated by a supervisor agent. Companies such as Itaú and Box are already running production systems using AgentCore to handle millions of transactions daily.
Meta has chosen the open-source path, making LLaMA 3.1 with 405 billion parameters available as the first open-source model on a par with leading commercial systems. The LLaMA Stack API enables the building of agentic applications, while the llama-agents framework offers a distributed architecture for multi-agent systems. This strategy gives companies full control over their AI agents without dependency on external providers, which is particularly important for organisations in regulated industries or with high data-privacy requirements. Block (Cash App), Accenture and government agencies are already using LLaMA models to build their own, highly customised agentic systems.
Enterprise automation platforms are evolving towards autonomy
UiPath, the RPA market leader, has completely rebuilt its platform around the concept of “agentic automation”, introduced on 30 April 2024. Its philosophy that “agents think, robots execute, people lead” elegantly combines the deterministic precision of RPA with the adaptive intelligence of AI. Johnson Controls saved 18 million dollars and recovered 900,000 hours of work by automating the processing of 6,500 invoices a day using UiPath agentic orchestration. Agent Builder makes it possible to create AI agents in a low-code model that can handle complex processes such as resolving invoice disputes or managing healthcare referrals. Maestro, an intelligent orchestration platform, unifies AI agents, automation, BPM and process intelligence into a single coherent ecosystem. Autopilot uses conversational AI to dynamically build workflows based on natural-language commands, and a self-healing feature allows automations to adapt to changes in user interfaces.
“Integrating RPA platforms with agentic AI is the key to the success of many of our projects,” explains Michał Korzeń. “Most companies have already invested significant funds in process automation and fear that new technology will render those investments worthless. We show how these technologies can complement each other. RPA excels at repetitive, deterministic tasks, while AI agents excel in situations requiring adaptation and decision-making. In one of our recent projects we combined UiPath robots executing transactions in legacy systems with AI agents analysing documents and making business decisions. The effect? A process that previously required 15 people is now handled by 3 people supported by a hybrid automation system.”
Omega Healthcare achieved a 100% increase in productivity and a 50% faster invoice-processing time thanks to AI agents integrated with the UiPath platform. In the insurance sector, one company achieved a 245% return on investment, reducing claims-processing time by 62% through automatic data collection and cross-system verification. In financial services, error-resolution time has been reduced to under 30 minutes thanks to proactive detection and auto-remediation, significantly reducing system downtime. These concrete examples show that agentic automation is not a futuristic vision, but a reality delivering measurable financial benefits.
SS&C Technologies processes 50,000 documents a month using AI agents, achieving over 90% automation. Uber uses agents to summarise customer service interactions and retrieve context, significantly speeding up response times. H&M has deployed virtual shopping assistants that reduce cart-abandonment rates through personalised recommendations and real-time support. Darktrace uses its Antigena agent for autonomous response to cybersecurity threats, neutralising attacks within seconds without human intervention.
The reality of deployments shows both successes and spectacular failures
Market data paints a fascinating, if contradictory, picture of enterprise adoption of agentic AI. According to the latest research, 78% of companies use generative AI in at least one business function, up from 55% a year earlier. However, only 19% have made significant investments in agentic AI, while 42% are investing cautiously. This shows that most organisations are still testing the water rather than diving fully into agentic transformation. Interestingly, 51% of organisations are exploring AI agents and 37% are already running pilot projects, suggesting that the coming year will be pivotal for mass adoption.
But behind these optimistic statistics lies a less pleasant reality. According to the latest MIT Media Lab research, as many as 95% of generative AI pilots in large enterprises fail to pass the testing phase or fail to deliver measurable financial benefits. This is a shocking statistic, particularly given that companies have collectively invested between 30 and 40 billion dollars in these projects. Leadership points to poor project selection, a lack of internal flexibility, and the mistake of trying to build everything in-house instead of integrating proven external solutions.
The case of IBM is particularly instructive and has become a cautionary tale for the whole industry. In 2023, the company laid off 8,000 HR employees, replacing them with an AI system called AskHR. Initial results looked impressive - 94% process automation. Sounds great, doesn’t it? The problem arose in the remaining 6% of cases. These were situations requiring empathy, ethical judgement or emotional nuance - everything that machines still cannot handle.
What happened next? IBM had to use all the savings from the headcount reduction to rehire staff - this time in roles requiring creativity, strategic thinking and engagement in client relationships. As a former IBM executive put it: “We weren’t wrong about what AI can do. We underestimated what only humans can do.” Financially, the whole operation came out even; reputationally, it was a disaster. This is a perfect example of how a superficial focus on numbers (94% success!) can obscure fundamental problems with an AI deployment.
The financial sector, which theoretically leads adoption with a 20% market share in agentic AI for enterprise operations, also has its success stories. Banks using AI agents to draft credit risk memoranda report productivity gains of 20-60%. Digital factories modernising legacy applications are achieving more than a 50% reduction in time and effort. One market research firm achieved over 60% productivity growth thanks to data-quality agents, with expected savings exceeding 3 million dollars a year. These figures show that ROI from agentic AI is not only possible, but can be spectacular with the right application.
SS&C Technologies processes 50,000 documents a month using AI agents, achieving over 90% automation. Uber uses agents to summarise customer service and retrieve context, significantly speeding up response time. H&M has deployed virtual shopping assistants that reduce cart-abandonment rates through personalised recommendations and real-time support. Darktrace uses its Antigena agent for autonomous response to cybersecurity threats, neutralising attacks within seconds without human intervention.
Implementation challenges, however, are just as real as the successes. Fewer than 10% of use cases in specific industries make it past the pilot phase, according to McKinsey. Integration with legacy systems requires costly modifications, and data quality and availability remain a major barrier. 53% of technology leaders cite security as the biggest challenge in deploying agentic AI. Current models are not yet mature enough to autonomously achieve complex business goals, which requires a human-in-the-loop approach to ensure reliability.
Costs also represent a significant challenge. Annual maintenance costs can exceed the initial investment in building the system, which is a fundamental departure from traditional IT. The economic sustainability of applications with heavy usage at scale remains uncertain. The risk of vendor lock-in in a rapidly evolving technology landscape forces companies to be very cautious in choosing technology partners. Organisations must also contend with a skills gap - the need for expertise at the intersection of AI and business processes, which is currently scarce in the labour market.
“The biggest challenge we see among our clients is not the technology, but organisational readiness,” notes Jacek Bugajski. “Deploying agentic AI is not just a matter of purchasing software. It is a fundamental shift in how people think about business processes, organisational structure and workplace culture. We see companies that have invested millions in the technology but have not prepared their teams to work with digital agents. The result? Resistance, fear for jobs, inefficient use of the system’s capabilities. That is why at SNOK we always start with education and building a change-management strategy. Success in agentic transformation is 30% technology and 70% people.”
Forecasts for 2025-2026 - between hype and reality
Growth forecasts for the agentic AI market are, on one hand, staggering, and on the other, raise questions of realism. From 5.4 billion dollars in 2024, the market is expected to grow to 50.31 billion dollars by 2030, at a compound annual growth rate of 45.8%. Alternative forecasts are even more optimistic, predicting growth to 196.6 billion dollars by 2034. The enterprise AI agents and copilots market is expected to grow from 5 billion dollars in 2024 to 13 billion by the end of 2025 - more than doubling in just a year.
But these figures should be read with a healthy dose of scepticism. IDC estimates that global spending on AI infrastructure could reach 1.8 trillion dollars by 2030. This is an astronomical sum, which raises the question - are companies really ready for such investment? Lessons from the dot-com bubble teach us that inflated expectations, premature adoption and a lack of understanding of what is needed to extract real value from technological change can lead to spectacular crashes.
Gartner predicts that 15% of everyday business decisions will be made autonomously by 2028, compared with 0% today. This is a fundamental change in how enterprises operate. 33% of enterprise software will include agentic AI by 2028, up from less than 1% today. However, the same Gartner warns that 40% of agentic AI projects will be cancelled by the end of 2027 due to costs or unclear ROI. This is not a contradiction - it is realism based on experience with previous waves of technology.
Research conducted by Model Evaluation & Threat Research (METR) casts further doubt on optimistic productivity forecasts. In a rigorous test, experienced developers worked 20% more slowly using AI, even though they themselves had predicted a 40% productivity increase. “Nobody expected this result,” admitted Nate Rush, one of the study’s authors. “We didn’t even consider a slowdown as a possibility.”
The infrastructure crisis - the uncomfortable truth about agentic AI
Behind the glossy presentations and promises of revolution lies an uncomfortable truth that the technology industry would rather not discuss loudly. Agentic AI needs gigantic infrastructure, and we simply cannot build it fast enough. According to the Wall Street Journal, data centres alone will drive US electricity demand up by 90,000 megawatts by 2030. That is the equivalent of powering 67 million homes. The problem is that current plans for expanding energy infrastructure cover only 10% of this increase.
Goldman Sachs, in its report, leaves no room for illusion - to meet the energy needs of data centres by 2030, we need to increase energy production by 156% above current capacity. This is not a matter of optimisation or minor improvements. It is a fundamental infrastructure challenge that could halt the entire AI revolution.
Some experts see a solution in small modular nuclear reactors (SMRs). Sounds futuristic and environmentally friendly, doesn’t it? There is just one problem - each such reactor, with a capacity of 10 megawatts, costs 1.5 billion dollars according to the US Department of Energy. If we were to meet projected needs with nuclear energy alone, the total investment would run into the trillions of dollars - and that is just for the energy, not counting data centre infrastructure.
Microsoft, one of the biggest players in the market, is already feeling these constraints. The company has cancelled or delayed several data centre projects, halting investments totalling “several hundred megawatts” in the United States alone. It has similarly slowed or paused discussions about locations in the United Kingdom and Australia. Analysts suggest this is the result of a reassessment of demand and energy availability. This shows that even technology giants are starting to hit a wall of physical constraints.
And who would build all of this? Data centres are massive construction projects requiring skilled workers, who are in dramatically short supply in the market. In Ohio, where new centres are planned, there is a shortage of construction and HVAC specialists. Existing workers are approaching retirement age, and there are not enough younger workers with appropriate experience to replace them. Making matters worse, many of these workers, often immigrants, are being deported, further deepening the workforce crisis.
“I think people who believe we can build and power the infrastructure that AI requires within the next five to ten years are unrealistically optimistic,” says Charles Yeomans, CEO of Atombeam, in an interview for Cyber Protection Magazine. “Municipalities are already blocking the construction of additional data centres. Voters will not tolerate power outages or dramatic increases in energy costs just so big technology companies can deploy their products.”
Growth forecasts for the agentic AI market are staggering. From 5.4 billion dollars in 2024, the market is expected to grow to 50.31 billion dollars by 2030, at a compound annual growth rate of 45.8%. Alternative forecasts are even more optimistic, predicting growth to 196.6 billion dollars by 2034. The enterprise AI agents and copilots market is expected to grow from 5 billion dollars in 2024 to 13 billion by the end of 2025 - more than doubling in just a year.
Gartner predicts that 15% of everyday business decisions will be made autonomously by 2028, compared with 0% today. This is a fundamental change in how enterprises operate. 33% of enterprise software will include agentic AI by 2028, up from less than 1% today. However, Gartner’s warning that 40% of agentic AI projects will be cancelled by the end of 2027 due to costs or unclear ROI is a reminder of the need for a cautious approach.
25% of companies using generative AI will launch agentic AI pilots in 2025. Deloitte forecasts 50% adoption by 2027, and 45% of Fortune 500 companies are actively piloting agentic systems already in 2025. These figures suggest we are at an inflection point, where early adopters will build significant competitive advantage.
Technological breakthroughs driving this growth include improved reasoning capabilities in models such as GPT-o1 or Gemini 2.0 Flash Thinking Mode. Larger context windows and multimodal processing enable agents to handle more complex tasks. Growth in edge deployments for low-latency, real-time decisions opens new possibilities in manufacturing, logistics and IoT.
Venture capital investment in AI start-ups exceeded 100 billion dollars in 2024, of which nearly a third went to AI-related firms. That is an 80% year-on-year increase. In the first quarter of 2025 alone, more than 30 billion dollars was invested. Since 2023, 9.7 billion dollars has gone to start-ups focused on agentic AI, showing where investors see the greatest potential.
Europe and Poland - between ambition and reality
The European market finds itself in a particularly difficult position. On one hand, there are ambitions to catch up with global leaders; on the other, structural problems that are holding back development. Investment in generative AI in Europe is approaching 47.6 billion dollars in 2024 and is expected to grow to 545.48 billion dollars by 2031 at a CAGR of 37.5%. That sounds impressive, but North America still dominates with a 42% market share, while the Asia-Pacific region is growing the fastest. Europe risks being stuck in a technological “middle ground” - too slow to compete with US innovation, too expensive to rival Asian scale.
In Europe, Denmark, Sweden and Belgium are the leaders in AI adoption. Poland is in the group of lagging countries, with only 5.9% of companies (employing more than 10 people) deploying AI. This is a significant gap relative to EU leaders and a warning signal for the Polish economy. Without decisive action, Poland risks marginalisation in a global economy increasingly built on AI. To make matters worse, the shortage of qualified AI specialists in Poland is even more acute than the European average.
The European Union is investing 2.1 billion euros in AI factories across seven consortia. Poland is participating in a consortium led by Finland, alongside the Czech Republic, Denmark, Estonia and Norway. This is an opportunity to close the gap, but it requires active engagement from both the public and private sectors. The EU AI Act, intended to be a competitive advantage for Europe by offering clear rules of the game, could paradoxically slow innovation if it proves too restrictive.
The Polish government has launched a National AI Strategy with a dedicated governance centre. The target of 10% of public procurement budget related to AI is ambitious, but necessary to stimulate the market. The Polish Development Fund supports AI development through loan guarantee and lending programmes. Priority sectors identified are industry, healthcare and transport - areas where agentic AI can bring the greatest economic benefit.
The problem is that ambition and funding alone are not enough. Poland is grappling with the same infrastructure problems as the rest of the world, but on an even greater scale. A lack of data centres, limited energy infrastructure and an exodus of young talent to Western Europe create a difficult barrier to overcome. In addition, a Deloitte survey of 30,252 consumers and employees across 11 EU countries shows worrying statistics: only 44% had used generative AI, 22% were aware of the technology but had not used it, and as many as 34% were unaware it existed. In Poland these figures are even worse.
“Poland has a unique opportunity to seize this moment of transformation and build a strong position in the agentic AI economy,” emphasises Jacek Bugajski. “We have excellent developers, a growing start-up ecosystem and increasing awareness of the importance of AI among business leaders. What we lack is the courage to undertake large, transformative projects. At SNOK we try to act as a catalyst for this change. We show that Polish companies can not only keep pace with global trends, but lead them. The key is partnership between business, technology and public administration. If we act together, we can build Poland as a centre of agentic AI competence in Central and Eastern Europe.”
If you think the infrastructure problems are serious, wait until we look at security issues. AI infrastructure is already one of the most attractive targets for cybercriminals, and the rapid growth of this sector is dramatically outpacing its ability to secure itself. It is like building a plane while flying it - except this plane is carrying the most sensitive business data in the world.
According to the latest Future of Life Institute report, which assessed the major AI platforms (Anthropic, OpenAI, Google DeepMind, X.AI, Meta, as well as China’s Zhipu AI and DeepSeek), the best overall grade was a C+ for Anthropic. OpenAI received a C, Google, X.AI and Meta received a D, and the Chinese platforms failed the test outright. Most worrying of all - every platform failed in the “Existential Safety” category due to security risks and hallucinations.
Key vulnerabilities include weak resilience to adversarial attacks, a lack of explainability standards for high-stakes models, and insufficient tools for real-time oversight of autonomous agents. The problem is compounded by a patchwork of fragmented regulations - the EU AI Act, US sector-specific rules and closed Chinese systems operate in parallel, without global coordination. Enforcement is weak, particularly for open-source and decentralised models.
“53% of technology leaders cite security as the biggest challenge in deploying agentic AI,” the Tumeryk report emphasises. Worse still, the company found that all platforms fail to control hallucinations, and most fail to protect against malicious prompt-injection attacks. These are fundamental problems that remain a major concern for users.
The economic asymmetry between attack and defence is alarming. Using AI for attacks is cheap, fast and easy to replicate. Defence requires a proactive, continuous effort to anticipate, monitor and mitigate unknown threats, at significantly higher operating cost. This inequality is already visible in the growing wave of phishing and social-engineering attacks that exploit the natural linguistic fluency of AI models.
Anthropic and ESET recently announced that they had identified cybercriminal groups using AI models to automate various stages of cyberattacks, including data exfiltration, encryption and the creation of personalised extortion notes. This is not science fiction - it is happening now, and we can barely keep up with the defence.
SNOK as a guide through the labyrinth of agentic transformation
In this complex technological and business landscape, the role of a trusted advisor is becoming critical to the success of enterprises. SNOK, with its deep expertise in combining various technologies and its understanding of the local business context, is ideally positioned to guide Polish companies through agentic transformation. This is no longer about choosing a single tool or platform, but about building a coherent strategy that combines the capabilities of various systems into an effective whole.
The value SNOK can deliver to C-level leaders extends far beyond technical advisory work. It is help in answering fundamental questions: Which processes in my organisation will benefit most from AI agent autonomy? How should investment be balanced between quick wins and long-term transformation? How should the organisation be prepared culturally and in terms of competencies to work with digital agents? How can security and regulatory compliance be ensured while maintaining innovation?
“Our role is not just to deliver technology, but to be a strategic transformation partner,” explains Michał Korzeń. “Every company is different, with its own specifics, culture and legacy systems. There is no one-size-fits-all solution. That is why at SNOK we start with a deep understanding of the client’s business, its goals and constraints. We then design a solution architecture that combines the best elements of various platforms - it might be Claude for document analysis, UiPath for process automation, our own ML models for industry-specific tasks, all orchestrated by our integration platform. The key is that the client is not dependent on a single vendor and can flexibly adapt the system to changing needs.”
SNOK can help avoid the “agent washing” trap through genuine verification of the capabilities of solutions on offer. Thanks to practical knowledge of deployments across various industries, we can point out which vendor promises are real and which are merely marketing. Experience in systems integration allows SNOK to design an architecture that combines existing IT infrastructure with new agentic AI capabilities, minimising the risk and cost of transformation.
SNOK’s role in building bridges between the world of deterministic RPA and the probabilistic nature of AI is particularly important. Many companies have invested significant funds in process automation and fear that agentic AI will render those investments worthless. SNOK can show how to use existing RPA robots as the “hands” for the “brains” of AI agents, creating synergy instead of cannibalising past investment.
In the context of the Polish market, SNOK can help companies make use of available support programmes and European funds. Knowledge of local business and regulatory realities allows the design of solutions that are not only technologically advanced, but also practical and feasible under Polish conditions. SNOK can also act as a bridge between Polish companies and global technology providers, helping negotiate terms and adapt solutions to local needs.
Educating boards and supervisory boards is another area where SNOK can add significant value. Understanding the implications of agentic AI for business models, organisational structures and competitive strategies is essential for making the right investment decisions. SNOK can run strategic workshops that help leaders see both the opportunities and the risks, and build a transformation roadmap tailored to the specific company.
“We see ourselves as the architect of agentic transformation for Polish enterprises,” concludes Michał Korzeń. “We have a unique position in the market - on one hand, a deep understanding of the latest technologies and global trends; on the other, practical experience of deployments in Polish realities. Our team combines experts in AI, automation, systems integration and business transformation. This allows us to see agentic AI not as another technology hype cycle, but as a tool for fundamental business transformation. And most importantly - we know how to carry out that transformation, from strategy through proof of concept and pilot, all the way to scaling and optimisation.”
The time to act is now
Agentic AI is no longer a futuristic vision - it is a reality that is already transforming how enterprises operate around the world. Companies that understand and harness this potential within the next 12-24 months will build a competitive advantage that cannot be closed. Those that hesitate risk not only losing market position, but marginalisation in an economy where autonomous systems become the standard.
Success in the age of agentic AI requires more than purchasing technology. It requires a fundamental rethink of business processes, preparing the organisation to work with digital agents, and building a culture that embraces autonomy while maintaining control. This is a challenge that is both technological and organisational, strategic and cultural.
Poland and Polish companies face a historic opportunity. We can use this moment of transformation to close the technological gap and build a position in the global AI economy. But this requires courage, determination and wise decisions. It also requires partners who understand both global trends and local realities - partners such as SNOK.
The question is no longer “whether” to deploy agentic AI, but “how” and “when”. The answer to “when” is simple: now. The answer to “how” is more complex and requires a considered strategy, the right partners and determination in execution. Companies that take up this challenge with the right support and preparation will not only survive the coming transformation - they will be its beneficiaries and leaders.
“We stand at the threshold of the greatest business transformation since the internet revolution,” concludes Jacek Bugajski. “Agentic AI will change everything - from how decisions are made, to organisational structures, to business models. Companies that understand this change and prepare for it accordingly will dominate their industries. Those that ignore this trend or move too slowly will be pushed out of the market by more agile competitors. At SNOK we are ready to be a partner in this transformation. We have the knowledge, experience and determination to help Polish companies not only survive, but win in the era of agentic AI. The question is: are Polish companies ready to take up this challenge? Our experience shows that more and more of them are answering ‘yes’. And that gives us great hope for the future of the Polish economy.”
The era of agents is just beginning. Those who understand its implications and take action will write the success story of the next decade. The rest will be left merely observing as the business world changes without their involvement. The choice belongs to each of us, but the consequences of that decision will define the future of our organisations for years to come. SNOK is ready to be a guide on this transformative journey, combining global technological knowledge with local business expertise, helping Polish companies not only keep up with change, but become its leaders.