The revolution has begun. Not tomorrow, not next year, but now. Artificial intelligence (AI) is no longer a distant sci-fi vision, but a hard reality for Hungarian companies. Deloitte’s recent 2025 “Hungarian AI Landscape” report paints a picture of a country that is throwing itself wholeheartedly into the application of AI. The numbers speak for themselves: an astonishing 85% of the 109 organizations participating in the survey are already actively using some form of AI solution, and another 10% plan to introduce it within a year. This is not just experimentation; money is following the enthusiasm. Eighty-three percent of companies plan to increase their AI spending in the coming year, with 40% expecting significant growth of more than 20
These figures suggest a dynamically developing, mature market. But if we look a little beneath the surface, a much more complex and worrying picture emerges. This large-scale, tactical-level adoption masks a dangerous strategic vacuum. It is as if we were to equip an army with the most modern weapons but forget to provide a general to tell them where to shoot. Most Hungarian companies currently consist of well-armed soldiers who have no battle plan.
This situation creates the illusion of progress. Companies are spending money and introducing tools, which gives the impression of development. But is this controlled, purposeful progress, or just feverish, aimless rushing around? The data in the report suggests that we are confusing mere activity with real results. The real question is not whether companies use AI, but how and, above all, why they do so.
The table below summarizes the most important, often contradictory findings on the state of AI in Hungary at a glance. This will be our compass in the following chapters as we explore the deeper connections.
| Indicator | Data |
| AI Use | 85% |
| Dedicated AI Strategy | 21% |
| Plans to Increase MI Budget | 83% |
| EU AI Act Readiness | Only 16% have a framework in place |
| Main Obstacle | the right use case (44%) |
| Main Success Factor | Selecting the right use case (45%) |
The Great AI Paradox: Everyone Uses It, But Few Know Why
The sharpest message in Deloitte’s report is the gap between tactical, efficiency-focused AI use and strategic, value-creating transformation. Most Hungarian companies currently use AI as a kind of digital steroid: a turbocharged hammer that drives the same old nails, only faster. Instead of using AI to create entirely new architectural designs, they optimize old, familiar processes.
The data reveals this with brutal honesty. When companies were asked what business goals they wanted to achieve with AI, the overwhelming majority of responses focused on operational performance. At the top of the list is “Automation of internal processes” with 69%, closely followed by “More efficient use of resources” with 62%. These are important and useful goals, but they are essentially about cost reduction and fine-tuning current operations.
In sharp contrast, strategic, growth-oriented goals lag at the bottom of the list. “Introducing new business models” scored only 11%, while “Reaching new markets” scored a mere 7%. (This striking imbalance clearly indicates that, in most places, AI is being used not to shape the future, but to ease the burdens of the past.
This tactical short-sightedness is a direct consequence of a strategic vacuum. According to the survey, only 21% of companies have a separate, dedicated AI strategy. Although an additional 12% have incorporated it into other strategies (such as their digital transformation plan), the shocking fact is that 39% of companies have no formal AI strategy at all.(
This situation is a direct path to “pilot purgatory.” Companies without a strategy naturally focus on projects that are easy to justify, low risk, and promise a quick, measurable return on investment (ROI). This perfectly explains the focus on efficiency and automation.(However, while useful, these projects quickly reach their limits. Once you’ve automated your back-office processes, where do you go next without a comprehensive vision? In most cases, the answer is nowhere. Companies launch a series of unrelated, small-scale pilot projects that never scale up and do not lead to fundamental, business-changing transformation.
Meanwhile, the 21% that do have a dedicated strategy are likely to have set themselves more difficult, ambitious goals: building new business models and targeting new markets. This creates a “two-speed” AI economy in Hungary. The vast majority (79%) are optimizing the present, while a small strategic elite is actively building the future. The gap between the two groups will grow exponentially, as the benefits of strategic AI use multiply over time. As the report states, “Without governance, AI is not value creation, but uncontrolled experimentation.”( The majority of Hungarian companies are currently stuck in this phase of uncontrolled experimentation.
The Real Barrier Is Not Technology, But Us: The Human Factor
After examining the strategic shortcomings, the picture becomes complete when we shift our focus from technology to human and organizational factors. The Deloitte report makes it clear: the biggest obstacles to the Hungarian AI revolution are not found in server rooms or lines of code, but in meeting rooms, HR departments, and corporate culture. Hungary’s path to AI is less about technological competition and more about talent management, cultural adaptation, and leadership skills.
According to the survey data, the most significant obstacles are not related to the complexity of the technology. At the top of the list is “Finding the right AI use case” with 44%, followed by “Security and data protection challenges” with 42%, and tied for second place is “Lack of employee skills” with 42%. The key to success is precisely the mirror image of these obstacles. The most critical success factor is “Selecting the right use case” at 45%, followed by “Support from senior management” at 40%.
The report makes a dramatic warning at one point: “The lack of AI talent and AI skills will soon be a greater risk than the technology itself.” This is supported by the finding that 36% of companies consider “training the current workforce” to be the most important area for development. The skills gap is therefore not an abstract problem, but the most pressing operational challenge.
But what does this mean in practice? An extremely important conclusion can be drawn from the data: identifying the right use case is not a one-off task, but a skill. The fact that this is both the biggest obstacle and the biggest success factor carries a deeper message. Finding a valuable use case requires a rare combination of skills: deep business and industry expertise, a realistic understanding of what AI can do, and the creativity to connect the two.
This is not a technical skill; it is a strategic, creative, and analytical skill. The 42% “employee skills gap” does not just mean that there are not enough data scientists. Rather, it means that there is a shortage of “translators”—people who can build bridges between business problems and technological solutions. So the solution is not just to hire more engineers. The solution is to develop the AI skills of existing business leaders, product managers, and process owners, as they are the ones who truly understand the business problems that need to be solved. This, in turn, feeds back into the urgent need for widespread training. (
| The 5 Biggest Barriers to AI Success | Percentage |
| 1. Finding the Right Use Case | 44% |
| 2. Lack of Employee Competencies | 42% |
| 3. Security and Data Protection Challenges | 42% |
| 4. Difficulty in Measuring Return on Investment (ROI) | 35% |
| 5. Lack of Mature Data Management Practices | 29% |
This table can also serve as a kind of diagnostic tool for you. Four of the five biggest obstacles are related to strategy, capabilities, and processes, and only one (security) can be considered partly technological. This is a clear, data-backed argument for focusing on developing internal capabilities.
The Governance Gap: A Ticking Time Bomb Under Hungarian Companies
This chapter is a direct and urgent warning. The Deloitte report has revealed a deep and dangerous gap in the preparedness of Hungarian companies for the EU AI Act and broader AI governance. This problem cannot be treated as a bureaucratic checkbox task; it is a fundamental threat to business continuity, customer trust, and long-term competitiveness.
The numbers are shocking. Thirty-eight percent of Hungarian companies simply do not know whether the EU AI Act applies to them at all. This is not just a lack of information; it is a sign of a systemic failure of legal and compliance oversight. situation is further exacerbated by the fact that only 16% have a developed compliance framework for the AI Act.( 75% of companies lack a transparent AI governance framework that covers the entire AI lifecycle from design to decommissioning.(
And who is responsible for all this? In most places, no one. 64% of organizations do not have an AI governance body or ethics committee to oversee risks. Responsibility either does not exist or is fragmented across different departments, which in practice is equivalent to a lack of responsibility. Ironically, companies recognize the importance of building trust—“transparency in data use is the most important factor for 60% (—but their inaction in governance undermines that very trust.
But why is this issue so critical? The problem goes far beyond potential fines. The first-level consequence is indeed legal and financial sanctions. The second level is the loss of customer trust and reputational damage. If a company cannot prove that its AI system is fair, secure, and compliant, customers will simply go elsewhere.
The third and most strategically critical consequence, however, is that a lack of governance will make it impossible to safely introduce the next wave of AI. How can a company introduce an autonomously acting “Agentic AI” system ( if it has no framework for managing its decisions, assessing its risks, or determining responsibility for its actions?
Governance is therefore not an unnecessary bureaucratic burden; it is the fundamental operating system necessary to run more advanced and autonomous AI systems. Companies that fail to build this will remain stuck at the level of simple, non-critical tools, while their prepared competitors will deploy transformative, autonomous systems. The current governance gap is not just a compliance issue; it is a strategic dead end that limits the future.
The Future Is Here: Agents, Augmented Intelligence, and the Next Wave
Having explored the significant challenges, it is time to look at the enormous opportunities on the horizon. This report is not only about the problems of the present, but also about the promises of the future. We are on the cusp of a paradigm shift toward “Agentic AI” and deeper human-AI collaboration. Companies that can solve strategic, talent management, and governance issues will be able to take a dominant position in this new era.
The report clearly identifies “Agentic AI,” or agent-based AI, as the next big wave. Here, AI will no longer be just a decision-support tool, but an “autonomous partner.” These systems will be able to perform complex, multi-step tasks independently, communicate with other systems, and act proactively to achieve their goals. The report warns that those who are not prepared for this now will be surprised in a few years when their market is completely transformed by new players.
This future is not about replacing the entire human workforce, but about augmented intelligence and collaboration. The report emphasizes the “deepening collaboration between humans and AI,” where human creativity and strategic thinking are complemented by the analytical power and execution capabilities of AI.
Company executives are already sensing this change. When asked which technologies they expect to deliver the greatest business benefits in the next 1-3 years, Generative AI (71%) and Machine Learning (55%) were followed by Agentic AI in third place with 51%, which is critically important. This shows that executives are already looking beyond today’s tools toward more autonomous systems. The explosive spread of Generative AI—already “implemented and in use in some form at 85% of companies (—functions as a kind of gateway drug. It familiarizes organizations with the power and pitfalls of AI, preparing them to embrace more complex technologies.
But what does this mean in practice? Agentic AI will fundamentally rewrite the concepts of “work” and “management.” It automates current AI tasks. Agentic AI will automate entire workflows and decision chains. Imagine a system that monitors supply chain data, predicts a disruption, autonomously searches for a new supplier, negotiates a price, and executes the purchase order—all without human intervention.
This fundamentally changes the nature of human work. Our role shifts from performing tasks to planning the goals of these autonomous agents, setting their limits, and monitoring their results. This also redefines the concept of management. How do you “manage” a team of AI agents? Your role will be a mix of systems architect and ethics officer, ensuring that agents operate within the strategic and ethical frameworks you define. This requires a whole new set of leadership skills, which brings us back to the critical importance of AI literacy and training discussed in Chapter 2.
Your Move: Will You Shape the Future, or Let It Shape You?
The Deloitte report is not a crystal ball, but it is an extremely clear map of the current situation. The paths are clearly visible: one leads to gradual efficiency gains and eventual obsolescence, the other to strategic transformation and market leadership. The decision about which path to take is being made now. The lessons of the report can be summarized in three essential commandments for every Hungarian leader who wants to succeed in the age of AI.
- Go Beyond Experimentation, Build a Strategy!
Stop chasing shiny new technologies. First, define what you want AI to achieve for your company in the long term and build a clear roadmap to get there. Your first step should be to answer the “why” question before deciding on the “what.” Don’t let technology drive your strategy; your strategy should guide your technology decisions. - Don’t Buy Software, Develop Capabilities!
The biggest obstacle is your own organization. Your top priority should be to build AI literacy at every level, from interns to the CEO. Your most valuable AI asset will not be an algorithm, but the people who know how to use it effectively and responsibly. Technology can be purchased, but knowledge and culture cannot. - Consider Governance Not as an Afterthought, But as a Foundation!
Build your AI house on the rock-solid foundations of governance, ethics, and security. Compliance with the EU AI Act is the minimum requirement. True leadership means building a framework that earns trust and enables the safe introduction of powerful autonomous systems in the future. Governance is not a brake, but a prerequisite for safe acceleration.
The choice is yours. The technology is already here. The question is, where will you be in five years? Among the few who shape the market, or among those who are shaped by it?