As artificial intelligence reshapes the architecture of global competition, it is evolving beyond a technological investment into a strategic management domain. Intertwined with data, infrastructure, decision-making systems, and risk governance, AI is increasingly influencing how companies position themselves in a fragmented geopolitical and regulatory environment. Brussels-based Senior Geopolitical Strategist and Certified Board Director Sevinç Kader argues that artificial intelligence should no longer be approached merely as a technology issue, but as a board-level strategic matter directly linked to competitiveness, governance, industrial capacity, and long-term resilience.

As digital transformation accelerates globally, AI is moving from the margins of operational support into the center of institutional decision-making. This transformation is creating not only new opportunities, but also new regulatory, legal, economic, and geopolitical complexities. In particular, the European Union’s risk-based regulatory approach is turning artificial intelligence into not only a technical issue, but increasingly a matter of governance, market access, strategic positioning, and economic power. These themes were also part of recent discussions during Tech Show Frankfurt 2026, where Sevinç Kader spoke on the geopolitical dimensions of AI, infrastructure, and digital sovereignty. We spoke with Brussels-based Senior Geopolitical Strategist and Certified Board Director Sevinç Kader about the rise of corporate AI, the evolving global regulatory landscape, and the strategic implications of the European Union Artificial Intelligence Act.

  • How would you define the concept of corporate artificial intelligence for companies today? How has the use of artificial intelligence in companies evolved recently?

Today, defining corporate artificial intelligence merely as a technology layer is no longer sufficient. Corporate AI is evolving into a strategic infrastructure shaping institutional capacity, operational models, decision-making architectures, risk management systems, customer relations, and competitive speed. In many ways, artificial intelligence is becoming comparable to previous foundational transformations such as electricity or the internet. Looking back years from now, the scale of this shift will likely appear even more profound than it does today. However, the critical issue is not simply whether companies are using artificial intelligence, but how it is being used, with which data, under which governance frameworks, and within what structure of accountability. Many organizations still frame AI primarily as a productivity tool. Yet from Brussels and other major global power centers, the issue is increasingly viewed through a much broader strategic lens. The debate is no longer limited to operational efficiency. It is increasingly about the quality of institutional decision-making, strategic adaptability, and long-term competitiveness. We are moving beyond traditional digital transformation into what increasingly resembles cognitive transformation. The next major shift is not simply the automation of tasks, but the industrialization of decision-making itself. Over the past two years, a visible transition has taken place. In the initial phase, generative AI was largely deployed for supportive and assistive functions. Today, however, a second phase is emerging. Companies are no longer asking whether employees are using AI, but how AI is beginning to shape institutional decision processes themselves. At the same time, most organizations are still in experimentation mode rather than transformation mode. Many companies continue to deploy AI at the edges of operations instead of redesigning workflows, decision architectures, and business models around it. As emphasized in recent economic debates, particularly by Daron Acemoğlu, technological progress does not automatically generate productivity gains. What matters is how technologies are directed and within which institutional structures they are embedded. This shifts the conversation directly toward governance quality, institutional capacity, and decision architecture. From this perspective, the value of AI investments cannot be measured solely by access to technology. It increasingly depends on how effectively these systems are integrated into strategic decision-making frameworks. For this reason, artificial intelligence should not be viewed solely as an information technology issue. It is also a board-level matter influencing capital allocation, organizational structure, risk appetite, industrial positioning, and long-term competitiveness. Artificial intelligence is no longer a standalone technology topic. Boards increasingly need to approach it through interconnected dimensions such as strategic competition and market positioning, exposure to regulatory and geopolitical risk, and organizational transformation together with capability architecture. Increasingly, AI governance is no longer merely a compliance discussion. It is becoming a question of institutional resilience, economic sovereignty, and strategic power.

“Artificial intelligence is no longer merely a technological domain”
  • What are the legal regulations regarding the use of artificial intelligence that are currently on the agenda in Türkiye and around the world?

Globally, artificial intelligence regulation is not evolving from a single center. What is emerging instead is a fragmented but increasingly dense regulatory landscape. The European Union has established one of the world’s first comprehensive risk-based frameworks. The United States continues to pursue a more market-oriented and innovation-driven approach, while several Asian powers are advancing through more interventionist state-led models. This demonstrates that artificial intelligence is no longer merely a technological domain. It is also becoming a competitive arena between different economic, political, regulatory, and geopolitical systems. While Türkiye does not yet have a standalone AI law comparable to the EU AI Act, the broader issue extends far beyond national legislation. Increasingly, companies operating across European markets, including those deeply integrated with the European economic ecosystem, are required to align with EU regulatory standards regardless of where they are headquartered. This is where the so-called “Brussels Effect” becomes strategically important. The European Union does not only regulate its internal market. It increasingly shapes global corporate behavior, market access conditions, and technological ecosystems through regulation. The Brussels Effect is now extending into AI governance. Regulation itself is no longer merely a legal framework. It is increasingly an instrument for shaping markets, industrial ecosystems, strategic dependencies, and geopolitical influence. As a result, alignment with European regulatory standards is increasingly becoming a structural business reality rather than simply a legal preference. Brussels itself is also evolving. It is no longer merely a diplomatic capital. It is increasingly becoming one of the global centers where the operating logic of the digital economy is being politically and regulatorily shaped. Yet many companies still underestimate how profoundly regulatory developments emerging from Brussels influence long-term business models, industrial competitiveness, and global market dynamics. Global competitors, however, have already positioned themselves accordingly. This is not merely about representation. It is about being present where regulatory frameworks are designed, where strategic standards are shaped, and where the future rules of competition are increasingly being defined.

What legal risks can companies face when using artificial intelligence, and what steps should they take to manage these risks?

The risks involved are extensive, and increasingly they are no longer operational in nature, but directly corporate and strategic. The main areas of exposure include data governance and privacy, algorithmic bias and discrimination, transparency obligations, intellectual property disputes, cybersecurity vulnerabilities, and ultimately corporate liability. However, one issue deserves particular attention: under the European Union Artificial Intelligence Act, the cost of non-compliance is becoming increasingly concrete and financially significant. Depending on the nature of the violation, administrative fines may reach up to €35 million or 7 percent of a company’s global annual turnover. Even lower-level violations can result in substantial penalties. This transforms AI risk from a theoretical compliance issue into a direct financial, reputational, operational, and strategic risk domain. At the same time, AI governance and cyber governance are increasingly converging. Every AI system expands the attack surface, while growing AI capabilities simultaneously increase AI-enabled cyber risks. For this reason, the issue is not merely about drafting an “AI policy.” Companies increasingly need robust governance architectures, pre-classified risk frameworks, internal oversight mechanisms, cybersecurity resilience, and board-level monitoring structures. Artificial intelligence is no longer simply a tool being used within organizations. When improperly designed or poorly governed, it can become a major field of institutional responsibility, strategic vulnerability, and reputational exposure. Boards today increasingly face a dual mandate: encouraging bold technological transformation while simultaneously acting as institutional guardrails.

“Europe is increasingly shaping global standards through regulation”
  • What is the European Union artificial intelligence law and what does it cover? Does this law affect companies in Türkiye?

The European Union Artificial Intelligence Act is one of the world’s first comprehensive risk-based AI regulatory frameworks. The Act entered into force on 1 August 2024 and is being implemented gradually. Prohibited practices and core obligations began taking effect in 2025, while comprehensive requirements for high-risk systems will become applicable from 2026 onward. Full implementation is expected to continue progressively through 2027. The key issue, however, is not only the regulation itself, but the scale of its external impact. Any company interacting with the European market is increasingly affected by the EU AI Act, directly or indirectly. This includes economies and companies deeply integrated into European supply chains and market structures, including Türkiye. Here again, the Brussels Effect plays a central role. Europe is increasingly shaping global standards through regulation. However, Europe’s regulatory architecture should not be interpreted only through compliance. Increasingly, regulation itself is becoming part of industrial positioning and strategic competition. Europe is attempting something historically unusual: regulating digital markets while simultaneously trying to build strategic digital capacity. The Digital Services Act, Digital Markets Act, Data Act, and AI Act are increasingly functioning not as isolated regulations, but as components of a broader European digital order. At the same time, Europe itself is engaged in an important internal debate. Recent statements from German Chancellor Friedrich Merz reflect concerns within industrial and business circles that certain regulatory approaches may become excessively restrictive for industrial AI applications. This points to a broader strategic tension inside Europe itself: how to balance trustworthy and regulated AI with competitiveness, industrial scale, innovation capacity, and strategic autonomy in relation to the United States and China. Europe is attempting something historically difficult: remaining open, democratic, and regulation-driven while simultaneously competing in an increasingly hard-power technological environment. This balance is still evolving and is likely to remain one of the defining policy debates of the coming decade.

“The global AI race is no longer purely a competition between models”
  • What awaits companies in the use of artificial intelligence in the coming period?

In the coming years, three major transformation areas are likely to dominate the AI landscape: autonomy, infrastructure, and governance. However, understanding this transformation requires a much broader strategic perspective. Global debates around artificial intelligence are often framed around models and applications. Yet the deeper competition is increasingly being shaped through infrastructure, industrial capacity, energy systems, semiconductors, compute power, and geopolitical alignment. The global AI race is no longer purely a competition between models. It is increasingly a competition between infrastructures, regulatory systems, energy capacity, industrial depth, and geopolitical alliances. Artificial intelligence is becoming an infrastructure issue extending from data centers and energy grids to semiconductor production and capital concentration. AI may appear immaterial, but its foundations are profoundly material. Behind every AI model sits a physical supply chain involving chips, minerals, logistics systems, energy infrastructure, and industrial ecosystems. The AI race therefore has a hard physical layer: semiconductors, energy, compute, minerals, grids, and industrial capacity. Advanced chip design remains heavily concentrated among US-based companies, while manufacturing capacity depends on a limited number of Asian actors. Yet the ability to produce these advanced semiconductors also relies heavily on sophisticated lithography systems developed in Europe. One of the most fascinating examples is ASML in the Netherlands. ASML is effectively the only company in the world producing EUV lithography systems required for the world’s most advanced AI chips. This means that one European company sits at one of the most strategic bottlenecks of the global AI economy. The geopolitical importance of semiconductors today is increasingly comparable to oil in previous decades. Export controls operating through these technological supply chains are no longer merely technical trade measures. They are increasingly instruments of geopolitical power. As a result, companies are no longer functioning solely as economic actors. They are becoming part of broader geopolitical and industrial competition. Recent US restrictions targeting China clearly demonstrate this dynamic. While such measures may limit technological access in the short term, they also accelerate the development of indigenous technological capacity over the medium term.

At the same time, one of the most critical bottlenecks in scaling AI systems is becoming increasingly visible: energy and total compute capacity. AI is no longer only a data and compute race. It is increasingly an energy race. In many respects, megawatts are becoming as strategic as algorithms. Data sovereignty without compute sovereignty is increasingly becoming an illusion. The energy demands of data centers are rapidly approaching infrastructure limits in several regions around the world. Cheap, stable, and scalable energy may become one of the decisive strategic advantages in the AI economy. Consequently, companies are no longer simply choosing technologies. They are increasingly determining which supply chains they will depend on, within which regulatory systems they will operate, and inside which geopolitical architectures they will position themselves. This is transforming artificial intelligence into a geopolitical strategic domain. The implications of this transformation are also explored in our latest strategic note, “Global AI Race,” prepared in Brussels under the European AI Hub framework. Ultimately, the central question facing boards of directors is no longer whether to use artificial intelligence, but at what pace, under which governance structures, and with what level of strategic risk appetite they will manage this transformation. In the coming decade, decisive advantage will increasingly belong not simply to the companies with the most advanced technologies, but to those capable of navigating the intersection of AI, infrastructure, regulation, energy, and geopolitical power.

“Artificial intelligence integration across Europe is accelerating”
  • At what point are sectors in the European Union today regarding artificial intelligence integration, and what kind of strategy are they following?

Artificial intelligence integration across Europe is accelerating, although significant differences remain between sectors and company sizes. Large multinational companies are advancing considerably faster, while adoption among smaller organizations remains more uneven. In industry and automotive manufacturing, AI is increasingly embedded into production optimization, predictive maintenance, quality control, and supply chain management. In financial services, AI has moved closer to the operational core through risk modeling, fraud detection, customer analytics, and algorithmic decision systems. In healthcare and pharmaceuticals, data-driven diagnostics, research acceleration, and AI-supported R&D are advancing rapidly. However, in Europe the issue is no longer merely technological adoption. It is increasingly about strategic positioning. Companies are beginning to approach artificial intelligence not only as a productivity instrument, but as part of their long-term competitive architecture. The future winners may not necessarily be the organizations with the largest number of AI tools, but those capable of redesigning workflows, institutional structures, and decision-making systems around AI most effectively. A visible distinction is now emerging between organizations that use AI as a supportive operational tool and those that integrate it into the architecture of strategic decision-making itself. Increasingly, competitive differentiation in Europe is beginning to emerge through this divide. Europe’s comparative advantage may not ultimately lie in consumer AI platforms, but in trusted industrial AI deployed at scale across advanced manufacturing and industrial ecosystems.