TMT 2026

TMT Global Overview 2026

The global technology, media and telecommunications (TMT) sector continues to evolve at pace, driven by rapid technological advancement and an increasingly dense and complex regulatory environment. In 2026, artificial intelligence sits at the centre of this evolution, acting both as a driver of innovation and as a catalyst for legal, regulatory, and maybe even geopolitical change. Policymakers across jurisdictions are responding by introducing new regulatory frameworks, sharpening enforcement priorities, and pursuing broader public policy objectives such as security, resilience and sovereignty. The differences between the US and European regulatory AI landscape remain striking, although the EU is toning down some of its legislation under the influence of pressure from big tech, lobbying and worries about competitiveness.

Rather than briefly touching upon all regulatory and legal topics that are expected to be of importance in 2026, this introduction focuses on four pressing issues that we predict will stir the TMT sector in 2026. Of course, the full range of topics will be discussed in this Guide by the various experts of each country.

The deep integration of artificial intelligence into TMT products and services

Artificial intelligence continues to move rapidly from a discrete feature to a foundational component of technology, media and telecommunications offerings. In 2026, this evolution will be characterised by the growing use of agentic AI systems, AI-driven pricing and personalisation models, and the widespread embedding of AI functionalities into software, platforms and network operations. Agentic AI refers to systems that are capable of autonomously initiating actions, making decisions, and adapting their behaviour to achieve defined objectives with limited or no real-time human intervention. Within the TMT sector, such systems are increasingly used to manage networks, optimise content distribution, automate customer interactions, and co-ordinate complex digital processes across platforms.

At the same time, AI-driven pricing, recommendation, and personalisation models are becoming central to how digital services are offered and monetised. These systems dynamically adjust prices, rank content, tailor advertising, and personalise user experiences based on large volumes of user data and real-time behavioural signals. In parallel, AI is no longer deployed as a (standalone) tool but is increasingly embedded by design into core software products, cloud services, telecom networks, and media platforms, often operating continuously and invisibly in the background. In the services industry, AI will change the way of working and making money. In other words, in all instances AI rapidly becomes a business model changer, rather than just a tool.

For TMT companies, this deep integration raises complex legal and regulatory questions relating to transparency, accountability, competition, data usage, and consumer protection. The increasing autonomy of AI systems challenges traditional legal concepts of human oversight and responsibility, particularly where decisions are made or actions are taken without direct human involvement. Determining who is legally responsible for the outcomes of AI-driven processes, whether developers, deployers, operators, or users, becomes more difficult as systems gain independence and scale.

The entry into force of landmark technology and AI legislation

The year 2026 marks a critical transition from legislative design to implementation and enforcement of major technology and AI-related laws across jurisdictions. After several years of policy debate and rulemaking, regulators (especially in Europe) are now turning their attention to how new frameworks operate in practice, placing increased emphasis on compliance readiness, supervisory engagement, and enforcement mechanisms. For companies in the TMT sector, this shift means that legal obligations are no longer abstract or future-facing, but immediate and operational.

In the European Union, key frameworks such as the AI Act and the Data Act will begin to apply, fundamentally reshaping how AI systems are developed, deployed, and governed, and how data is accessed and shared within digital ecosystems.

At the same time, the United States and other jurisdictions continue to advance AI regulation through a combination of sector-specific rules, state-level initiatives, and horizontal frameworks, rather than a single comprehensive regime. This approach results in a patchwork of obligations addressing issues such as automated decision-making, algorithmic transparency, discrimination, consumer protection, and cybersecurity. Similar developments are taking place across other regions, as governments seek to regulate AI in line with local policy priorities and legal traditions.

For TMT players operating across borders, compliance increasingly requires co-ordinated legal, technical, and operational strategies to manage overlapping obligations, divergent definitions, and differing enforcement approaches.

Governance, accountability and liability in an AI-driven TMT environment

As artificial intelligence becomes embedded across technology, media, and telecommunications products and services, regulators and courts are increasingly focused on questions of governance, accountability, and liability. AI systems are no longer confined to experimental or peripheral use cases. They are deployed at scale in areas such as network management, content moderation, advertising, customer interactions, pricing, and decision-making processes that have direct legal, economic and societal impact.

In 2026, regulatory and judicial attention is shifting toward the question of who bears responsibility when AI systems cause harm, produce discriminatory or unlawful outcomes, or fail in critical operational contexts. This issue is particularly acute where AI systems operate with a high degree of autonomy, adapt their behaviour over time, or rely on complex models that are difficult to explain or audit. Traditional liability frameworks, which assume clear chains of human decision-making and control, are increasingly strained when applied to AI-driven processes.

As a result, authorities are examining not only the outcomes of AI systems, but also the governance structures surrounding their development and use. This includes the allocation of responsibilities between developers, deployers, operators, and users, the existence of effective human oversight mechanisms, the quality of risk assessments and testing procedures, and the adequacy of monitoring and incident response processes throughout the AI life cycle. For TMT companies, establishing clear accountability frameworks and defensible governance models is becoming essential to managing legal exposure, responding to regulatory scrutiny, and maintaining trust in AI-enabled products and services.

This trend affects how TMT companies design governance structures, allocate responsibilities internally, and structure contractual relationships with customers, partners and suppliers. Clear lines of accountability, effective human oversight mechanisms, and robust risk management frameworks are becoming essential to managing legal exposure and maintaining trust in AI-enabled technologies.

Technology and digital sovereignty as a core public policy objective

Governments around the world are placing growing emphasis on technology and digital sovereignty, driven by concerns relating to national security, economic resilience, and strategic autonomy. This trend manifests in a range of policy measures aimed at reducing dependency on foreign technologies, strengthening domestic digital infrastructure, and exerting greater control over critical data, cloud services, and, eg, AI capabilities. In many jurisdictions, sovereignty objectives are reflected in localisation requirements, restrictions on the use of certain foreign technologies, and increased state involvement in the development and protection of strategic digital assets.

At the same time, approaches to technology and digital sovereignty are not uniform. In the United States, recent policy initiatives, including executive action by the President, have sought to balance national security considerations with a strong emphasis on innovation, competitiveness, and regulatory restraint. These initiatives explicitly caution against the proliferation of fragmented or overly prescriptive rules that could stifle technological development or undermine global leadership in AI. As a result, US policy has tended to favour guidance, risk management frameworks, and targeted safeguards over comprehensive, economy-wide regulation.

Furthermore, the definitions of digital sovereignty differ. For example:

• the legal and operational control over digital infrastructures, data and systems;
• the ability to decide and act autonomously on essential digital aspects of the long-term future in the economy, society and democracy; and
• exclusive local law jurisdiction over data and systems.

The definitions that exclude every piece of US hardware and software may well result in zero sovereign systems being available in the current market.

For the TMT sector, these differing approaches have far-reaching implications. Companies face increased scrutiny of supply chains, potential restrictions on cross-border data flows, localisation and resilience requirements, and heightened review of foreign investment and technology sourcing decisions. At the same time, they must navigate a global environment in which sovereignty-driven regulation may be pursued aggressively in some jurisdictions, while others prioritise flexibility and innovation. Balancing regulatory compliance, operational efficiency, and global scalability has therefore become a central strategic challenge for TMT companies operating across borders.

Conclusion

The TMT sector has entered 2026 at a moment of significant transition. Artificial intelligence is no longer a peripheral innovation and tool, but a foundational element and business model changer of digital infrastructure and services, bringing with it new regulatory, legal and geopolitical challenges. The convergence of AI integration, the application of new legislation, sovereignty-driven policy objectives, and heightened expectations around governance and accountability are reshaping how TMT businesses operate and manage risk.

This Guide is intended to support legal practitioners and businesses as they navigate this evolving landscape. Experts from their respective jurisdictions provide insight into a broad range of TMT-related topics, including artificial intelligence, the digital economy, cloud and edge computing, telecommunications, media services, technology contracting, trust services and digital identity, and the gaming industry. By understanding both the legal frameworks and their practical implications, stakeholders can better position themselves to respond to regulatory change and to operate responsibly in an increasingly AI-driven TMT environment.