2026 will be a special year for technology, marked by disruption and innovation at an unprecedented pace. At least, that is the forecast from analysts at Gartner. The 10 strategic technology trends for 2026 offer companies the potential to drive innovation, strengthen their resilience, and increase confidence in an AI-powered, highly connected world.
Thematically, the spectrum ranges from establishing a secure and scalable digital foundation to orchestrating various technologies to creating proactive security, governance, and digital identity. Companies that embrace the new technologies will have the opportunity to shape their industry for decades to come and massively accelerate business transformation.
Artificial intelligence is fundamentally changing software development. With the help of generative AI, AI-native development platforms are able to create software faster and easier than ever before and increase productivity. Comprehensive technical knowledge is no longer essential for software development. In addition, costs can be saved because small, agile teams with AI support can create a considerable number of applications in a short period of time. Large software engineering teams are no longer necessary.
AI super computing platforms combine high-performance computers, specialized processors, and scalable architectures to easily handle particularly data-intensive workloads. The enormous computing power is a prerequisite for training and operating advanced AI models. Performance, efficiency, and innovation are thus reaching a new level. The growing demand for AI super computing platforms is primarily the result of the emergence of increasingly complex AI models that conventional infrastructures are no longer able to handle.
Strict data protection laws, rigid data localization regulations, and the rapidly increasing use of artificial intelligence require effective protection of the data used. Confidential computing creates the basis for secure cloud strategies and ensures compliance for sensitive workloads. With the help of hardware-based Trusted Execution Environments (TEEs), data is protected during processing and unauthorized access is strictly prevented.
Gartner defines multiagent systems (MAS) as a collection of specialized AI agents that interact and collaborate with each other to perform complex workflows. By assigning each agent a specific task, MAS improve efficiency and scalability compared to monolithic AI solutions. While singular AI agents reach their limits in multi-stage processes, MAS enable modular automation and cross-platform integration.
One of the biggest challenges for companies in relation to artificial intelligence is generating measurable business value from the use of AI. Domain-Specific Language Models (DSLMs) offer enormous potential in this regard. These are AI models that are trained on particular data sets for specific industries, functions, or processes and achieve higher accuracy and compliance than generic large language models (LLMs). Gartner estimates that by 2028, around 60% of AI models used in companies will be domain-specific.
Physical AI brings intelligence to the real world – through robots, drones, vehicles, and smart devices that perceive, decide, and act. These systems combine sensors, actuators, and AI models to automate physical tasks. Companies will benefit from being able to leverage the productivity of digital AI in physical environments in the future.
In the age of AI, cyber threats are also becoming increasingly sophisticated and growing exponentially. Networks, applications, and IoT systems are particularly targeted. Preemptive cybersecurity (PCS) goes beyond traditional detection and response measures by using advanced AI-powered techniques to anticipate, prevent, and neutralize cyber attacks before they occur. According to Gartner’s forecast, technology products without PCS will lose their market relevance by 2029 as proactive defensive measures become a common requirement.
Companies are increasingly exposed to risks from code manipulation, abandoned open-source projects, and disinformation based on deepfakes. Digital provenance serves to verify the origin, ownership, and integrity of software, data, media, and processes. Digital provenance is verified using tools such as bills of materials, certification databases, and watermarks. Digital provenance creates transparency and establishes trust in systems based on third-party components and AI-generated content.
As the adoption of AI continues to accelerate and expand, traditional security tools are no longer sufficient to protect AI-based applications and processes. AI security platforms secure companies’ AI investments and protect both third-party AI services and proprietary AI applications. In particular, they provide protection against AI-specific risks such as prompt injection, malicious actions by AI agents, and data leaks.
Geopolitical tensions and regulatory requirements are leading more and more companies to review and reassess their cloud strategy with regard to potential dependencies on providers. Geopatriation refers to the relocation of data and applications from global hyperscaler clouds to sovereign or local environments with the aim of reducing geopolitical risks for the company.