As some mature generative AI models and agentic AI disrupted the technology landscape in 2025, the New Year is set to witness further disruption. Consulting and research firm Gartner has forecast that AI Supercomputing Platforms, Multi-agent Systems, Domain-Specific Language Models (DSLMs), AI-Native Development Platforms, and Confidential Computing are among the significant trends to be seen in 2026.
“Technology leaders face a pivotal year in 2026, where disruption, innovation, and risk are expanding at unprecedented speed,” Gene Alvarez, Distinguished Vice-President Analyst at Gartner, said.
Top Key Tech Trends in 2026
AI Supercomputing Platform
AI supercomputing platforms integrate CPUs, GPUs, AI ASICs, neuromorphic and alternative computing paradigms, enabling organisations to orchestrate complex workloads while unlocking new levels of performance, efficiency, and innovation.
These systems combine powerful processors, massive memory, specialised hardware, and orchestration software to tackle data-intensive workloads across machine learning, simulation, and analytics.
Gartner predicts that by 2028, over 40% of leading enterprises will have adopted hybrid computing architectures into critical business workflows, up from the current 8%.
Multi-agent Systems
Multi-agent systems (MAS) are collections of AI agents that interact to achieve individual or shared complex goals. Agents may be delivered in a single environment or developed and deployed independently across distributed environments.
(In Generative AI parlance, an agent is an AI system that can autonomously break down a complex goal, make a plan, and use tools (like browsing the web or running code) to take a series of actions to achieve it.)
Domain-Specific Language Models (DSLMs)
Chief Information Officers and Chief Executive Officers are demanding more business value from AI, but generic large language models (LLMs) often fall short for specialised tasks. Domain-specific language models (DSLMs) fill this gap with higher accuracy, lower costs, and better compliance.
DSLMs are language models trained or fine-tuned on specialised data for a particular industry, function, or process.
AI Security Platforms
AI security platforms provide a unified way to secure third-party and custom-built AI applications. They centralise visibility, enforce usage policies, and protect against AI-specific risks such as prompt injection, data leakage, and rogue agent actions. These platforms help CIOs enforce use policies, monitor AI activity, and apply consistent guardrails across AI.
Gartner predicts that over 50% of enterprises will use AI security platforms to protect their AI investments by 2028.
AI-Native Development Platforms
AI-native development platforms use GenAI to create software faster and easier than was previously possible. Software engineers embedded in the business, acting as “forward-deployed engineers,” can use these platforms to work together with domain experts to develop applications.
Organisations can have tiny teams of people paired with AI to create more applications with the same level of developers they have today.
Confidential Computing
Confidential computing changes how organisations handle sensitive data. Isolating workloads inside hardware-based trusted execution environments (TEEs) keeps content and workloads private even from infrastructure owners, cloud providers, or anyone with physical access to the hardware.
Gartner predicts that by 2029, more than 75% of operations processed on untrusted infrastructure will be secured in use via confidential computing.
Physical AI
Physical AI brings intelligence into the real world by powering machines and devices that sense, decide, and act, such as robots, drones, and smart equipment. It brings measurable gains in industries where automation, adaptability, and safety are priorities.
As adoption grows, organisations need new skills that bridge IT, operations, and engineering.
Preemptive Cybersecurity
Preemptive cybersecurity is trending as organisations face an exponential rise in threats targeting networks, data, and connected systems. Gartner forecasts that, by 2030, preemptive solutions will account for half of all security spending, as CIOs shift from reactive defence to proactive protection.
Digital Provenance
As organisations rely more on third-party software, open-source code, and AI-generated content, verifying digital provenance has become essential. Digital provenance refers to the ability to verify the origin, ownership, and integrity of software, data, media, and processes.
Gartner predicts that by 2029, those who fail to invest in digital provenance capabilities adequately will be exposed to sanctions risks, potentially running into the billions of dollars.
Geopatriation
“Geopatriation means moving company data and applications out of global public clouds and into local options such as sovereign clouds, regional cloud providers, or an organisation’s own data centres due to perceived geopolitical risk,” Gartner said.
Cloud sovereignty, once limited to banks and governments, now affects a wide range of organisations as global instability increases.
Published on October 27, 2025
