To no one’s surprise, AI took center stage at the Gartner IT Symposium/Xpo going on this week in Orlando, Fla., where the research firm made its annual presentation of the core technology trends set to shape enterprise IT in the coming year.
“Enterprises need a dedicated AI leader,” said keynote speaker Daryl Plummer, distinguished vice president analyst, chief of research and Gartner Fellow.
“Technology leaders face a pivotal year in 2026, where disruption, innovation, and risk are expanding at unprecedented speed,” said Gene Alvarez, distinguished vice president analyst at Gartner. “The top strategic technology trends identified for 2026 are tightly interwoven and reflect the realities of an AI-powered, hyperconnected world where organizations must drive responsible innovation, operational excellence, and digital trust.”
“What feels different this year is the pace. We’ve seen more innovations emerge in a single year than ever before,” said Tori Paulman, vice president analyst at Gartner.
Here are Gartner’s top strategic technology trends for 2026:
AI super computing platform
Prediction: By 2028, more than 40% of leading enterprises will have adopted hybrid computing paradigm architectures into critical business workflows, up from the current 8%, according to Gartner.
“AI supercomputing platforms integrate CPUs, GPUs, AI ASICs, neuromorphic and alternative computing paradigms, enabling organizations to orchestrate complex workloads while unlocking new levels of performance, efficiency and innovation. These systems combine powerful processors, massive memory, specialized hardware, and orchestration software to tackle data-intensive workloads in areas like machine learning, simulation, and analytics,” Gartner stated.
Domain-specific language models
Prediction: By 2028, more than half of the genAI models used by enterprises will be domain-specific, Gartner expects.
Domain-specific language models (DSLM) fill a gap found in generic large language models with higher accuracy, lower costs, and better compliance. “DSLMs are language models trained or fine-tuned on specialized data for a particular industry, function, or process. Unlike general-purpose models, DSLMs deliver higher accuracy, reliability, and compliance for targeted business needs,” Gartner stated.
“AI agents unpinned by DSLMs can interpret industry-specific context to make sound decisions even in unfamiliar scenarios, excelling in accuracy, explainability and sound decision-making,” Paulman said.
Worldwide end-user spending on generative AI models is projected to total $14.2 billion in 2025, according to Gartner. End-user spending on specialized genAI models, which include DSLMs, is estimated to total $1.1 billion in 2025, according to Gartner.
AI security platforms
Prediction: By 2028, more than 50% of enterprises will use AI security platforms to protect their AI investments, Gartner predicts.
“AI security platforms provide a unified way to secure third-party and custom-built AI applications. They centralize 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 stated.
The popularity of custom-built AI agents is introducing new attack surfaces and risks that demand enterprises adopt secure development and runtime security practices, Gartner stated. “As AI agents’ actions are based on a probabilistic model, they are, by nature, less predictable, making risk management less straightforward. To reap the benefits of AI agents while heading off the uncertainties,” Gartner stated.
AI-native development platforms
Prediction: By 2030, AI-native development platforms will result in 80% of organizations evolving large software engineering teams into smaller, more nimble teams augmented by AI, Gartner forecasts.
AI-native development platforms use GenAI to create software faster and easier than was previously possible. Organizations can have tiny teams of people paired with AI to create more applications with the same level of developers they have today. Leading organizations are creating tiny platform teams to allow non-technical domain experts to produce software themselves, with security and governance guardrails in place, according to Gartner.
Confidential computing
Prediction: By 2029, more than 75% of operations processed in untrusted infrastructure will be secured in-use by confidential computing, Gartner forecasts.
“Confidential computing changes how organizations handle sensitive data. By isolating workloads inside hardware-based trusted execution environments (TEEs), it keeps content and workloads private even from infrastructure owners, cloud providers, or anyone with physical access to the hardware,” Gartner stated. “This is especially valuable for regulated industries and global operations facing geopolitical and compliance risks and for cross-competitor collaboration.”
Preemptive cybersecurity
Prediction: By 2030, preemptive cybersecurity solutions will account for half of all security spending, up from less than 5% in 2024, as CIOs shift from reactive defense to proactive protection, Gartner predicts.
Preemptive cybersecurity technologies use advanced AI and machine learning (ML) to anticipate and neutralize threats before they materialize, according to Gartner. They include capabilities such as predictive threat intelligence, advanced deception and automated moving target defense.
Preemptive cybersecurity is trending as organizations face an exponential rise in threats targeting networks, data, and connected systems. “Preemptive cybersecurity is about acting before attackers strike using AI-powered SecOps, programmatic denial and deception,” Paulman stated. “This is a world where prediction is protection.”
Preemptive cybersecurity solutions will increasingly replace standalone detection and response solutions as the preferred approach to defend against cyberthreats, according to Gartner.
“Preemptive cybersecurity will soon be the new gold standard for every entity operating on, in, or through the various interconnected layers of the global attack surface grid (GASG),” stated Carl Manion, managing vice president at Gartner in a recent report.
Digital provenance
Prediction: By 2029, those who failed to adequately invest in digital provenance capabilities will be open to sanction risks potentially running into the billions of dollars, Gartner predicts.
“As organizations 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. New tools such as software bills of materials (SBoM), attestation databases, and digital watermarking offer organizations the means to validate and track digital assets across the supply chain,” Gartner stated.
Geopatriation
Prediction: By 2030, more than 75% of European and Middle Eastern enterprises will geopatriate their virtual workloads into solutions designed to reduce geopolitical risk, up from less than 5% in 2025, Gartner forecasts.
According to Gartner’s definition, 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 organization’s own data centers due to perceived geopolitical risk. Cloud sovereignty, once limited to banks and governments, now affects a wide range of organizations as global instability increases.
Multiagent systems
Gartner defines multiagent systems (MAS) as 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.
“Adopting multiagent systems gives organizations a practical way to automate complex business processes, upskill teams, and create new ways for people and AI agents to work together,” said Alvarez. “Modular, specialized agents can boost efficiency, speed up delivery, and reduce risk by reusing proven solutions across workflows. This approach also makes it easier to scale operations and adapt quickly to changing needs.”
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,” Gartner stated. “As adoption grows, organizations need new skills that bridge IT, operations, and engineering. This shift creates opportunities for upskilling and collaboration but may also raise job concerns and require careful change management.”
