Gartner has published its forecast of strategic technology trends for 2027. Its essence can be summed up in one phrase: business is moving from chats with AI to autonomous agents that carry out tasks themselves. But without oversight, quality data and a proper security system, such agents will quickly turn from assistants into a source of risk.
The forecast contains no single "magic" technology. Gartner identifies three interconnected directions: AI moves beyond text, companies learn to manage autonomous systems, and the technology environment becomes more flexible and less dependent on individual vendors.
AI is learning to work with numbers, voice and reality
Conventional language models write texts well but are weaker at precise calculations and modeling. That is why Gartner is betting on large quantitative models (LQM). They are built for numerical data, complex equations, forecasts and operational scenarios.
According to the forecast, by 2030 LQMs will replace language models in a quarter of strategic forecasting and operational modeling tasks. Put simply, such systems will be able not only to explain why sales fell, but also to work through several action scenarios with concrete figures.
Developing in parallel are speech reasoning models. They must not merely recognize voice but conduct a meaningful dialogue, clarify details and solve tasks. Gartner expects that by 2030 more than 70% of corporate voice interactions will be handled autonomously.
The third technology is world models. This is AI that models the physical environment and the consequences of actions: for example, how a robot will move a part in production or how a supply chain will change after a supplier failure. Such systems could become the foundation for robotics, logistics and the planning of complex operations.
The main risk is an agent without rights and context
An autonomous AI agent can search for data, place orders, write code or interact with corporate systems on its own. And this is exactly where the main problem arises: if an agent has overly broad rights, it becomes a vulnerability.
Gartner forecasts that by 2028 25% of corporate security breaches will be linked to abuse of AI agents. The causes are uncontrolled machine rights and outdated approaches to access management.
The answer to this is AI agent identity. An agent needs its own limited rights tied to a specific task and time frame. And a company must understand who authorized a given action for the agent and why. Put simply, an AI agent should not roam corporate systems with a "master key".
The second element is AI context platforms. They give the agent access to structured corporate data, rules and internal knowledge. According to Gartner, this approach can raise the accuracy of agentic AI to 80% and reduce costs to 60%.
This also includes autonomous vulnerability remediation. Such systems find security problems themselves, set priorities, test fixes and deploy them. The idea is to shorten the time between detecting a threat and eliminating it.
Companies want to depend less on vendors
The third block is resilient flexibility architecture. Geopolitics, sanctions risks and dependence on a single cloud or AI provider are forcing companies to look for fallback options.
SovereignTech — these are technologies that give business more control over hardware, software, data and AI models. The point is not to build everything in-house, but to always have a clear path away from a vendor. Gartner expects that by 2030 more than 75% organizations outside the US will adopt digital sovereignty strategies.
Two more trends sound unusual but are logical. Disposable applications — this is software that is easy to create, use and replace when it becomes outdated. And Wetware as a Service — an experimental direction where computing can be performed using living or synthetic biological systems. Gartner considers it a potentially energy-efficient alternative to classical chips.
Where the real coolness lies
The essence of the forecast is not that “AI will become even smarter.” Gartner is effectively saying: the next stage is AI that acts, rather than simply responding to requests.
The winners will not be those who launch agents first, but those who first build governance: clear access rights, verifiable identification, high-quality data, and the ability to quickly disable or replace a system. Without this, autonomy will bring not efficiency but new security incidents and a loss of control.
At the same time, it is important not to mistake wishful thinking for reality. LQM, world models and biocomputing remain developing areas rather than ready-made mass-market products. Closest to practice are context platforms, agent identity management and automated vulnerability remediation.
