Gartner Releases Top 10 Technology Trends for 2027: Non-Text AI Tops the List, Harness Is the Focus for the Next 1-3 Years

Gartner Unveils Top 10 Technology Trends for 2027: Non-Text AI Ranks First, Harness Is the Focus for the Next 1-3 Years

Zhidongxi Download the App | Exclusive Early Access November 03, 2026 19:27:58 From Beijing

Zhidongxi, November 3 — Today, at a media briefing, Arnold Gao, Research Vice President at the well-known research firm Gartner, provided a detailed interpretation of Gartner's recently released report "Top 10 Strategic Technology Trends for 2027."

According to the report, the top ten technology trends that enterprises need to focus on in 2027 include large quantitative models, voice reasoning models, world models, AI context platforms, coding agent harnesses, AI agent identity, autonomous exposure remediation, sovereign technology, disposable applications, and wetware as a service. These trends mainly focus on the expansion of non-text AI capabilities, the building of agent governance systems, and the enduring agility of technology.

At the briefing, media outlets including Zhidongxi interviewed Gao on topics such as agent harnesses, the impact of falling software development costs, and enterprise IT architecture upgrades.

When discussing agent harnesses, Gao said that because the speed of code generation has surpassed the speed of human review, without an agent harness there will be governance misalignment and constant rework. Harness remains important, and Gartner lists it as a trend to focus on over the next 1 to 3 years. Because model capabilities are not yet mature enough at this stage, Harness is needed to assist in completing the software development process; however, as model capabilities improve, enterprises will need Harness less and less, and in the long term its importance may gradually diminish.

Regarding the impact of falling software development costs, Gao believes that tool software with simple functions, limited differentiation, and short usage cycles, as well as some traditional SaaS models, may be affected, and enterprises may in the future directly use AI to generate related applications. However, core software such as enterprise resource planning (ERP) systems and customer relationship management (CRM) systems will not be replaced quickly, because what such products truly provide is enterprise business logic and industry knowledge, not just the software itself.

1. Able to Calculate, Listen, and Simulate: Non-Text Capabilities Become a New Track for Models

The top ten strategic technology trends released by Gartner this time first point to non-text AI capabilities. Future models will need to possess capabilities in calculation, listening, simulation, and other areas on top of conversation.

In terms of calculation capability, large quantitative models (LQMs) are built specifically for processing and analyzing quantitative data, mainly targeting digital tasks in finance, healthcare, supply chains, and other areas. Traditional large language models rely on token prediction to generate text and may have issues with accuracy, compliance, and interpretability in complex calculation and prediction tasks. Large quantitative models place greater emphasis on numerical relationships, variable constraints, simulation prediction, and result traceability, which can help enterprises shift from troubleshooting after the fact to pre-simulation and proactive decision-making.

In terms of listening, voice reasoning models are a major trend to focus on. They can reason directly on audio and complete judgments, transfers, follow-ups, and process execution based on the content of a conversation. Gartner predicts that by 2030, more than 70% of enterprise voice interactions will be handled autonomously by voice models, whereas in 2026 this proportion is still less than 10%.

In terms of simulation capability, world models target more complex real-world environments. They help AI understand space, time, and causality, enabling agents to simulate possible outcomes before taking actions. Fields such as robotics, autonomous vehicles, and spatial computing are all important application directions for world models.

2. Agent Governance Covers the Full Chain of Context, Harness, and Vulnerability Remediation

AI context platforms can use unified semantics to connect enterprise knowledge with real-time data, so that agents understand both the rules and the current business state, thereby avoiding excessive permissions, compliance issues, and token waste. Gartner predicts that by 2027, enterprises that prioritize semantic processing will increase agent accuracy by up to 80% and reduce costs by up to 60%.

An AI coding agent harness is a closed-loop control system used to manage autonomous development behavior in the software development lifecycle. It can provide models with an execution environment and context, specifying the information agents can access, the tools they can invoke, and the deliverables they can produce. Because the speed of code generation has surpassed the speed of human review, without a harness there will be governance misalignment and constant rework. Gartner predicts that by 2028, enterprises adopting this harness will see a 60% reduction in defects in software releases.

AI agent identity is a tool used to identify and manage agents. It can associate models, prompts, tools, credentials, and agent behavior, preventing the large number of security vulnerabilities exposed in traditional systems. Gartner predicts that by 2028, 25% of enterprise security breach incidents will be traced to AI agent abuse, rooted in ineffective management of machine permissions and the use of traditional credential management methods.

Autonomous exposure remediation can combine AI technology with automated methods to identify, prioritize, and remediate security vulnerabilities in enterprise digital assets. The system can automatically test patches or virtual patches and deploy them to production environments. Gartner predicts that by 2030, one-third of organizations will be able to use human-machine collaborative automated remediation to continuously address the most exploitable security exposures within 48 hours.

The third group of trends focuses on the impact of AI on enterprise software forms and future computing methods, including sovereign technology, disposable technology, and wetware as a service (a new operating model that enables computing through cloud-based remote access to living or synthetic biological systems).

Disposable applications refer to software applications built for specific business purposes that will be intentionally decommissioned once the business objectives are completed or no longer relevant. Disposable applications can align the software lifecycle with business value, reduce the risk of the application portfolio, and also make it easier to standardize governance over the creation of AI applications.

Wetware as a service represents a longer-term computing direction. It envisions accessing living or synthetic biological systems through the cloud and using the plasticity and energy efficiency of biological networks to complete some computing tasks. Compared with other trends, wetware as a service is still far from widespread enterprise adoption and is closer to forward-looking exploration.

Looking at these trends together, AI competition is expanding capabilities outward, extending non-text AI capabilities on the basis of large language models; on the other hand, AI is also tightening governance inward, and the reliability of technology has become a key topic of focus. For enterprises, the next test is whether they can make agents run reliably in real business, and whether they can promptly locate and quickly fix problems when they arise.

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