Analyst firm Gartner has taken a peek into the next 18 months and come to one conclusion: artificial intelligence will remain one of the key drivers of technology trends in the coming year.
Hardly a surprise? Probably not, but the scope of what Gartner is predicting is nonetheless worth taking a look at.
Some of the predictions – such as the emergence of speech reasoning models and a need to move away from inaccurate large language models to large quantitative ones – seem relatively simple predictions, while others, like the rise of wetware-as-a-service seem a touch more… exotic.
Regardless, according to Gene Alvarez, Distinguished VP Analyst at Gartner, AI is the backbone of nearly everything to come in 2027.
“The critical friction today is that AI systems operate in an architectural vacuum, generating software faster than humans can review it, creating unmanaged credential paths and binding processes to single-source vendor monopolies,” Alvarez said.
“To prevent this systemic exposure, organisations must move from a passive observer of model capabilities to an active architect of organisation, human. and machine boundaries.”
Here are Gartner’s top strategic technology trends for 2027:
Large Quantitative Models
LQMs use modular, hybrid neural networks designed to process and analyse numerical data, reducing the accuracy and compliance risks of using general-purpose language models for quantitative tasks. They enable proactive simulation and more reliable autonomous forecasting in regulated sectors such as finance and healthcare.
“This rigorous approach drives unprecedented business agility, enabling organisations to safely deploy autonomous forecasting in strictly regulated industries, such as finance and healthcare,” Alvarez said.
Speech Reasoning Models
Speech reasoning models analyse audio directly, using conversational context and intent to determine how to respond. They aim to improve the accuracy, speed, and resolution rates of enterprise voice automation, making complex, judgment-intensive customer interactions easier to automate at scale.
“Voice remains the enterprise’s highest-volume interaction channel, but even a decade of customer support automation has failed to deliver the accuracy, speed and resolution rates enterprise deployment demands,” Alvarez said.
“Speech reasoning models close that gap at scale, making complex, judgment-intensive conversations viable to automate.”
World Models
World models enable machines to understand space, time, and causality, allowing autonomous agents to simulate future scenarios, predict outcomes, and assess the consequences of actions before executing them. Growing investment in robotics, autonomous vehicles, and spatial computing is accelerating their development.
AI Context Platforms
AI context platforms equip agents with the contextual knowledge needed to interpret inputs, make decisions and act in line with business objectives. Without a dedicated context layer, organisations risk excessive agent privileges, compliance failures, reduced trust, and higher token costs.
Gartner predicts that by 2027, organisations prioritising semantics in AI-ready data will improve agentic AI accuracy by up to 80 per cent and cut costs by up to 60 per cent.
Agent Coding Harness
An AI coding agent harness is a closed-loop control system that governs autonomous coding agents throughout the software development lifecycle. Without one, developers must manually verify machine-generated output, creating additional governance overhead that can erode productivity gains.
AI Agent Identity
AI agents operating autonomously on behalf of multiple users expose weaknesses in legacy identity systems, particularly those lacking just-in-time access controls. Gartner predicts that by 2028, 25 per cent of enterprise security breaches will stem from AI agent abuse linked to unmanaged machine privileges and outdated credential practices.
“Applying dynamic identity controls allows organisations to replace high-risk static credentials with dynamic, policy-controlled access,” Alvarez said.
“AI agent identity connects models, prompts and credentials to agent actions, creating trust and accountability.”
Autonomous Exposure Remediation
AER combines AI and automation to identify, prioritise, and remediate security vulnerabilities across an organisation's digital environment. It can automatically test and deploy patches or virtual patches into production systems, accelerating exposure reduction.
Sovereign Tech
SovTech encompasses hardware, software, databases, and AI models designed to give organisations practical exit options from critical technology providers. By reducing vendor and geopolitical dependencies, these technologies help maintain operational continuity and limit lock-in risks.
Gartner predicts that by 2030, more than 75 per cent of organisations outside the US will adopt digital sovereignty strategies, shifting critical workloads to reduce geopolitical exposure and vendor dependence.
Disposable Applications
Disposable applications are purpose-built software designed to be retired once their specific business purpose has been fulfilled or is no longer relevant. This approach shifts software procurement, development and budgeting towards dynamic application portfolios rather than long-term legacy investments.
“Transitioning to disposable applications fundamentally alters how software is bought, designed and budgeted for, turning application portfolios into dynamic systems, rather than long-term legacy investments,” said Alvarez.
Wetware-as-a-Service
Wetware-as-a-Service is an emerging model that provides remote, cloud-based access to living or synthetic biological systems for computation. These biological processors use biochemical networks to execute complex algorithms with potentially high energy efficiency, offering an alternative to conventional silicon hardware as AI's environmental and energy demands grow.
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