What comes after generative AI? Gartner’s top technology trends for 2027

Generative AI may soon do much more than create text and pictures. It could become better at working with numbers, understanding spoken conversations and completing tasks on its own. Gartner’s top 10 strategic technology trends for 2027 look at how businesses can use these new AI abilities while keeping their systems safe and making their technology easier to change when needed.

Gartner announced these trends at its IT Symposium/Xpo in Orlando. They cover three main areas: using AI for more than text, managing AI systems that act on their own and building technology that can adapt over time. Gartner also focuses on the information, security controls and computer systems businesses need to use AI safely and responsibly.

1. Large quantitative models bring numerical work into focus

Large quantitative models (LQMs) are AI systems built to work with numbers. They could help businesses understand financial risks, plan how goods move through supply chains and predict future energy needs. These jobs require AI that can study numbers carefully instead of depending only on answers produced by language models.

Gartner predicts that by 2030, LQMs will take over more than 25% of the strategic forecasting and operational modelling tasks now done by large language models. This suggests businesses may change how they use large amounts of data to make important decisions.

2. Speech reasoning models aim to make voice interactions more capable

Speech reasoning models are AI systems designed to understand spoken words while remembering what a conversation is about. They aim to do more than simply recognise what someone says. They could also understand follow-up questions, deal with confusion and respond when a situation changes.

Gartner predicts that by 2030, these models will handle more than 70% of voice conversations in businesses without human help. If companies start using them widely, they could change how businesses provide customer service over the phone and manage other spoken conversations.

3. World models simulate environments before action

World models are AI systems that create a picture of how a real or digital environment works. They let AI explore what might happen before it takes action. For example, a robot could use a world model to predict how moving an object might affect other things around it.

Businesses could use these simulations to make better decisions before spending money, using resources or taking action in the real world. Gartner sees world models as part of a bigger effort to make AI useful for more than working with text.

4. AI context platforms give agents relevant business information

AI agents are systems that can carry out tasks for people or businesses. To understand instructions and make good decisions, they need accurate background information. AI context platforms aim to bring useful business information together so AI agents do not have to work with missing or scattered knowledge.

Gartner says that making data easier for AI to understand, including what the information means, could improve the accuracy of AI agents by up to 80% and lower costs by as much as 60%. These are Gartner’s predictions, not results that every business is guaranteed to achieve.

5. AI coding agent harnesses focus on verification

As AI agents do more software development work, businesses need ways to check that the code they create meets the right standards. An AI coding agent harness connects software development tools with business information, checking processes and feedback.

This means businesses should focus not only on getting AI to write code but also on testing the code and checking the changes it makes. Companies still need to make sure their software works well, stays secure and remains easy to update and maintain when AI agents help build it.

6. AI agent identity brings accountability to autonomous systems

AI agent identity is about knowing which AI system is doing a task, what it is allowed to do and who is responsible for it. Businesses need to give AI agents the right permissions and make sure someone is responsible when those agents use computer systems or carry out work.

Gartner predicts that misuse of AI agents will be involved in 25% of business security breaches by 2028. This shows why companies need to apply identity checks and access rules to AI agents, just as they already do for human workers.

7. Autonomous exposure remediation links security detection with action

Autonomous exposure remediation uses AI and automation to find security weaknesses, decide which ones need attention first and help fix them. The system can connect finding a problem with taking steps to solve it, such as installing security updates and testing whether the fixes work.

The challenge is to fix security problems faster without losing control over changes that might cause new problems. Businesses need safety checks to spot fixes that do not work and undo them when necessary.

8. SovereignTech addresses dependence on critical technology providers

SovereignTech is about how businesses manage their dependence on technology companies, the countries and legal systems involved, and the computer systems they rely on. Companies need to think about where their technology runs, which laws apply and whether they can move their software and computing tasks to other systems if needed.

Gartner predicts that more than 75% of organisations outside the United States will have digital sovereignty strategies by 2030. This means Gartner expects more businesses to make plans for keeping control over their digital technology and data. These plans are especially important when laws and dependence on outside technology providers affect the systems businesses choose.

9. Disposable applications challenge the idea that all software must endure

Disposable applications are software tools built to do a particular job and then removed when that job is finished. As AI makes it easier to create software, businesses may be able to build more temporary tools instead of keeping every application running for years.

But temporary software also needs careful planning. Businesses should decide from the beginning when an application will be removed and what will replace it if necessary. Otherwise, a tool meant to be used for a short time could become something the company has to maintain for years.

10. Wetware as a service explores biological computing

Wetware as a service (WaaS) is a new idea that lets people use biological or artificially created biological systems for computing without needing to have those systems in the same place. Gartner says this could become one way to deal with the growing energy use and environmental problems linked to expanding traditional computers that use silicon chips.

The idea is to use biological systems to process information and perform computing tasks. This technology is still developing, and its future use in businesses is uncertain. Whether companies can use it in practice will depend on how the technology improves and how well it can work with the computer systems they already have.

What the trends mean for technology leaders

Gartner’s predictions for 2027 show that businesses may need to change what they focus on when planning technology. Along with using new AI abilities, companies will need to manage the identities and permissions of AI systems, check work done automatically and make sure they can change the important technology providers and systems they depend on.

These ten trends are not a single plan that every business should follow. Which trends matter most will depend on how each company works, what security it needs, what technology it already uses and how much risk it is willing to accept from new technology.

For chief information officers (CIOs), who lead a company's technology decisions, the main job is to find where AI systems that work more independently can bring clear, measurable benefits. At the same time, they must make sure those systems are secure, that someone is responsible for their actions and that they can adapt when things change. The next stage of AI in business will depend not just on what AI can do, but also on how well companies manage it and provide the support it needs.