Gartner said today, in new guidance tied to its 2026 Data and Analytics Summit, that businesses looking to get real work out of AI should worry less about picking the perfect large language model and put enterprise software agents first.
The shift in emphasis is pretty clear. Gartner’s view is that companies should begin with common, low-complexity tasks, put guardrails in place early, and teach employees how to work alongside agents. Models are turning into more of a commodity, so the advantage now comes from something else: how well a company connects agents to its own data, how safely it automates workflows, and how clearly it sets the limits on agent autonomy.
Gartner also argues that the first meaningful gains will usually come from operational tools, not headline-grabbing chatbots. The examples it points to cover a wide range: student enrolment assistants, transport planning tools, lead qualification, document summarization, contract data extraction, workflows in finance and healthcare, IT service management, and even multi-agent systems for supply chains.
If you’re lining up AI projects, this update is probably worth reading. Gartner says the market will hit $11.55 billion in 2026 and almost $295 billion by 2035. Even so, companies still need governance, clean data, modern architecture, and clear human accountability, especially when some researchers are still saying that current LLM agents need better planning and reasoning.
Gartner’s summit guidance is available on Gartner.
Author: Monique Irish Casingal
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