What types of tasks do tech teams trust agents to handle?

Tech teams show highest confidence in measurable tasks like generating reports, writing boilerplate code, data quality monitoring, visualization anomaly detection, real-time data stream monitoring, and data profiling. Confidence is also growing in areas involving multistep workflows and advanced reasoning to make decisions.

What is the main barrier to agent adoption?

The primary barrier is a lack of business context supplied to agentic systems. The more complex the task, the more reasoning capability an agent requires and the greater its need for business context, which is still at an early stage of development, especially where enterprise data is difficult to wrangle and connect into the agent lifecycle quickly and reliably.

Why is 2026 significant for enterprise AI?

Gartner is calling 2026 an "inflection year" for organizations to align their AI projects with strategic business objectives. As pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek.

Large Language ModelsMIT Technology Review AIJun 29, 2026Jun 30, 2026, 01:00 JST

Tech teams gain confidence in AI agents for workflow tasks

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3 Key Points

What happened
A survey of 300 global technology experts ranks confidence in agentic AI (self-directed AI systems) across 101 tasks in AI, data, and cloud workflows. Tech teams report highest confidence in measurable, structured tasks like generating reports, writing boilerplate code, data quality monitoring, and anomaly detection, with growing confidence in areas requiring complex judgment and multistep reasoning.
Why it matters
As organizations face IT infrastructure costs projected to grow two to three times by 2030 even with flat budgets, agentic AI offers a way to automate and coordinate entire workflows. However, confidence drops significantly when agents lack business context—the real-world information needed to make sound decisions. This suggests the breakthrough potential of agents depends on enterprises getting better at connecting their data and business knowledge into agent systems quickly and reliably.
What to watch
Data workflows emerged as the breakthrough domain where tech teams trust agents most, particularly when domain experts provide context at the point of data generation. Gartner is calling 2026 an "inflection year" for organizations to align their AI projects with strategic business objectives, signaling that measurable ROI pressure is driving agent adoption in tech functions.

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FAQ

What types of tasks do tech teams trust agents to handle?: Tech teams show highest confidence in measurable tasks like generating reports, writing boilerplate code, data quality monitoring, visualization anomaly detection, real-time data stream monitoring, and data profiling. Confidence is also growing in areas involving multistep workflows and advanced reasoning to make decisions.
What is the main barrier to agent adoption?: The primary barrier is a lack of business context supplied to agentic systems. The more complex the task, the more reasoning capability an agent requires and the greater its need for business context, which is still at an early stage of development, especially where enterprise data is difficult to wrangle and connect into the agent lifecycle quickly and reliably.
Why is 2026 significant for enterprise AI?: Gartner is calling 2026 an "inflection year" for organizations to align their AI projects with strategic business objectives. As pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek.

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