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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Tech teams gain confidence in AI agents for workflow tasks

MIT Technology Review AI1h ago5 min read

Key takeaway

Technology experts surveyed in a new report show strong confidence in AI agents for routine and structured tasks—such as report generation, code writing, and data monitoring—but confidence drops sharply when agents lack business context to make complex decisions. As IT infrastructure costs are projected to grow while budgets stay flat, enterprises are looking to agentic AI to deliver measurable financial outcomes, with data workflows emerging as the most trusted use case so far.

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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.

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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Tech teams gain confidence in AI agents for workflow tasks

Key takeaway

3 Key Points

FAQ

Discussion

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