Tech teams gain confidence in AI agents for workflow tasks

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.

3 Key Points

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.