Enterprise AI spending is climbing, and Gartner flags 2026 as the year firms must tie projects to business goals. With IT infrastructure costs projected to triple by 2030 on static budgets, leaders turn to agentic AI to squeeze measurable ROI. A MIT Technology Review survey of 300 global tech experts shows agents already significantly handling engineering, deployment and continuous‑improvement workloads.

Confidence spikes for repeatable tasks—report generation, boilerplate code, and data‑quality monitoring—while uncertainty rises when agents lack business context. The study ranks 101 tasks across AI, data and cloud workflows, revealing the strongest trust in structured data operations such as anomaly detection and real‑time stream monitoring. Experts cite the need for richer context pipelines to unlock complex, multistep reasoning.

Microsoft Azure Platform's Jeremy Winter argues that embedding agents within existing identity, governance and operations frameworks makes them behave like trusted systems. Human oversight remains essential; teams that pair agents with domain experts see productivity gains and career growth. The MIT report concludes that as companies refine context‑delivery and security controls, agent confidence will keep rising, translating into tangible cost savings for IT departments.