Enterprise investment in AI is booming, but the honeymoon phase is officially over. Gartner has declared 2026 an “inflection year” for organizations to align their AI projects with strategic business objectives - which is corporate speak for “show us the money, or we’re pulling the plug.” As the pressure to prove ROI mounts, executives are turning to agentic AI, hoping it will deliver the measurable financial outcomes their shareholders demand.
A prime opportunity for AI agents exists in the tech function itself, where IT infrastructure costs are projected to balloon two to three times by 2030, according to McKinsey, even as budgets stay frozen. Over the last 18 months, tech teams - engineers, developers, architects, and other practitioners who keep the digital lights on - have clearly put agents to work. The ultimate promise of agents is not just to automate tasks but to manage entire workflows, pursuing business goals in a way that lets humans and agents collaborate. But given the risks of automated decision-making, teams can’t just hand over the keys without confidence that agents are safe, reliable, and secure.
Among technology experts, our research shows that teams are exceedingly confident about using agentic AI across a significant amount of AI, data, and cloud tasks. Where agent readiness drops, it’s largely due to a lack of business context being supplied to these systems. The more complex the task, the more reasoning capability an agent needs - and the more business context it requires. Such context-generation capabilities are still in early development, especially when enterprise data is messy and hard to wrangle. Human oversight, it turns out, remains a key factor for success.
Knowing that tech teams are pivotal to this transformation, the experts we interviewed expect agent confidence to accelerate as experience deepens and business environments mature. “As we design agents to operate within the same operational boundaries, identity systems, and governance models that teams already use, they start to behave more like the systems organizations already trust,” says Jeremy Winter, corporate vice president and chief product officer at Microsoft Azure Platform.
This report - based on a survey of 300 global technology experts - ranks 101 tasks across AI, data, and cloud workflows based on respondents’ confidence in agents acting on their behalf. It also examines how tech teams view the opportunities and challenges of agentic AI, along with its potential to enhance their careers. Confidence is surging for measurable tasks and growing in areas of complex judgment. Tech experts overwhelmingly believe agents help with everyday work, including streamlining processes, improving performance, and reducing repetitive tasks. Confidence is highest for generating reports and boilerplate code, with clear opportunity in multistep workflows and advanced reasoning.
Data workflows are the breakthrough domain. Tech teams trust agents most where structure provides a reliable foundation for decisions - areas like data quality monitoring, visualization anomaly detection, real-time data stream monitoring, and data profiling. This is where domain experts closest to the data can provide context to let agents act and deliver trusted outcomes.
Read the Microsoft Cloud blog by Amanda Silver, corporate vice president of Microsoft 365 Core and Work IQ, which underscores the importance of keeping humans in the loop and how systems thinking advances careers. And for a deeper dive into data workflows as a breakthrough use case for agents, check out the Fabric blog to hear from Kim Manis, corporate vice president of Product for Microsoft Fabric.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.