Gartner

Gartner defined AIOps in 2016, and that definition still frames how automated IT operations are described: a progression from predictive maintenance, where the system alerts a human, to automated remediation, then to autonomous self-healing systems that diagnose and resolve novel problems without intervention.

Two of the firm's numbers do most of the argumentative work around agentic AI. Patrick Joubert, CEO of Rippletide, opens on the gap Gartner measured: 64% of technology executives want to deploy agentic AI within the next 24 months, while only 17% have actually put it into production. He reads that distance as a governance and trust problem rather than a capability one. Next to it sits the forecast that 40% of agentic AI projects will be cancelled by 2027 for excessive costs, unclear ROI, and inadequate risk controls.

A third prediction targets the job itself: by 2028, the developer's role shifts from implementation to orchestration. Marco Mornati leans on it to close his one-day BMAD experiment, where the binding constraint turned out to be architectural judgement rather than code generation.

The «Hype Cycle for Generative AI 2025» projects that more than 95% of enterprises will have integrated generative AI APIs, models, or applications into production by 2028, and sorts the field into four investment areas: models, AI engineering, agents and applications, and infrastructure. The same organization forecasting near-universal adoption by 2028 forecasts four in ten agentic projects abandoned the year before.