89% of Enterprise AI Agent Pilots Never Reach Production, Deloitte 2026 Finds

A system built on a large language model that executes multi-step tasks semi-autonomously, such as processing requests, updating systems, or handling customer interactions, with tools, memory, and access to enterprise data.

The main causes are poor or fragmented data quality, missing governance and compliance controls, integration difficulty with legacy systems, rising inference costs at scale, and unclear ROI metrics.

No, but they should start with narrow, high-value use cases backed by unified data infrastructure and clear governance, rather than launching broad projects before data readiness.

Predictive AI projects historically scaled better because their scope is narrower and risk lower. Agents are more complex because they take real actions inside enterprise systems, raising governance and observability requirements.