ChatSee.ai’s “State of Enterprise AI Failures: 2026” report analyzed more than 10,000 observed failure events across enterprise deployments from 2023 through mid-2026. Hallucination-related failures accounted for less than 10% of observed breakdowns (ChatSee.ai, July 2026).
The largest reported family was resolution and escalation breakdowns, at 31.1% of observed failures. That category identifies a part of the workflow to investigate. It does not establish that the model’s preceding answer was correct, or reveal the overall accuracy of model answers.
Execution and action-related failures rose 62% relative to the Q2 2024 baseline. As agents take actions across tools, evaluation needs to examine the selected tool, the policy applied, the context supplied, and the next handoff. Answer accuracy remains one part of that evaluation.
Check visibility across the workflow
The same report put the average organization at roughly 37 deployed agents and said only 24% had full visibility into their communication. The remaining three-quarters lacked that reported level of visibility; partial visibility and an inability to trace any handoff are different conditions.
A team can begin by following a failed task across its systems. Inspect the original request, model output, tool action, and escalation record. Record where the task stopped reaching the person or process responsible for the next step. Assign an owner for repairing that path.
Review spending against observed failures
AlixPartners projects that 20 to 30% of AI program budgets will go to trust and governance by 2027, up from 10 to 15% today. That projection describes a spending direction, leaving each team to determine which controls address its actual failures.
Gartner estimates that 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. Analyst Anushree Verma described many current projects as early-stage experiments or proofs of concept driven by hype and often misapplied.
These forecasts give leaders reasons to review their programs. They do not determine the outcome of a particular deployment or prove that more governance spending prevents failure.
Build a local failure record
Classify your own incidents before reallocating the budget. Keep output errors, execution errors, and failed escalations distinguishable, while allowing one incident to involve several causes. Review accuracy checks alongside observability, tool-use guardrails, escalation routing, and agent-to-agent communication logs.
Give an operations owner responsibility for the complete workflow, working with the people who build and evaluate the model. Investigate resolution failures against the actual handoff record rather than assuming the answer was right.
The report’s percentages describe observed failure categories. Use them to broaden the review, then base changes on evidence from your own assignments.