ChatSee’s study of 10,000+ AI failure events finds less than 10% were hallucination-related, 31.1% involved resolution or escalation breakdowns, and execution and action failures rose 62% relative to the Q2 2024 baseline

SAN FRANCISCO, July 29, 2026 /PRNewswire/ — New research from ChatSee.ai, which provides the failure intelligence layer for autonomous AI systems, finds that hallucination-related failures accounted for less than 10% of more than 10,000 observed enterprise AI failure events analyzed by the company. The largest failure family, resolution and escalation breakdowns, represented 31.1% of observed failures, while execution and action-related failures increased 62% relative to the Q2 2024 baseline. The findings suggest that as enterprises move from AI systems that answer questions to AI systems that perform work, the dominant risk is shifting from incorrect content to failed task completion.

As enterprises push beyond chatbots into agentic systems, AI failures are becoming harder to detect and more consequential. A system may answer politely, follow output rules, and still fail to resolve a customer issue, miss an escalation, invoke the wrong tool, or leave a workflow incomplete. Or worse, once an AI system is given a goal, tools, data access, and discretion over how to act, it may ultimately achieve the objective through a strategy that is not aligned with the operator’s intent. Yet many organizations still evaluate AI risk primarily through the lens of hallucinations, output guardrails, and prompt testing.

ChatSee’s State of Enterprise AI Failures 2026, based on a cross-industry analysis spanning more than 10,000 observed AI failure events across more than 150 failure categories, 10 major industry verticals, business functions, and seven AI lifecycle stages, explores how enterprise AI risk has expanded beyond model quality into workflow reliability and task-completion failures.

The research reveals how enterprises’ current approach to AI risk is increasingly misaligned with where failures are actually occurring, with key findings including:

• Most observed enterprise AI failures occur outside traditional hallucination categories: Hallucination-related failures accounted for less than 10% of observed enterprise AI failures in the analyzed corpus. The largest failure family, resolution and escalation breakdowns, represented 31.1% of observed failures, occurring when AI systems appear to provide service while never actually resolving the underlying issue. The remaining failures were distributed across retrieval, scoping, execution, response, and governance, meaning enterprises focused solely on model improvement will not address many of the failures their systems produce.
   
• Different industries have distinct AI failure signatures: The same AI architecture creates materially different risks depending on the industry in which it is deployed. Financial services failures concentrate around escalation and governance. Healthcare and insurance failures cluster around context integrity and policy-sensitive guidance. Technology and telecom failures tend to occur in execution, entitlement, and workflow completion.
   
• The same AI function fails differently by industry: Customer support AI does not have a single failure profile. A support agent in banking fails differently than one in telecom, travel, healthcare, or logistics because the underlying data, policy, entitlement, and escalation structures differ. Enterprises need a common failure intelligence layer that can compare failures across AI systems while recognizing how workflows, policies, entitlements, and escalation paths differ by industry and function.
   
• Execution failures are rising as AI systems move from answering to acting: Action and execution failures increased by approximately 62% relative to the Q2 2024 baseline, while hallucination-related failures declined by approximately 7% over the same period. As enterprises move from chatbots to agentic AI systems, the observed failure mix is shifting toward systems that fail while performing work, not only while answering questions.
   
• Many enterprise AI controls were built for the chatbot era: Output filters, red-teaming practices, and hallucination guardrails remain necessary but are insufficient for AI agents that invoke tools, execute workflows, and coordinate across enterprise systems. An agent can pass every output filter and still invoke the wrong tool, fail to escalate, enter a resolution loop, or break a workflow silently.

“The enterprise AI question is no longer only whether a model can answer correctly, but whether the AI system can retrieve the right context, take the right action, escalate at the right time, and bring work to resolution,” said Sekhar Sarukkai, CEO and co-founder of ChatSee. “A banking customer can report suspicious activity, receive a polite and compliant response, and still never be escalated for human review. That is a serious enterprise failure even if the model never hallucinated.”

Together, the findings point to a new enterprise requirement: failure intelligence that can detect, classify, remember, and help reduce recurring AI behavioral failures once systems are running in production. Pre-deployment evaluation remains necessary, but it is not sufficient. Modern AI systems require runtime assurance.

“As the recent OpenAI–Hugging Face incident showed, agents do not need malicious intent to create enterprise risk,” Sarukkai added. “An AI system can pursue an assigned goal through a strategy the operator never intended. That is the failure class enterprises will increasingly face as agents gain more autonomy. Governance cannot be a static checklist; it has to account for how systems behave while they act.”

The full State of Enterprise AI Failures 2026, which includes a full “failure intelligence framework” for understanding where enterprise AI systems break down after deployment, is available for download at https://www.chatsee.ai/state-of-enterprise-ai-failures.

Methodology

The State of Enterprise AI Failures 2026 analyzes more than 10,000 observed AI failure events collected between 2023 and 2026. Data sources include public incident reports, community-reported failures, open-source agent traces, Hugging Face datasets, AI interaction logs, production observations, and proprietary research conducted by ChatSee. ChatSee developed a normalized taxonomy of more than 150 AI failure categories to compare failure patterns across lifecycle stages, industries, and business functions. The report covers 10 major industry verticals, including financial services, healthcare and insurance, technology, retail, telecom, manufacturing, and professional services. Findings represent observed failure distribution within the analyzed corpus and should not be interpreted as absolute market incident rates.

About ChatSee.ai

ChatSee provides a failure intelligence platform for autonomous AI systems. By transforming behavioral failures into organizational memory, ChatSee enables enterprises to continuously improve the reliability, governance, and trustworthiness of AI systems running in production.

Learn more at chatsee.ai.

Media Contact

contact@chatsee.ai 

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SOURCE Chatsee AI Inc