Chatsee.ai Report
How AI risk is shifting from model intelligence to system behavior — a cross-industry analysis of observed AI failure patterns across lifecycle stages, functions, and agentic systems.
10,000+
observed AI failure events
150+
normalized failure categories
10+
industry verticals
7
lifecycle stages
OBSERVED FAILURE MIX
More than 10,000 observed failure events, grouped by the business outcome they affected.
31.1%
20.0%
19.4%
16.8%
12.6%
Hallucination-related failures accounted for under 10% of what we observed. Shares describe distribution within the analyzed corpus, not absolute market incident rates.
CORE THESIS
Enterprise AI is evolving from a model-quality problem into a systems-reliability problem. The most important failures increasingly occur around context, execution, access, escalation and resolution — not only around hallucinations.
Contents
What's in the Report #
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Foreword — why runtime behavior needs a taxonomy, anchored in one agentic incident
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Executive summary — the central argument and top five findings
01
About this study — data sources, scope, methodology, and limitations
02
Understanding the enterprise AI lifecycle — seven stages where AI systems can fail
03
The biggest failure families — business-outcome view of the taxonomy
04
Where enterprise AI fails: industry view — industry × lifecycle heat map and sector snapshots
05
Where business functions fail — function × lifecycle heat map and customer-support vertical context
06
The evolution of enterprise AI failures — failure trends as systems become more agentic
07
Agent-type signals — directional evidence from coding, support, financial, and workflow agents
08
Emerging patterns — what the findings imply for runtime assurance
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Conclusion — what enterprise leaders should take away
31 PAGES · PDF · 2026
WHO IT'S FOR
Traditional monitoring tells you if your API is up, but not if the agent’s logic is sane. For SREs, this creates a massive visibility gap.
CIOs & CAIOs
Board-facing view of enterprise AI risk beyond model quality.
CISOs & risk
Escalation, governance, and audit exposure in agentic workflows.
SREs & platform
Runtime signals, execution reliability, workflow-completion metrics.
Product leaders
What agent-type specialisation means for what you ship next.