Applying Failure Mode & Effects Analysis to Conversational AI Systems
Version 2.0 — Updated December 2025
Artificial Intelligence is now deeply embedded in human communication, decision-making, and emotional experience. As described in the Executive Summary, the core safety challenge is not simply the content an AI generates, but the systemic patterns through which failures occur. These patterns behave like failure modes in any complex system: they repeat, they escalate, and they can be predicted before they reach users.
What Traditional AI Testing Often Misses
Conversational AI systems are typically validated for correctness, policy compliance, performance, and misuse resistance. These forms of testing are necessary, but they do not fully capture how conversational systems affect people over time.
Many of the most consequential failures in conversational AI do not appear as incorrect answers, crashes, or policy violations. They emerge through patterns of interaction that are individually acceptable but cumulatively harmful.
These failures often remain invisible because they:
• do not trigger safety filters
• do not violate content policies
• do not produce immediate user complaints
• do not appear in single-turn evaluations
A conversational system can therefore appear to be functioning correctly while still producing outcomes that undermine user judgment, trust, or wellbeing.
Failure Modes in Conversational AI
In engineering terms, a failure mode is not an isolated malfunction. It is a repeatable pattern of behavior through which a system can cause harm.
In conversational AI, failure modes often arise from:
• tone consistency across interactions
• reinforcement of user assumptions
• escalation of trust or reliance
• subtle shifts in framing or certainty
• limitations in detecting user vulnerability
Because these behaviors unfold across multiple exchanges, they are rarely captured by tests designed to evaluate individual responses.
AI-FMEA treats these interaction patterns as legitimate system behaviors that can be identified, described, and evaluated—just like failure modes in any other complex engineered system.
Questions Engineers Must Be Willing to Ask
Applying AI-FMEA requires engineers to examine conversational behavior from the perspective of cumulative human impact. This begins by asking different questions than those typically used in system validation.
Examples include:
• If this response pattern repeats across many sessions, what does it train the user to expect?
• Does this interaction increase or reduce user dependence on the system?
• Would this behavior feel appropriate after one interaction but problematic after fifty?
• Could this pattern influence user judgment without ever producing an explicit error?
• Would a vulnerable user experience this response differently than a confident one?
• If this behavior fails, how likely is the system to recognize that failure before harm occurs?
• Could this pattern pass silently without triggering metrics, alerts, or complaints?
These questions do not assume malicious intent or flawed design. They recognize that conversational systems influence users through consistency, authority, and repetition—properties that standard testing rarely evaluates directly.
In practice, evaluating conversational AI requires engineers to consider three fundamental dimensions of risk: the potential impact of a failure if it reaches a user, the likelihood that the failure will occur through normal interaction, and the system’s ability to recognize the failure before harm occurs.
These dimensions are not always explicitly identified, measured, or documented in current AI development workflows, particularly when failures emerge through cumulative interaction patterns rather than single outputs.
AI-FMEA formalizes this reasoning by treating —Severity, Occurrence, and Detection—as explicit, comparable factors. Doing so allows engineering teams to surface risks that may otherwise remain diffuse, prioritize attention where it matters most, and document safety reasoning in a way that reflects how conversational systems actually behave over time.
For practitioners, regulators, and organizations wishing to adopt AI-FMEA within their own workflow, the downloadable templates below provide the tools needed for both analysis and implementation.
For those responsible for oversight, evaluation, and governance, the next page examines how AI-FMEA results are interpreted and reviewed from a regulatory perspective. AI-FMEA – A Regulator’s & Reviewer’s Perspective
Click below to download the available templates
FMEA-06-Example-FEMA-Emotional-Dependency-v1.2.ods (ODS file or Excel)
FMEA-06-Example-FEMA-Emotional-Dependency-v1.2.pdf