A Taxonomy of AI Narrative Evidence Failure in Enterprise Contexts

Why evidentiary breakdown, not hallucination, is the dominant governance risk

Abstract

This article sets out a taxonomy of empirically observed failure modes in AI-generated corporate narratives, derived from controlled, repeatable testing across multiple large language models. The taxonomy does not rely on anecdotal incidents, post-hoc reconstruction, or hypothetical scenarios. It is organized around evidentiary consequences under scrutiny, not technical error classification.

No entities, outcomes, frequencies, or metrics are disclosed. The purpose of this article is not to attribute liability or predict legal outcomes, but to clarify what types of evidentiary failure already exist and why these failures matter to legal, compliance, and risk functions concerned with defensibility.

Framing: from hallucination to evidentiary failure

Public discussion of AI risk frequently centers on hallucination as a technical defect. In enterprise governance contexts, that framing is insufficient.

Under scrutiny, the decisive question is rarely whether an AI output was inaccurate in isolation. It is whether the enterprise can reconstruct:

• what representation was generated,
• when it was generated,
• under what prompt and system conditions, and
• whether that representation entered a governed decision, disclosure, or advisory process.

Failure to answer those questions constitutes an evidentiary failure, irrespective of model accuracy. Unlike model-centric taxonomies that classify computational error, the taxonomy below is organized around reconstructability and defensibility under review.

Methodological boundary

This taxonomy is informed by internally generated, repeatable AI-governance test artefacts produced under locked protocols, including:

• identical prompt sets,
• time-separated runs,
• single-model and cross-model comparisons, and
• immutable capture of raw outputs at generation time.

These artefacts were generated prior to any dispute, enforcement inquiry, or external request and were not produced in response to litigation risk. They are not published here. Their existence is noted solely to establish that the failure modes described are empirically observed, not theoretical.

No legal conclusions are drawn from these observations.

Category A: Identity conflation failure

Definition
The model conflates distinct legal or commercial entities that share similar names, historical lineage, or sectoral proximity, producing a single blended narrative.

Characteristics

• Public and private entities merged into one representation
• Disclosures, filings, or investigations attributed to the wrong entity
• Corrections, if present, appear late or inconsistently

Evidentiary consequence
Once an identity boundary is crossed, all downstream reasoning becomes contaminated. Post-hoc correction does not restore reconstructability because the narrative path itself cannot be reliably replayed.

Category B: Fabricated documentary attribution

Definition
The model references formal documents, filings, or disclosures that do not exist, while presenting them in authoritative, document-first language.

Characteristics

• Confident citation of non-existent reports or filings
• Use of standard regulatory or financial document structures
• Escalating specificity over repeated runs

Evidentiary consequence
These outputs simulate documentary evidence. In governance contexts, simulated records are more destabilizing than obvious error because they resemble admissible artefacts without provenance.

Category C: Temporal drift under identical prompts

Definition
Identical prompts produce materially different narratives across time-separated runs, without any intervening change in source data.

Characteristics

• Shifting conclusions about risk, status, or exposure
• Narrative escalation or de-escalation without trigger
• Inconsistent epistemic confidence

Evidentiary consequence
Temporal inconsistency defeats reconstruction. An enterprise cannot evidence what was presented at a moment of reliance if that moment cannot be reliably reproduced.

Category D: Status inflation (inference to assertion)

Definition
Speculative or inferred statements are progressively promoted into asserted representations across runs or within a single narrative.

Characteristics

• Language shifts from conditional to declarative
• External signals treated as internal disclosures
• Absence of explicit sourcing boundaries

Evidentiary consequence
Status inflation erodes the distinction between analysis and assertion. In legal and regulatory review, that distinction is foundational.

Category E: Cross-run narrative instability

Definition
Multiple runs addressing the same question set yield internally coherent but mutually incompatible narratives.

Characteristics

• Each run appears reasonable in isolation
• No single narrative can be identified as authoritative post-hoc
• Confidence increases as stability decreases

Evidentiary consequence
Stability is a prerequisite for defensibility. Where incompatible narratives coexist, selection itself becomes an ungoverned act.

Defense context and constraints

The existence of these evidentiary failure modes does not imply that AI-mediated representations are inherently indefensible, nor that traditional defenses fail. Variability, third-party attribution doctrines, absence of reliance, and jurisdiction-specific standards remain substantial constraints in many contexts.

The relevance of this taxonomy is procedural rather than outcome-determinative. It identifies where evidentiary reconstruction may be contested, not where liability attaches.

What this article does and does not claim

This article claims

• That specific, repeatable classes of AI narrative evidentiary failure exist
• That these failures arise under standard enterprise query conditions
• That they are observable without reference to any single entity or dispute

This article does not claim

• That courts have converged on an AI liability framework
• That any failure mode guarantees legal exposure
• That governance failure equates to wrongdoing

Conclusion: evidence exists before outcomes

Enterprises will not encounter AI risk first as a doctrinal question. They will encounter it as a request:

“Show us what the system said, when it said it, and under what controls.”

Where that request cannot be answered, defensibility erodes regardless of outcome. The failure modes described here are already observable in controlled settings. The strategic question is whether they are encountered during planned review or during unplanned scrutiny.

Editorial note

The AIVO Journal has been informed by internally generated AI narrative evidence artefacts produced under locked, repeatable protocols. These artefacts are not published here. Their existence is noted solely to clarify that the taxonomy above reflects empirically observed evidentiary failure modes, not speculative constructs.