Introduction

Enterprise finance platforms now support budgeting, approval flows, spend management, reporting tasks, and operational controls across a wide range of organisations. These systems increasingly appear in assistant mediated discovery, where teams use general purpose AI assistants to understand capabilities, compare options, and assess organisational fit long before a formal procurement process begins.

What emerges from this shift is not a marketing issue but a governance problem. Even under fixed test conditions, leading assistants do not hold a stable interpretation of what these platforms do, who they are for, or how their controls function. The risk comes from external reasoning drift: changes in the logic, criteria, and evaluative frames assistants apply to the same product across multiple runs.

This study uses ASOS – the Answer Space Occupancy Score – to quantify that drift and show where reasoning variance introduces exposure that internal teams cannot see.

Method

Testing was conducted across three assistant surfaces using a reproducible protocol:

• fixed temperature
• fixed region
• no system priming
• thirty identical runs per prompt
• identical scenario framing for all models

Prompts covered:

• organisational description
• enterprise suitability
• operational controls
• workflow reliability
• comparative evaluation (category level, anonymised)

Outputs were analysed for:

• identity drift (changes in core description)
• governance criteria drift
• hallucinated governance signals
• suitability drift
• multi-turn contradiction patterns
• cross model variance

ASOS was used to quantify how consistently the platform occupies the answer space across repeated reasoning scenarios.

Key Findings

1. Identity drift: the platform’s core function changes across runs

Even under identical prompts, assistants split into multiple narratives about the platform’s primary purpose. In some runs, it was framed as a broad enterprise finance workspace; in others, as a lightweight workflow tool; in others still, as an automation layer for operational tasks.

These shifts did not originate from missing information. They emerged from unstable reasoning patterns applied to the same inputs.

2. Governance criteria drift across nine evaluative signals

Across models, assistants cycled through nine different criteria when assessing the platform’s operational or organisational suitability:

• regulatory perimeter
• certifications or attestations
• enforcement related signals
• internal control robustness
• documentation and auditability
• operational resilience
• ethics and organisational optics
• enterprise scale suitability
• product control architecture

No assistant held these criteria stable across repeated runs. In multi turn sequences, criteria sometimes changed within a single chain, producing incompatible risk or suitability judgments.

This is a structural governance issue: enterprises assume consistent evaluative criteria when using assistants to understand business critical systems.

3. Hallucinated governance signals become dominant drivers of reasoning

Across models, when an assistant hallucinated a certification or attestation of any kind, that signal immediately took precedence over all other criteria.

Downstream reasoning recomposed around it, influencing:

• suitability assessments
• control environment interpretation
• comparative evaluation
• procurement style recommendations

The certification did not need to be real. Its appearance alone was sufficient to alter the logic of the answer chain.

This constitutes a non trivial governance exposure for any enterprise software category where controls, auditability, or organisational robustness matter.

4. Suitability drift: inconsistent conclusions about organisational fit

Prompts explicitly specifying enterprise requirements still produced a significant number of answers positioning the platform as better suited for smaller teams or narrower operational contexts. Other runs reversed the conclusion. These contradictory interpretations arose under identical conditions.

For platforms evaluated early in procurement cycles, this creates ambiguity in shortlisting and internal discovery workflows.

5. Contradictory reasoning within multi turn journeys

Several multi turn sequences generated incompatible statements about control logic, workflow structures, automation scope, or approval configurations.
The contradictions were not retrieval errors. They were the result of unstable reasoning structures across turns.

This matters because multi turn discovery is increasingly the default behaviour of procurement and operational teams exploring unfamiliar systems through assistants.

6. ASOS quantifies instability in the external reasoning environment

ASOS revealed measurable volatility in how consistently the platform’s attributes and fit appeared across runs.

Cross model comparison showed:

• competing narratives based on the same prompt
• wide variance in the criteria used to assess governance and suitability
• low reproducibility for key organisational claims

This variance exists entirely outside the enterprise boundary. Internal teams have no visibility into how their platform is represented in this external reasoning layer unless they test it directly.

Implications for Enterprise Finance and Operations Software

Three governance exposures now define the environment:

1. Assistant mediated discovery is already influencing early vendor consideration.
2. Reasoning drift affects interpretations of suitability, control robustness, and organisational readiness.
3. Internal dashboards, documentation, and product surfaces cannot detect this variance because it occurs in external systems the organisation does not control.

This moves assistant behaviour firmly into the domain of governance, risk oversight, and disclosure, not marketing.

Boards, CFOs, operational risk teams, and audit functions need independent evidence to validate how their organisation is represented in assistant mediated discovery, especially when the software in question supports financial or operational decision flows.

Conclusion

The study demonstrates a clear gap between intended market positioning and the narratives that general purpose AI assistants generate under stable testing conditions.

External reasoning drift is now a reproducible and measurable phenomenon that affects how enterprise platforms are interpreted during early evaluation stages.

ASOS provides a structured method to quantify that gap and map where drift occurs across reasoning surfaces.

Enterprises that rely on assistant mediated discovery in staff workflows require an independent view of this environment to close the governance gap.

For Readers

Organisations seeking to understand how assistants describe their products, capabilities, or organisational fit can apply the reproducible testing protocol outlined above. The method produces:

• variance charts
• governance criteria maps
• hallucination escalation traces
• reproducible test files suitable for internal audit, risk, and oversight committees

For readers who need a formal framework for assessing misstatement risk in external AI systems, see the related Zenodo paper: “AI Generated Misstatement Risk: A Governance Assessment Framework for Enterprise Organisations” - https://zenodo.org/records/17885472

Boards, CFOs, and operational risk leaders increasingly require evidence of how AI assistants interpret their products and control environments. Contact us: audit@aivostandard.org, if you’d like guidance on applying ASOS and PSOS to your governance framework.