AIVO Journal — Governance Analysis

Many enterprises now use AI assistants to help staff compare suppliers, summarise competitors, understand products, and interpret market narratives. Too many executives assume these systems behave like reliable analysts. They do not.

The trust problem is not theoretical. It is observable, measurable, and economically relevant.

Below is the evidence and the discipline required to handle it.

1. The Assumptions That Create the Problem

Leaders often assume three things:

1. the same question produces the same answer
2. answers remain mostly accurate over time
3. changes are explainable and documented

Those assumptions hold for accounting systems and financial processes. They do not hold for probabilistic models that shift behaviour without telling anyone.

This mismatch between expectations and reality is the core of the trust failure.

2. The Evidence: What Actually Happens

We ran repeated tests. Same question. Same conditions. Different outputs.

Across 200 controlled runs:

• 61 percent produced materially different answers within ten minutes
• 48 percent produced different reasoning even when the facts had not changed
• 27 percent contradicted the model’s own earlier statements
• 34 percent disagreed with at least one competing model

If a human analyst behaved like this, you would intervene within a week.

3. Three Concrete Examples

A. Retail Recommendation Instability

Thirty runs of the same question:

• Brand A recommended in 20 runs
• Brand B recommended in 10 runs
• Price, delivery, and product claims shifted even though none of those facts had changed

This instability has financial implications. One retailer we tested would have overpriced a seasonal product by 7 percent if it had acted on a flawed model-generated price summary.

B. Conflicting Safety Advice

Identical health prompt:

• One model: “safe to use”
• Another: “long term effects unclear”
• Third: warns about interactions that do not exist in clinical sources

In regulated environments, inconsistency is not an inconvenience. It is a liability.

C. Auto Decision Journey Drift

Across identical multi-turn journeys:

• Different model answers recommended different brands
• “Best value” explanations changed from price to safety to fuel efficiency
• The same model contradicted itself between morning and afternoon runs

If your investment committee behaved this way, you would reform it immediately.

4. Why the Instability Happens

The causes are simple.

1. The models update without notice

Behaviour changes silently, with no documentation.

2. There is no stated limit on variability

A system without a stability threshold cannot be assumed stable.

3. The models aim to sound helpful, not stay consistent

Optimising for persuasion instead of repeatability produces unpredictable answers.

4. Different models have different training and incentives

Expecting GPT, Gemini, and Claude to agree is like expecting competing banks to issue identical valuations.

5. Small wording shifts cause large reasoning shifts

A fragile reasoning system cannot be trusted for regulated or high-stakes use.

6. There is no audit trail

You cannot rewind the logic behind a changed answer.
A system you cannot audit is a system you cannot fully trust.

5. Why the Model Vendors Cannot Fix This

This point is essential. Vendors are not hiding the solution. They cannot create it.

Their incentives are:

• to maximise general capability
• to optimise for helpful replies
• to broaden usage

Your incentives are:

• to reduce risk
• to increase reliability
• to maintain accountability

These goals do not align.
Vendors cannot police their own volatility because the volatility is a byproduct of how these models achieve breadth and fluency.

This is why an external measurement and control layer is unavoidable.

6. The Economic and Governance Consequences

Instability touches areas that involve real money and real exposure:

• Brand reputation: assistants give incorrect summaries to journalists and investors
• Procurement: supplier recommendations drift across runs
• Pricing and merchandising: incorrect demand or price signals distort decisions
• Financial narratives: inconsistent claims can influence analyst sentiment
• Regulation: incorrect safety or disclosure statements create compliance risk
• Customer behaviour: recommendations change unpredictably

Quiet failures are still failures. In aggregate, they shape decisions that cost money.

7. How to Prevent Larger Problems

These controls are not complicated. They are basic risk management.

1. Run repeated tests on important prompts

If a question produces inconsistent answers, treat the topic as unstable.

2. Track changes over time

A single answer means nothing. Trends show drift.

3. Compare models side by side

Disagreement is a signal that merits investigation.

4. Set volatility limits

If variability is unacceptable, prohibit unsupervised use in that domain.

5. Audit how assistants describe your business and competitors

Narratives influence purchasing, reporting, and investment.

This is the AI equivalent of reconciling bank statements. It is not optional.

8. How to Respond When Trust Fails

When an inconsistency appears, the correct response is procedural.

1. Capture the evidence

Inputs, outputs, time, model, and conditions.

2. Notify risk and compliance

Treat it as an anomaly with external implications.

3. Determine whether the issue is persistent

Re run the prompt. Compare across models.

4. Remove unstable outputs from sensitive workflows

Do not use inconsistent answers for investor, safety, or regulatory materials.

5. Increase monitoring

Repeated instability warrants increased scrutiny.

6. Record the incident

Boards and regulators expect documentation.

These are the same principles used in finance, safety, and quality control.
Ignoring small failures leads to large ones.

9. Final Position

This is not a story about futuristic risk. It is about the oldest business principle there is:
Do not put weight on a tool you have not tested.

Trust weakens when outputs wander.
Losses follow when people assume wander is impossible.

Enterprises that apply the disciplines described above will avoid unnecessary mistakes.
Those that continue treating assistants as reliable sources will eventually face consequences that are predictable, preventable, and self-inflicted.

That is not pessimism.
That is ordinary prudence.

The Collapse of Trust in AI Assistants: A Practical Examination for Decision Makers

Enterprises increasingly rely on AI assistants to support research, procurement, product comparisons, competitive intelligence, and communication tasks. These systems are commonly assumed to behave like stable analysts: consistent, predictable, and aligned with factual sources. Our findings demonstrate that this assumption is incorrect. Across 200 controlled tests involving GPT, Gemini, and Claude, we observe substantial instability: 61 percent of identical runs produce materially different answers 48 percent shift their reasoning 27 percent contradict themselves 34 percent disagree with competing models This behaviour is structural, not incidental. It arises from silent model updates, a lack of stability thresholds, missing audit trails, and optimisation for plausibility rather than reproducibility. This paper presents the evidence, explains why the volatility cannot be resolved by model vendors, outlines the financial and regulatory consequences for enterprises, and proposes a governance framework for prevention and remediation. The analysis is designed for CFOs, CROs, GCs, CIOs, board members, and executive decision makers.

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