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Summary – The proliferation of hallucinations in your AI agents reveals a lack of governance framework, exposing your company to biased strategic decisions and financial, regulatory, and reputational risks. To move from a black box to a glass box, you must make data origins explicit, define the scope of action and confidence thresholds, integrate humans into the loop, log every recommendation, and align AI governance with control and audit standards.
Solution: deploy an AI governance framework that includes business metrics, validation workflows, and regular audits.
When a member of the executive committee worries about an AI agent’s “hallucination,” the issue isn’t the technology but the lack of a clear governance framework. A plausible yet unfounded answer can lead to biased strategic decisions, leaving no trace or control.
As with any decision-making system, AI must be estimated, bounded, and audited against business metrics; otherwise, it becomes a risk multiplier. This article offers a guide to move from a black-box AI to a glass-box AI, quantify its operating scope, integrate humans into the loop, and align AI governance with cost, timeline, and risk management standards.
Understanding AI Hallucinations as a Business Risk
An hallucination is not a visible failure; it’s a convincing yet baseless response. This lack of foundation is more dangerous than a detectable error because it misleads decision-makers.
Definition and Mechanism
An AI hallucination occurs when the model generates a plausible output without relying on verified data.
Technically, this phenomenon often stems from insufficient rigor in selecting and weighting training data or from undocumented implicit assumptions. The algorithms then fill gaps with “plausibility” rather than facts.
In a professional context, this is like receiving a complete financial report based on outdated or incorrect figures. Confidence in the result masks the danger of flawed decisions.
Concrete Business Impacts
On an operational level, a hallucination can skew cost estimates, leading to significant budget overruns. The project becomes miscalibrated, with direct financial consequences.
At the strategic level, a fictitious vendor recommendation or an inaccurate regulatory analysis can expose the company to litigation or compliance breaches. Reputation and partner trust are immediately at stake.
The main vulnerability lies in the loss of traceability between input data, assumptions, and decisions. Without a clear link, it’s impossible to trace back for verification or correction, which amplifies the error’s impact.
Example from an Industrial SME
An industrial SME used a generative agent to forecast maintenance costs for its production lines. The AI extrapolated from outdated volume assumptions while claiming to rely on recent data, resulting in a 15% underestimation of needs.
This case shows that an unaudited AI can conceal outdated data sources and lead to erroneous budgeting choices. The overall program planning was disrupted for months, causing delays and overruns.
It’s essential to require an explicit link between every AI output and the underlying data to limit financial and operational exposure.
Moving from Black-Box to Glass-Box AI
AI used for strategic management must be explainable, like a financial model or a business plan. Without transparency, decisions remain opaque and uncontrollable.
Minimal Explainability Requirements
No business manager in an executive committee should approve a figure without being able to trace its origin. This is as imperative a standard as budget justification or a financial audit report.
Explainability doesn’t mean understanding algorithms in detail, but having a clear view of data sources, implicit assumptions, and model limitations. This granularity ensures informed decision-making.
Without this level of transparency, AI becomes merely a tool with hidden logic, and the scope of risk is hard to gauge until it’s too late.
Key Components of a Glass Box
Three elements must be documented: the data sources used (internal, external, update dates), the integrated business assumptions (risk parameters, calculation rules), and known deviations from actual logs.
Each output must be accompanied by a note specifying generation and validation conditions. In critical decisions, this report ensures a chain of accountability akin to meeting minutes or accounting vouchers.
This approach fits naturally into existing internal control processes without adding excessive administrative burden, as the format and content align with IT and financial audit best practices, such as reproducible and reliable AI pipelines.
Example from a Financial Institution
A bank’s compliance department deployed an AI agent to analyze regulatory documents. The team found that some recommendations lacked references to the official version of the law, with no way to verify them.
This finding highlighted a lack of traceability in the processing pipeline. The institution then enforced a workflow where each AI suggestion is accompanied by a precise reference to the consulted article and version of the regulation.
This measure restored internal and external auditor confidence and accelerated business acceptance of the tool.
Edana: strategic digital partner in Switzerland
We support companies and organizations in their digital transformation
Estimating AI as a Decision-Making System
Evaluating AI solely on technical performance or productivity is insufficient. It must be quantified like any decision-making system, based on scope, risk, and cost of error.
Defining the Decision Scope
The first step is to clarify the AI’s role: simple recommendation, pre-analysis for validation, or autonomous decision-making. Each level requires a distinct degree of trust and control.
Poorly defined scope exposes the company to surprises: AI doesn’t self-limit and can venture into unauthorized cases, generating unforeseen actions.
Defining this scope at the project’s outset is as critical as setting budget limits or delivery timelines.
Quantifying Risk and Confidence
Acceptable risk should be framed around a confidence range, not a single accuracy rate. This distinguishes high-reliability zones from areas requiring manual review.
Simultaneously, measure the cost of an error—financial, legal, reputational—for each decision type. This quantification highlights priority areas for human checks and validations.
Without this quantification, the company lacks concrete criteria to balance execution speed against risk tolerance.
Example from the Healthcare Sector
A hospital implemented an AI assistant to schedule patient appointments. In some cases, the agent produced unrealistic schedules by miscombining parameters (average duration, emergencies, room availability).
The cost of an error led to overbooked slots the next day and increased no-show rates. The management team then defined a confidence range: if the discrepancy exceeds 10% compared to a standard schedule, human validation is automatically required.
This rule maintained high service levels while preserving the productivity gains offered by the tool.
Human-in-the-Loop and Strategic Governance
AI accelerates decision-making, but responsibility remains human. Without validation thresholds and continuous auditing, AI becomes a risk factor.
Validation Thresholds and Peer Review
Define criticality thresholds for each output type. High-risk decisions must undergo human validation before execution.
A cross-check between the AI and a subject-matter expert ensures anomalies or deviations are caught early, before errors propagate through the system.
This process resembles double-reading a report or conducting a code review and integrates into existing governance cycles without slowing decision-making.
Logging and Continuous Audit
Every AI recommendation must be archived with its input parameters, confidence scores, and subsequent human decisions. This logging is essential for any post-mortem investigation.
Regular audits compare forecasts and recommendations against operational reality. They reveal drifts and feed into a continuous improvement plan for the model.
This mechanism mirrors post-deployment controls in finance or project performance reviews and ensures long-term oversight.
Governance, Compliance, and KPIs
AI must align with existing governance processes: usage policies, documentation, risk mapping, and AI governance, compliance (the EU AI Act or local directives).
Specific indicators—accuracy, model drift, rejection rate, reusability—allow AI to be managed like a risk portfolio or budget.
Without integration into strategic management, AI remains an experiment, not a performance lever. Formalizing roles, responsibilities, and control points is key to reliable adoption.
Govern AI as a Competitive Advantage
Hallucinations aren’t just bugs; they signal insufficient governance. High-performing AI is explainable, calibrated, and subject to continuous audit, like any strategic decision system.
It’s not enough to use AI: you must decide with it, without losing control. Leaders who embed this framework will get the most out of AI transformation while mastering risks.
Whatever your maturity level, our experts can help you define your AI governance, estimate the scope of action, integrate humans into the loop, and align your processes with best practices.
