When Success Isn’t a Fixed Number - Measuring Success in a Non-Deterministic Data World

With the rise of AI-powered systems ,particularly Large Language Models (LLMs), agents, and generative analytics tools, this challenge becomes sharper. How do you measure success when the same question, asked of the same data ,yields slightly different answers each time? And when there may be no single “correct” answer at all?

Welcome to the age of non-deterministic systems, where success is no longer defined by exact numbers but by stability, traceability, and impact.

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Why Measuring Success in BI Was Never Simple, and Is Even Harder with AI

The question 

‍“How do you measure success?” 

has followed Business Intelligence since its beginnings.

Even then, Return on Investment (ROI) was rarely purely technical, it was organizational:

• Faster reporting meant time savings.
• Automated data preparation reduced errors.
• Self-service BI freed up IT resources.

These effects could still be quantified - in hours, cost, or error reduction.

But once BI systems began to influence decisions, their benefits became harder to measure: How do you quantify a better decision? How do you measure that a team reacted faster or more confidently, without a control group?

With the arrival of AI, this dilemma deepens :Answers are probabilistic, benefits indirect, and classic KPIs like accuracy or precision fall short when multiple plausible answers exist [1][2].

That’s why success must be redefined, not as binary correctness, but as a balance between factuality ,stability, and impact.

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A Hot Topic at Every Conference

At recent industry events, from Big Data & AI World Frankfurt to World of Data Basel, one question dominated discussions:

How can we measure the value of AI systems when results are never exactly repeatable?

At inics, we anticipated this development early. Three years ago, one of our student researchers wrote his bachelor’s thesis precisely on this topic: “Evaluating Performance and Stability of Probabilistic Models in Business Intelligence Environments.”

What was once an academic niche has now become a core governance and trust issue for enterprises, and a key focus of our daily project work.

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From KPI to Context - What Really Matters Today

In classical BI, everything revolved around deterministic KPIs: precise, comparable, predictable.

In the age of AI, the focus is shifting. It’s no longer the exact number that matters, but consistency, explainability, and impact.

The goal is not the perfect answer, but one that is reliable, reasoned, and actionable. Success, therefore, means that the system behaves stably, produces traceable results, and supports better decisions.

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Measuring Stability, Without Reinventing KPIs

The market for AI evaluation metrics is vast, and sometimes confusing. Between Factual Accuracy, Faithfulness, Calibration Error, and Reference Hallucination Score, it can seem as if entirely new KPIs must be invented to prove value [3][4].

That’s not necessary. The key lies in combining and interpreting existing metrics correctly, in the context of business and decision-making processes.

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Hallucination ≠ Instability

Many metrics, such as

• Factuality Scores(e.g.,FActScore‍ [5] or
• Reference HallucinationScore(Aljamaan et al., 2024 [6]),

measure how often a model produces factually incorrect information.

That’s important, but it says nothing about reproducibility. A model can be consistently wrong (perfect stability, zero truth), or correct but inconsistent.

→ Factuality and stability are two sides of the same coin, and must be assessed separately.

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Combining Existing Metrics Effectively

Companies don’t need to invent new KPIs. They need to use existing tools strategically. Three established metric clusters are sufficient to evaluate AI systems transparently:

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Together, these dimensions paint a complete picture: How reliable, traceable, and useful is the system in daily operations?

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Confidence, The Missing Link

Beyond these metrics, one factor is gaining importance: Confidence.

It reflects how certain the model is about its own answer. High confidence can indicate internal stability, or, when wrong, dangerous overconfidence.

That’s why confidence is increasingly seen as the corrective bridge between factuality and trust [10][11].

In practice, several types emerge:

• Prediction Confidence: 
  The probability with which the model believes its answer is correct.
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• Calibration Confidence:
  Whether confidence and actual accuracy align (Expected Calibration Error, [12]).
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• Self-Consistency Confidence: 
  Agreement of multiple runs with the same input.
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• Human-Validated Confidence: 
  Comparison between model confidence and user perception (“How confident did the answer seem?”).

Used correctly, confidence helps make uncertainty visible, bridging technical model quality and human trust.

From Numbers to Impact - The New Benchmark Framework

Current research [1][4][10][12] shows that success measurement requires multiple dimensions. In practice, four (sometimes five) have proven effective:

1. Factuality (Truthfulness): 
   Proportion of verifiably correct statements.
2. Stability (Consistency): 
   Variance across identical inputs.
3. Confidence & Explainability: 
   How certain, consistent, and transparent the answers are.
4. Adoption & Business Impact: 
   Usage rates, decision times, and “answer-to-action” ratios.

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Practical Implementation

1. Establish a baseline
       Document current usage and decision processes.
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2. Combine an evaluation suite 
       Measure factuality, stability, confidence, and trust systematically.
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3. Set up monitoring
   Combine quantitative (variance, confidence) and qualitative (survey) data.
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4. Evaluate pilot
   After 8–12 weeks, review against benchmarks.
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5. Iterate, don’t celebrate
   Success measurement is not a one-off audit but an ongoing governance practice.

From Control to Trust - With Clear Benchmarks

Success will no longer mean

“The system makes no mistakes,” but rather,
“We understand when and why it makes them.”

The goal is controlled trust, measurable reliability in a world of probabilities. Organizations that adopt this mindset gain transparency and credibility, with management, compliance, and end users alike.

Conclusion

Measuring success in BI has always been more than a ROI calculation. It was an attempt to make decision quality visible.

With AI, that principle is redefined: It’s not about inventing new metrics, but about combining existing ones correctly and relating them to business outcomes, including the confidence with which a system rates its own answers. Those who master this don’t just measure better, they understand more deeply what “success” truly means in the age of probabilistic systems.