At a glance
- creates transparency and trust in AI systems
- reduces risks through verifiable results
- is a key prerequisite for AI in controlling and business intelligence
- combines AI with data models, business logic, and traditional analytical systems
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Explainable AI definition
Explainable AI (XAI), also known as “explainable artificial intelligence,” refers to methods and system architectures that make it possible to present the functionality, decision logic, and results of AI systems in an understandable and verifiable way. The aim is not merely to provide results, but also to make transparent how those results were produced.
While many modern AI models—in particular neural networks and large language models—operate as so-called “black boxes,” explainable AI takes the opposite approach: individual computational steps are made visible, intermediate results can be examined, and the data and models used remain traceable.
Explainable AI is therefore not merely a technical concept, but a fundamental principle for the responsible use of artificial intelligence—particularly in the context of business intelligence, predictive analytics, and data-driven corporate management.
„With DeltaMaster, we can trace every analysis step all the way down to the underlying data. That is why we accept AI-supported insights in our reports, because we can verify them at any time.“Head of Controlling
Industrial company
Trusted by market leaders and hidden champions
Why is explainable AI important for companies and business intelligence?
The importance of explainable AI stems from the limitations of modern AI systems. Many powerful models deliver results without making it transparent how those results were produced.
This gives rise to several key challenges:
- Lack of traceability: Decisions cannot be verified or explained.
- Susceptibility to errors: AI systems can produce incorrect or biased results.
- Lack of trust: Users have limited confidence in results they do not understand.
- Risks for companies: Incorrect decisions can result in financial, legal, or reputational damage.
Explainable AI addresses these issues by providing transparency, auditability, and understanding. This is particularly important in controlling and corporate management, where decisions must not only be made but also justified.
How does explainable AI (XAI) work? – Methods and principles
Explainable AI is based on the principle of breaking down complex AI processes into understandable and verifiable steps. Instead of presenting an opaque overall decision, the path leading to the result is made transparent.
Key operating principles include:
- Separation into individual steps: AI computations are divided into individual processing steps that can be traced separately.
- Verifiable intermediate results: Each processing step produces results that can be reviewed and validated.
- Combination with traditional systems: Explainable AI combines AI with database logic, mathematical models, and rule-based methods.
- Transparent data basis: The data sources used and their influence on the result remain visible at all times.
- Reproducibility: Results can be generated and verified again under the same conditions.
This distinguishes explainable AI fundamentally from traditional AI approaches, where only the result is visible, but not the path leading to it.
Explainable AI and human-in-the-loop
A central principle of explainable AI is the concept of human-in-the-loop. Here, humans remain an active control and decision-making authority. This means:
- AI provides analyses, forecasts, and recommendations for action.
- Results are understandable and verifiable.
- The final assessment and decision remain with the human.
Explainable AI therefore enables AI results to be reviewed, but does not replace human responsibility. This interaction is particularly important in areas such as controlling, reporting, and financial analysis.
Explainable AI in controlling and business intelligence
In the context of business intelligence and corporate management, Explainable AI is of particular importance. Here, decisions are based on figures that must be correct, consistent, and comprehensible.
Explainable AI supports this by:
- Transparent analyses: causes of variances are presented in a comprehensible way
- Reliable decision foundations: results are verifiable and reproducible
- Integration of context: figures are linked with additional information
- Comprehensible recommendations for action: measures can be derived logically
This way, AI does not replace controlling but becomes a tool that deepens analyses and supports decision processes.
Explainable AI and compliance: why transparency is indispensable for AI
A central area of application for Explainable AI lies in complying with regulatory requirements and internal governance guidelines.
Explainable AI enables:
- Documentation of all processing steps
- Traceability of calculations and data sources
- Auditability through audit trails and logs
- Reproducibility of results
These properties are crucial for meeting requirements arising from regulation, data protection, and internal control. Without explainability, a safe use of AI is hardly possible in many areas of a company.
Limits of Explainable AI
Despite its advantages, Explainable AI also has its limits:
- AI systems remain inherently error-prone
- transparency does not automatically guarantee correctness
- interpretation of the results requires expertise
- human control remains indispensable
Explainable AI thus creates the foundation for trust and control – but does not replace professional assessment.
Explainable AI examples
Explainable AI is used in many areas, particularly wherever decisions need to be understandable and traceable:
These use cases demonstrate that explainable AI is particularly relevant wherever trust, traceability, and responsibility are central considerations.
Where would you like to ensure Explainable AI (XAI)?
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Bissantz and explainable AI
Bissantz consistently follows the explainable AI approach in its use of AI for business intelligence. The aim is to leverage the benefits of modern AI without sacrificing transparency and control.
In DeltaMaster and the DeltaApp, AI is used in such a way that:
- analyses remain comprehensible rather than appearing as a black box;
- calculations are structured and verifiable;
- results are presented in a visually understandable way;
- relationships and interdependencies are clearly recognizable
AI functions support, among other things:
- the automatic detection of anomalies and relationships;
- the generation of comments on key performance indicators and variances;
- the interpretation of reports and dashboards;
- the derivation of recommendations for action.
Bissantz consistently follows the principle of human-in-the-loop: AI provides support, while humans make the decisions.
Another key element is the complete traceability of all steps—from the underlying data to the interpretation. This approach is consistent with the concept of Haptic Reasoning, in which analytical relationships are presented visually in a way that allows them to be intuitively understood and verified.
This creates an approach in which AI does not serve as a substitute for analytical thinking, but as a tool that increases transparency, makes relationships visible, and supports informed decision-making.
| Function | Benefit |
|---|---|
| Detection of anomalies | automatically uncovers relevant relationships in the data |
| Automatic commentary | explains KPIs and variances in an understandable way |
| Interpretation of reports | makes dashboards and reports comprehensible |
| Derivation of recommended actions | logically derives measures from the analyses |
| Traceability of all steps | verifiable from the data basis through to the interpretation |
„For us, it was essential that AI results remain auditable and reproducible. The traceability of all steps meets our governance requirements and builds trust among the business departments.“IT Manager
Financial services provider
Frequently asked questions
What does explainable AI mean in simple terms?
Explainable AI means making the decisions of AI systems understandable and traceable so that people can review and assess them.
Why is explainable AI important?
Explainable AI is important because many AI systems otherwise operate as black boxes, making their decisions impossible to verify—a risk for companies.
Is explainable AI the same as artificial intelligence?
No. Explainable AI is not a separate type of AI, but rather an approach for bringing transparency and traceability to AI systems.
Where is explainable AI used?
It is used primarily in business intelligence, controlling, financial analysis, medicine, and wherever decisions need to be understandable and traceable.
How does Bissantz implement explainable AI in DeltaMaster?
DeltaMaster combines AI functions with fully traceable computational steps: comments are generated with AI support based on transparent data models and definable key performance indicator logic. Every step—from the data source to the recommendation for action—can be verified. Explainability is not an add-on, but a fundamental principle.
Summary
Explainable AI is the answer to a central challenge posed by modern AI: How can powerful models be used without giving up control, trust, and responsibility? By making AI decisions transparent, verifiable, and reproducible, explainable AI becomes a fundamental prerequisite for the responsible use of AI in controlling, Business Intelligence, and corporate management. With DeltaMaster, Bissantz consistently implements this principle as a combination of AI-supported analysis, visual comprehensibility, and human judgment.