What is explainable AI (XAI)?

Explainable AI (XAI) refers to methods of artificial intelligence that make decisions, predictions, and results understandable, verifiable, and transparent to humans. The aim is to build trust in AI systems and enable their use in sensitive areas such as controlling, business intelligence, and corporate management. Bissantz ensures explainable AI in AI-powered BI solutions.

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.

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.

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:

This distinguishes explainable AI fundamentally from traditional AI approaches, where only the result is visible, but not the path leading to it.

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 is particularly important in the context of business intelligence and corporate management. Decisions in these areas are based on figures that must be accurate, consistent, and comprehensible.

Explainable AI supports:

AI therefore does not become a replacement for controlling, but rather a tool that deepens analyses and supports decision-making processes.

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.

A key area of application for explainable AI is compliance with regulatory requirements and internal governance policies.

Explainable AI enables:

These characteristics are essential for meeting requirements arising from regulation, data protection, and internal controls. Without explainability, the secure use of AI is hardly possible in many areas of business.

Despite its advantages, explainable AI also has limitations:

• AI systems remain inherently susceptible to errors.
• Transparency does not automatically guarantee correctness.
• Interpreting results requires specialist knowledge.
• Human oversight remains indispensable.

Explainable AI therefore provides the foundation for trust and control, but does not replace expert assessment.

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:

AI functions support, among other things:

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.

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.