Explainable AI (XAI): Why It Is Crucial for Trust, Compliance, and Business Impact

10.02.2026

Martin Gaßner

Management Summary

Explainable AI (XAI) is a key success factor for the successful long-term use of AI. It creates transparency in automated decisions, actively involves stakeholders in the decision-making process, and thereby strengthens trust and acceptance. At the same time, XAI helps organizations identify risks early on, critically evaluate results, and support documented compliance with regulatory requirements, such as those outlined in the EU AI Act.

Comprehensible explanations facilitate dialogue between business units, IT, and management, ensuring that AI applications address real user needs and are actually used in everyday work. In short: Without explainability, AI often remains technically brilliant but ineffective.

AI is now used in applications ranging from sales forecasting to generative AI applications. At the same time, the EU AI Act is introducing new requirements for the use of AI: automated decisions must be made transparent, systems must involve humans in the decision-making process, and they must function robustly.

Imagine you’re developing a new AI application. The technology is technically capable and aligned with the defined requirements to your business goals, and designed to support users in their daily decisions. The application is expected to improve operational decisions.

Yet users may still choose not to use it. Feedback like

“I don’t trust the AI as much as I trust my own judgment.”

“It takes too long for our experts to review the results.”

“Once, the result was obviously wrong, and I don’t know why.”

show that acceptance is lacking, even though the model meets its technical performance targets. The missing elements are often trust and transparency. Users want to be part of the decision-making process, not just recipients of instructions. They’re aware of their responsibilities and want to weigh the risks.

This is exactly where Explainable AI (XAI) comes in. XAI makes decisions understandable, builds trust, and can support transparency and compliance requirements. In this article, we’ll show how XAI makes a difference and why it’s more than just a “nice-to-have” today.

What is Explainable AI?

Explainable AI (XAI) is an umbrella term for methods that make the decisions of AI models transparent, traceable, and explainable.
The goal is to enable people to understand, review, and use AI-driven decisions responsibly.

This means:

Traceability: Which features influence a decision?

Interpretability: Why was this specific result generated?

Tailored presentation: Explanations are adapted to specific departments, management, or IT.

XAI serves as a bridge between complex model behavior and the people who use its outputs.

XAI in Practice: Ensuring That AI Is Actually Used

Many AI projects fail not because of the quality of the model, but because users do not trust the results or cannot incorporate them into their decisions.
Explainable AI (XAI) addresses precisely this acceptance issue by making AI decisions explainable, verifiable, and thus usable in a responsible manner.

This problem is particularly evident in practice:
In many projects, the initial focus is on model performance, as measured by accuracy or other metrics.
However, this is not sufficient for productive use.
Users must understand why a particular result is produced in order to contextualize it, question it, or integrate it into existing decision-making processes.

Based on our experience with explainable AI, we have distilled the following principles:

User-centered

Explanations should reflect users’ knowledge and workflows. Explanations should be provided in a format that is easy for users to understand and can be integrated into existing processes. It is essential to regularly gather user feedback on this.This helps to keep training efforts and implementation friction to a minimum, while increasing acceptance and practical use.

Strategic

Explainability must be considered from the very beginning and on an ongoing basis, from the requirements phase through to operation. Starting with the users’ most critical decisions—which must be supported as early as possible—XAI should be implemented in a targeted and iterative manner. This can best be achieved through agile development and consistent requirements management. This allows the greatest benefits of XAI to be realized as quickly as possiblewithout creating unnecessary delays.

Meaningful

Relevant decisions require precise, transparent, and robust explanations based on sound methodologies. In this way, XAI builds trust, visibly reduces risks associated with its use, and can be applied even in complex cases.

So how exactly does XAI add value to a project? Below, we outline the general phases in the lifecycle of an AI application (based on https://vdsbook.com/02-dslc) and highlight where XAI provides benefits.

Conceptualization:

Stakeholders rarely ask for XAI directly. Nevertheless, they need to understand how the system works and the factual basis on which decisions are made. That is why we recommend paying attention to requirements—including those related to transparency and traceability—early on. Domain experts should be involved early on to understand how users can best interact with an AI application. The resulting requirements must be prioritized in terms of cost and benefit. To ensure that signals are recognized in a timely manner and that explainability is planned for early on and implemented cost-effectively, it is essential to draw on relevant experience with XAI. Early planning lays the foundation for the project to achieve its goal, whereas planning at a later stage leads to costly ad hoc solutions or delays.

Data Processing:

Data is the raw material for every AI system. That is why it is crucial to understand the data to ensure that the AI works with high-quality raw materials. This understanding builds trust among stakeholders and reduces internal resistance. XAI supports this by making patterns visible in the data used by AI models, thereby providing insights into the underlying business context and identifying gaps. This often uncovers issues in the data collection process that compromise data quality. When XAI is used to make data understandable, it also facilitates compliance with regulatory requirements such as transparency, data quality, and robustness—as mandated by the EU AI Act for high-risk systems. At the same time, higher data quality can improve model performance, thereby improving the entire AI system. Later in the project, there is often little room for adjustments because the pressure to meet deadlines and existing software leave little leeway. Those who invest early in this area secure advantages that pay off, especially in critical systems.

Explainable AI and the EU AI Act:

The EU AI Act requires providers and operators of high-risk AI systems to ensure transparency, traceability, and human oversight.
XAI is a key technical tool for implementing these requirements in practice, for example through documented decision-making logic, feature analyses, and traceable model outputs.
Making data and model behavior understandable early on with the help of XAI not only facilitates regulatory compliance but also improves data quality and model robustness—and thus the performance of the entire AI system.

Development:

XAI explains how a model arrived at a decision. During the development phase, this makes it possible to align the model’s logic with the desired behavior. Through dialogue with experts, it is possible to validate which correlations discovered in the data align with domain knowledge. This allows errors in the model to be detected earlier and with less effort than through experimental evaluation alone. Better and faster feedback leads to a faster development while maintaining the same level of risk, resulting in lower development and remediation costs. Furthermore, the exchange with experts builds trust in the model. Transparent logic also facilitates compliance with regulatory guidelines and reduces regulatory risks, which is often a prerequisite for deployment in sensitive areas.

Operation:

Explanations create transparency and engage users. This builds trust and increases acceptance. At the same time, users can critically assess the application’s outputs, thereby reducing the risk of incorrect decisions—a risk that can only be minimized, but never entirely eliminated, in learning systems. In addition, explanations can be stored long-term to monitor the model’s decision-making logic. Changes become visible early on, ensuring reliability and making maintenance work predictable.

It follows that explainability is a cross-cutting issue for AI and data science projects. It must be considered early on to support reliable long-term use in the form of acceptance, robustness, and compliance.

In the following, we’ll use an example from our consulting practice to show how this works in practice.

Case Study: Background

One of our manufacturing clients developed an AI-powered tool to automate production planning. This required forecasting monthly product sales. However, the business units questioned individual forecasts or speculated about influencing factors. Due to a lack of trust, the previous tool was hardly used.

Solution

Our colleagues began by analyzing the existing process to ensure that the automated process would seamlessly replace it. Since the production decision was central to the customer’s value creation process, we interviewed process experts and concluded that feature-importance analyses were needed so that planners could translate forecasts into operational decisions.

Result

The analysis revealed differences between the model logic and the business logic. Experts from the business units were now able to understand how changes in the input data affected the forecast. This transparency helped build trust—not only in the model, but also in the collaboration between the development team and the business units. As a result, the application was used regularly, meaning that forecasts were now based on solid data rather than on intuitive expert estimates. Thanks to more accurate forecasts, our client was able to respond more quickly and effectively to changes in the market.
Learning

AI only delivers business value when its decisions align with business logic and are understood by subject matter experts.
Explainable AI makes this alignment visible by showing how model decisions are reached, thereby becoming a an important factor in long-term adoption.

Conclusion

Key insights on Explainable AI:

XAI is crucial for building trust and acceptance of AI systems

XAI identifies risks earlier and supports demonstrable compliance (e.g., the EU AI Act) through transparency and human involvement.

High model quality alone is not enough: Without explainability, AI often goes unused and thus has no business impact.

XAI should be integrated throughout the entire AI lifecycle

XAI serves as a bridge for translation and communication between business units, IT, and management—and is what makes AI capable of integrating with real-world decisions and processes.

Explainable AI isn’t just a “nice-to-have”; it’s the key to putting AI into practice. Explainability builds trust, fosters acceptance, and helps manage technical risks. Anyone investing in AI should consider XAI from the very beginning so that technical excellence translates into real impact.