Explainable AI (XAI) in 2025: Guide to enterprise-ready AI

As AI tools become more advanced, more computations are done in a “black box” that humans can hardly comprehend. This approach is problematic since it prevents transparency, trust and model understanding. After all, people don’t easily trust a machine’s recommendations that they don’t thoroughly understand. Explainable Artificial Intelligence (XAI) comes in to solve the black box problem.

XAI explains how models draw specific conclusions and what the strengths and weaknesses of the algorithm are. XAI widens the interpretability of AI models and helps humans to understand the reasons for their decisions.

What is XAI?

XAI is the ability of algorithms to explain why they come up with specific results. While AI-based algorithms help humans to make better decisions in their businesses, humans may not always understand how AI reached that conclusion. XAI aims to explain how these algorithms reach these conclusions and what factors affect the conclusion. In Wikipedia, XAI is defined as:

Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that human experts can understand the results of the solution. It contrasts with the concept of the “black box” in machine learning, where even their designers cannot explain why the AI arrived at a specific decision.

XAI for generative AI

XAI is not confident to traditional machine learning. Large language models are based on deep learning and they also operate in a black box manner. If users can’t understand why LLMs arrive at certain responses, securing LLMs and making them useful as part of enterprise generative AI wouldn’t be possible.

LLM providers like Anthropic publish research on the topic, outlining parameters that impact LLM behavior,¹ however more comprehensive XAI solutions in generative AI are yet to come.

Why is it relevant now?

Shortly, XAI creates a transparent environment where users can understand and trust AI-made decisions.

Businesses use AI tools to improve their performance and make better decisions. However, besides benefiting from the output of these tools, understanding how they work is also vital. The lack of explainability prevents companies from making relevant “what-if” scenarios and creating trust issues because they don’t understand how AI reaches a specific result.

Gartner states that the lack of explainability is not a new problem. However, AI tools become more sophisticated to deliver better results in businesses, and this problem draws more attention now. These more complex AI tools are performed in a “black box,” where it is hard to interpret the reasons behind their decisions.

While humans can explain simpler AI models like decision trees or logistic regression, more accurate models like neural networks or random forests are black-box models. The black box problem is one of the main challenges of machine learning algorithms. These AI-powered algorithms come up with specific decisions, but it is hard to interpret the reasons behind these decisions.

XAI is important as it explains black box AI models, helping humans understand how they work. It clarifies decision reasoning, different outcomes, and model strengths/weaknesses. As businesses better understand AI models and their solutions, XAI builds trust between companies and AI. This technology allows businesses to use AI models to their full potential

How does it work?

Today’s AI technology delivers a decision or recommendation for businesses by benefiting from different models. However, users can’t easily perceive how the results are achieved or why the model didn’t deliver a different result. Besides delivering accurate and specific results, XAI uses an explainable model with an explanation interface to help users understand for the model works.

We can categorize XAI under two types:

Explainable models like Decision Trees or Naive Bayes

These models are simple and quickly implementable. The algorithms consist of simple calculations that can even be done by humans themselves. Thus, these models are explainable, and humans can easily understand how these models arrive at a specific decision. To observe the reasons behind that specific decision, users can quickly analyze the algorithm to find the effects of different inputs.

Explaining black box models

Explaining more complex models like Artificial Neural Networks (ANNs) or random forests is more difficult. They are also referred to as black box models due to their complexity and the difficulty of understanding the relations between their inputs and predictions.

Businesses use these models more commonly because they are better performing than explainable models in most commercial applications. To explain these models, XAI approaches involve building an explanation interface that has data visualization and scenario analysis features. This interface makes these models more easily understandable by humans. Features of such an interface include:

Visual analysis

XAI interfaces visualize outputs of different data points to explain the relationships between specific features and the model predictions. In the above example, users can observe the X and Y values of different data points and understand their impact on the inference of absolute error from the color code.

In Figure 3, the feature represented in the X axis seems to determine the outcome more strongly than the feature represented in the Y axis.

Scenario analysis

XAI analyzes each feature and analyzes its effect on the outcome. With this analysis, users can create new scenarios and understand how changing input values affect the output. In the above example, users can see the factors that positively/negatively affect the predicted loan risk.

What are its advantages?

Rather than improving performance, XAI aims to explain how specific decisions or recommendations are reached. It helps humans how/why AI behaves in certain ways and builds trust between humans and AI models. The main advantages of XAI are:

• Improved explainability and transparency: Businesses can understand sophisticated AI models better and perceive why they behave in certain ways under specific conditions. Even if it is a black-box model, humans can use an explanation interface to understand how these AI models achieve certain conclusions.
• Faster adoption: As businesses can understand AI models better, they can trust them in more important decisions 
• Improved debugging: When the system works unexpectedly, XAI can be used to identify problems and help developers to debug the issue.
• Enabling auditing for regulatory requirements

What are the XAI limitations and ways to mitigate them?

Certainly! Here’s the revised version in bullet point format:

• Inconsistent definitions: The lack of standardized definitions for terms like “explainability” and “interpretability” in explainable AI research creates confusion, hindering a shared understanding.
  • Tips: To effectively manage this, researchers should work toward consensus by developing common frameworks and hosting collaborative workshops.
• Lack of practical guidance: While many artificial intelligence techniques emerge, practical advice on implementing and testing these methods in real-world contexts is limited.
  • Tips: Creating detailed best practices, sharing case studies, and offering training can help professionals effectively manage these challenges.
• Challenges in building trust: For human users to appropriately trust AI systems, more research is needed on how explanations can build trust, especially among non-experts.
  • Tips: Interactive explanations and improved communication strategies can help ensure users can trust their artificially intelligent machine partners.
• Debate over transparency methods: Some argue that explainability may oversimplify complex models, suggesting a shift to inherently interpretable models or rigorous model evaluation.
  • Tips: Combining explainable models with rigorous testing ensures prediction accuracy and reliability, particularly for future warfighters in high-stakes fields.
• Need for broader focus in XAI research: As AI explainability evolves, it must expand beyond technical aspects to address social transparency, ensuring the emerging generation of AI systems considers the broader impact on human users.
  • Tips: Interdisciplinary research and user-centered design can guide this evolution.

How does XAI serve AI ethics?

While AI becomes more integrated into our lives, the importance of AI ethics increases. However, the complexity of advanced AI models and lack of transparency create doubts about these models. Without understanding them, humans can’t decide if these AI models are socially beneficial, trustworthy, safe, and fair. Thus, AI models need to follow specific ethical guidelines.

Gartner gathers AI ethics under five main components:

• Explainability and transparent
• Human-centric and socially beneficial
• Fair
• Secure and safe
• Accountable

One of the primary purposes of XAI is to help AI models to serve these five components. Humans need to have a deep understanding of AI models to understand if they follow these components. Humans can’t trust an AI model that they don’t know how it works. By understanding how these models work, humans can decide if AI models follow all of these five characteristics. 

What are the companies providing XAI?

There are various tools that deliver XAI explanation interfaces to clarify complex AI models, including:

1. AI Governance Tools: AI governance tools establish guidelines and frameworks that mandate transparency and documentation of AI models, which helps in understanding how decisions are made and ensures adherence to ethical standards.
2. Responsible AI Platform: Responsible AI tools often include built-in features for model interpretability and explainability, such as tools for visualizing model behavior and generating human-understandable explanations for predictions.
3. MLOps: MLOps tools integrate practices for model versioning, monitoring, and auditing. This way, they can track model changes and explain them, facilitating insights into how models evolve and how their decisions are made over time.
4. LLMOps: LLMOps tools manage large language models, including explainability features that help decode complex model outputs and provide clearer insights into how language models generate their responses.

There are also tools that fall under different categories, such as:

• Google Cloud Platform: Google Cloud’s XAI platform uses your ML models to score each factor to see how each of them contributes to the final result of predictions. It also manipulates data to create scenario analyses 
• Flowcast: This API based solution aims to unveil black box models by being integrated into different company systems. Flowcast creates models to clarify the relationships between input and output values of different models. To achieve that, Flowcast relies on transfer learning and continuous improvement.

Here is a list of more AI-related articles you might be interested in:

• State of AI technology
• Future of AI according to top AI experts
• Advantages of AI according to top practitioners
• AI in Business: Guide to Transforming Your Company

If you have questions on Explainable AI (XAI), feel free to contact us:

External Links

• 1. Mapping the Mind of a Large Language Model \ Anthropic.