Artificial intelligence (AI) is transforming the business world at a dizzying pace, driving automation, improving decision making and opening up new opportunities for innovation. However, as AI systems become more sophisticated, a legitimate concern arises: the lack of transparency in their inner workings. We rightly ask: how do these machines make decisions? What logic do they follow to reach certain conclusions? These questions are answered by Explainable AI (XAI), a discipline that seeks to shed light on the inner workings of AI, fostering trust and facilitating more ethical and understandable use. 

In the past, accuracy was the primary metric for evaluating an AI model, but in critical business contexts, accuracy alone is not enough. It is necessary to understand how each result is generated. Explainability becomes a key element in building trust in AI systems and ensuring their responsible use.

Explainable AI (XAI) offers concrete benefits for companies. First, it builds trust by enabling teams to understand the decisions generated by AI systems, facilitating their integration into workflows. Second, analyzing the reasoning behind the models allows identifying and correcting biases in the data or algorithms, preventing unfair or discriminatory decisions. In addition, XAI helps organizations comply with regulations such as GDPR, which requires transparency in the use of AI, especially in decisions that directly affect people. Finally, analyzing model explanations can reveal areas for improvement, optimizing their performance and increasing their effectiveness.

Several techniques are used to implement XAI: 

• Inherently interpretable models, such as decision trees or linear regression, which are transparent by design. 

• Post-hoc techniques, which explain the behavior of more complex models, such as LIME (Local Interpretable Model-agnostic Explanations) or SHAP (SHapley Additive exPlanations). 

• Visualizations, which clearly represent how the AI arrives at its conclusions. 

While XAI focuses on the interpretability of current AI models, quantum computing is presented as a disruptive force that will transform information processing. Although still under development, this technology promises to revolutionize fields such as optimization, simulation and cryptography. 

The rise of Explainable AI (XAI) represents a crucial move towards more accountable and understandable AI. In a context where AI increasingly influences business decisions, transparency and interpretability become key pillars .