In 2025, artificial intelligence (AI) is more embedded than ever in the business landscape. Yet, building and maintaining trust in AI and decisions made by algorithms remains one of the biggest challenges for organizations. In this context, Explainable AI (XAI) is emerging as a cornerstone of responsible AI, empowering companies to achieve algorithmic transparency, meet regulatory demands, and restore trust in AI across all business functions.
Explainable AI is quickly becoming one of the most discussed solutions for businesses striving to build trustworthy, transparent, and compliant AI systems in 2025.
In fact, global consumer trust in AI has fallen from 61% to 53% over the past five years, and 60% of companies using AI admit to trust issues with their algorithmic models.
Adding to the challenge is growing regulatory scrutiny. In the European Union and Spain, transparency is now a legal requirement for certain AI systems, with penalties reaching up to €35 million for serious non-compliance. In this evolving environment, Explainable AI (XAI) is becoming essential to securing business trust and regulatory readiness.
In this article, we’ll explore what Explainable AI is, why it matters more than ever, real-world use cases across key industries, and practical recommendations for successful implementation in your organization.
Summary – Explainable AI & AI Trust at a Glance
- Explainable AI (XAI): A set of techniques that make AI decisions transparent, traceable, and understandable to humans.
- Trust in AI: Explainability builds trust among executives, teams, and customers by clarifying how and why decisions are made.
- Transparency & Compliance: XAI is crucial for meeting regulatory requirements (e.g. GDPR, the EU AI Act) and avoiding costly sanctions.
- Responsible AI: Helps detect and mitigate algorithmic bias or errors, supporting ethical and fair AI in the enterprise.
- Competitive Advantage: Companies embracing explainable AI in 2025 will be better positioned to harness its benefits without losing stakeholder trust.
Explainable AI (XAI) refers to a set of techniques and methodologies that allow humans to understand, interpret, and trust the decisions made by AI systems—particularly those powered by machine learning. In essence, XAI aims to "open the black box" of complex AI algorithms and shed light on how and why specific outcomes are produced.
By leveraging explainable AI techniques, companies can ensure algorithmic transparency across AI systems used for recruitment, finance, healthcare, and beyond.
IBM defines explainable AI as a set of tools that “enable users to understand and trust the results generated by machine learning algorithms.” At Bismart, we believe that explainability is fundamental to improving both transparency and trust in AI-powered systems.
Leading analysts like Forrester also highlight lack of interopretability as one of the main obstacles to widespread AI adoption in business environments.
As more organizations adopt AI technologies, the demand for explainability in machine learning and deep learning models continues to grow.
In practical terms, explainable AI (XAI) allows organizations to understand and justify the decisions made by their AI systems—especially in sensitive, high-stakes scenarios such as:
The ability to interpret AI decisions is crucial for meeting internal governance standards and external AI compliance regulations.
Unlike opaque "black box" models, which produce outcomes without clear reasoning—even to their own developers—XAI enables organizations to trace the logic behind each prediction. This transparency is essential for validating model behavior, identifying errors or biases, and, most importantly, building trust among internal teams and external stakeholders.
The rapid rise of AI in high-impact industries such as finance, healthcare, HR, insurance, and public administration has driven the introduction of strict regulatory frameworks. Notably, the EU Artificial Intelligence Act categorizes certain applications as high-risk, requiring them to be:
Companies like Liferay have already begun auditing their AI systems to align with these transparency principles—proving that explainability is not only feasible, but also a strategic advantage.
In Spain, authorities and labor unions are enforcing similar expectations. AI systems deployed in workplace settings must be auditable and subject to human oversight. Non-compliance can result in fines of up to €35 million, underlining that explainability is no longer just a best practice—it’s a legal necessity.
Implementing explainable AI helps companies stay ahead of upcoming AI laws and future-proof their algorithmic accountability strategy.
Explainable AI helps address this by enabling:
Explainability is especially critical in processes where automated decisions directly impact people, such as hiring, credit scoring, or healthcare eligibility. Without it, organizations risk not only legal exposure, but reputational damage and public distrust.
Explainable AI is no longer a theoretical ideal—it’s reshaping critical processes across industries where transparency and traceability are non-negotiable.
Two of the most affected sectors are finance and human resources, where algorithmic decisions directly impact people’s lives and organizations' reputations.
In the financial sector, transparency isn’t optional, it’s essential. Banks, insurers, and investment firms increasingly rely on AI models to make high-stakes decisions, from loan approvals and fraud detection to risk scoring. A biased or opaque model can lead to severe economic, legal, and reputational consequences.
Explainable AI makes it possible to use these technologies responsibly. Take loan approvals, for example: machine learning models assign risk scores that influence whether a customer is approved or denied.
With XAI, financial institutions can clearly explain the outcome—such as how factors like income, credit history, and debt ratio influenced the decision. Using XAI tools such as SHAP and LIME, financial institutions can achieve both regulatory compliance and algorithmic interpretability.
This transparency boosts customer trust and ensures compliance with the "right to explanation" outlined in EU regulations on automated decision-making.
XAI is also a powerful tool for risk management and regulatory oversight. Institutions like the Bank of Spain and the European Central Bank now require that all AI-driven decisions be traceable, explainable, and auditable—especially after the failures linked to opaque risk models in past financial crises.
Spanish bank BBVA has developed an open-source library called Mercury, designed to integrate explainability modules into AI systems. The platform allows technical teams to analyze which variables influence outcomes in financial models—from credit scoring to personalized offer recommendations—enabling both technical validation and ethical accountability.
Major organizations like BBVA and Telefónica have also committed to subjecting their algorithms to ethical evaluations and regular audits, anticipating the requirements of the EU AI Act, which classifies many financial AI applications as high-risk.
In short, XAI has become synonymous with trust and compliance in finance. Executive teams gain clearer insights for decision-making, regulators get the oversight they demand, and customers receive decisions they can understand and challenge if necessary.
These practices showcase how leading banks are using explainable AI to align with AI governance frameworks and reduce model risk.
The use of AI in human resources is accelerating, with algorithms now supporting tasks like CV screening, candidate evaluation, and even employee performance prediction. However, because these decisions directly affect people, the risks of bias, unfairness, and reputational damage are significantly higher.
In 2018, Amazon deployed an AI system to automate hiring. The model, trained on historical resumes dominated by male applicants, began systematically penalizing female candidates. Despite efforts to fix the bias, Amazon couldn’t guarantee the model wouldn’t continue finding new ways to discriminate—so the project was ultimately scrapped.
This example illustrates a critical lesson: if you can’t explain how an algorithm makes decisions, you can’t control its biases.
Implementing XAI in HR not only prevents discrimination but also aligns with the emerging global standards for responsible AI in recruitment and promotion processes.
With XAI, HR departments can audit and understand AI-driven recommendations. For example, they can see that a candidate was prioritized based on years of experience, technical certifications, or specific skill sets—rather than arbitrary or biased factors. If the model begins rejecting candidates from certain demographics at higher rates, those patterns can be detected and addressed.
Moreover, in many jurisdictions, candidates rejected by automated systems have the legal right to request an explanation. XAI allows companies to comply with these regulations and offer clear, objective, and verifiable justifications.
From the recruiter’s perspective, explainable AI also enhances user confidence and decision quality. Hiring managers are more likely to adopt AI tools when they receive transparent insights—knowing, for instance, why the system recommended Candidate A over Candidate B.
European legislation is quickly catching up. Under the EU AI Act 2024, systems used in HR—such as those for scoring, promotions, or automated dismissals—are deemed high-risk. This classification brings with it strict obligations: explainability, human oversight, and regular auditing.
Forward-thinking companies are already preparing by investing in XAI platforms and algorithmic audit frameworks, understanding that regulatory compliance will soon be mandatory.
Adopting explainable AI in HR goes far beyond avoiding fines or legal disputes. It leads to better talent decisions, supports diversity and inclusion initiatives, and strengthens your reputation as a transparent and equitable employer.
In a landscape where algorithmic fairness and transparency in hiring are increasingly critical, having access to professionals with deep expertise in technology, data, and analytics is not just a competitive edge—it’s a business imperative.
At Bismart, we specialize in helping organizations find the right tech talent. Our recruitment team is dedicated to identifying highly qualified IT and data professionals, using a rigorous and agile selection process tailored to your business needs.
Adopting Explainable AI isn’t just a technical decision, it’s a strategic one. Successful implementation requires alignment between technology, governance, and organizational culture. Below are five key recommendations to help you implement XAI effectively and sustainably.
To successfully implement explainable AI, companies must combine technical best practices with robust AI governance and cross-functional collaboration.
The first step is to identify which AI systems require the highest level of explainability. Prioritize those that:
These high-stakes scenarios should be the first to incorporate XAI, ensuring transparency and traceability from the outset.
Complexity isn't always better. In some cases, simpler, more interpretable models—like decision trees or logistic regression—offer the best balance of accuracy and explainability.
When advanced models (e.g., deep neural networks) are necessary, pair them with explainability tools such as:
Using XAI tools such as LIME or SHAP not only provides technical traceability, but also significantly improves trust in AI, both among technical users and management profiles.
These explainability techniques help data teams monitor AI model behavior and ensure ethical AI deployment at scale.
A good explanation is only valuable if it’s understandable. That means designing user-friendly interfaces tailored to various stakeholders—data scientists, business users, executives, and customers alike.
Effective features may include:
Remember: great explainability is as much about communication as it is about computation.
XAI implementation should be cross-functional from the start. Engage:
This inclusive approach ensures that explanations are practical, legally sound, and aligned with business goals. It also helps define early on what needs to be explained and to what level of detail.
Engaging non-technical stakeholders in explainability efforts strengthens overall AI governance and ensures that algorithmic transparency is organization-wide.
Explainability isn't a one-off task—it’s a continuous process. To make it sustainable:
These practices not only future-proof your AI governance but also align with evolving regulatory requirements and industry best practices.
In today’s AI-driven landscape, Explainable AI is quickly becoming the backbone of responsible, auditable, and socially accepted artificial intelligence.
As we move through 2025, transparency is no longer a nice-to-have, it’s a non-negotiable expectation from regulators, customers, and employees alike. In this context, adopting XAI is not merely a technical upgrade; it’s a strategic imperative. Companies that prioritize explainability and ethical AI development will lead the way in building trustworthy, transparent, and high-performing AI ecosystems.
Organizations that embed explainability into their AI systems today will not only stay ahead of regulatory demands and avoid reputational risks—they will also gain a sustainable competitive edge rooted in trust. And in the world of AI, trust is what drives adoption, user confidence, and long-term value.
Put simply, explainability is no longer optional. It’s a defining trait of responsible AI and the key differentiator between companies that inspire confidence and those that raise concern.
The time to act is now. Let’s open the black box, and use AI not just to automate, but to build systems that are transparent, ethical, and truly aligned with business and societal values.
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