Artificial intelligence has become an essential component in the pharmaceutical and food & beverage industries, where it is used to control product quality, optimize processes, and ensure consumer safety.

However, AI does not always explain how it reaches its decisions, which can make it difficult to trust its results and validate them during audits.

Explainability (Explainable AI, XAI) solves this problem: it allows AI decisions to be understandable, defensible, and traceable, becoming a strategic requirement for companies operating in regulated environments.

This article analyzes the strategic benefits and regulatory compliance associated with explainability.

What AI Explainability Consists Of

Explainability involves making AI decisions transparent by showing which data, sensors, or patterns influence the outcomes.

In other words, it is not only about seeing the alert or recommendation but understanding and documenting why it was triggered.

This is especially critical in industries where each decision can affect product quality or consumer safety. For example, identifying which factor triggered a contamination alert in a beverage batch allows immediate and justifiable corrective actions.

Strategic Benefits of Explainability

Explainability provides clear advantages for industrial operations and risk management:

• It improves operational efficiency by enabling teams to quickly understand why AI issues alerts and act precisely.

• It reduces errors and waste by avoiding unnecessary interventions in production.

• It enables defensible decisions that can be verified by internal or external auditors.

In addition, it helps ensure product safety and quality. By understanding which factors influence an alert, plant personnel can act on real causes rather than false positives.

Regulatory Compliance

Current regulations, such as the EU AI Act, require high-risk AI systems to be:

• Transparent: users can see which data or processes influence each AI decision.

• Understandable: the explanation is presented so that operators, auditors, and regulators can interpret it without being algorithm experts.

• Auditable: all decisions are recorded for internal and external reviews, ensuring traceability and legal compliance.

Compliance ensures that AI decisions can be reviewed, challenged, and verified during quality, safety, or regulatory audits, while also increasing plant-team trust and minimizing operational risks.

It’s Not an Option: It’s Strategy

Explainability turns industrial AI into a trustworthy and defensible tool. Beyond improving efficiency and product safety, it also ensures regulatory compliance and strengthens the confidence of auditors, operators, and consumers.

In critical environments, XAI is no longer optional—it has become a strategic element for a company’s sustainability and reputation.