The era of optional AI transparency is over. According to our own research, the mandate is clear: some 87% of CX leaders agree that AI transparency will be non-negotiable for any customer-facing AI within two years. This shift isn’t just about simple disclosure; it’s about a foundational requirement for trust.

But transparency is not enough. We are seeing an increased emphasis on explainability—the technical ability to view, audit, and understand the steps an AI took to arrive at a recommendation or decision. In fact, 72% of UK CX leaders say it is very important or mission-critical that AI systems can show their reasoning.

To succeed with AI in CX, it’s important that AI enabled interactions provide enough clarity for customers to understand the nature of the interaction and enough context for employees to understand what the system did and why. When a customer challenges an outcome, the organisation should be able to provide an explanation that is accurate, consistent, and understandable.

If an AI agent resolves a complex billing issue or suggests a specific technical fix, a human agent shouldn’t have to guess at the logic. Real trust is built when we can pull back the curtain on the decision-making process, seeing the “chain of thought” or the specific knowledge sources used to ground a response. This level of visibility transforms AI from an opaque tool into an auditable, understandable partner.

The ultimate goal of transparency and explainability is to ensure that humans stay in control as AI increasingly handles service interactions. When AI provides a clear line of reasoning, it empowers employees to provide effective oversight. This human-in-the-loop model is essential for maintaining control over brand values and ensuring that automated interactions remain empathetic and accurate. Without explainability, oversight becomes impossible, and customer control is lost.

Explanation and accountability are key

As we master transparency and explainability, the next frontier is accountability. The expectation is not that AI will work perfectly every time, but that systems are in place to identify gaps, learn from feedback, and ensure human oversight and accountability. If an AI system makes a mistake, as all systems eventually do, how do we ensure there is a clear path to remediation?

Accountability means having integrated governance structures in place, like review and monitoring embedded in the product development lifecycle, executive oversight, and independent third-party audits, to ensure that when issues arise, they are identified and corrected quickly. It involves moving from passive monitoring to active supervision, where every AI feature is continuously assessed for its impact on fairness and safety.

For CX teams, this means they will increasingly need to demonstrate not only that AI improves efficiency, but also that it can be responsibly governed when it directly impacts customers.

At the end of the day, the path to value with AI depends on closing the trust gap. Customers are increasingly aware of AI’s presence in their daily lives, and their willingness to engage hinges on whether they feel protected and understood.

By prioritising transparency and explainability, and building systems that enable human oversight and accountability, we do more than just follow the law. We create a sustainable environment where innovation can thrive because it is built on a foundation of reliability and respect. In this new era, the most successful companies won’t just be the ones with the most powerful AI—they’ll be the ones that people can actually trust.