Stu Sjouwerman is co-founder and CEO of ReadingMinds, a pioneering AI-moderated interview platform for conducting sentiment analysis.gettyAs businesses increasingly come to rely on AI agents to perform a wide range of customer-facing activities, it’s critical that leaders have confidence in the decisions being made and actions being taken. This includes the ability to fully understand what inputs were used to make decisions and being able to literally go back to those inputs to verify the validity of outputs.This is known as provenance, which is a term that’s common in the world of art and collectibles. Provenance refers to the place and chain of ownership. For instance, a Picasso painting might be traced from its creation by Picasso at a specific time and place through each subsequent owner, with records documenting when and how it changed hands.​In tech circles, the concept of provenance can also be applied to the chain of events and inputs that led to an AI agent’s actions. For example, a customer account has been flagged for renewal risk, but why? What signals led to the decision to flag that account? Can those signals be traced back to their origins? Who said what, in what context and when?To build true executive trust, organizations must establish an auditable chain of evidence for every automated decision.Why Confidence Isn’t The Same As CorrectnessOne influential aspect of AI agents is the unwavering conviction of their outputs. They deliver responses and make recommendations with uncanny confidence, suggesting to users that these outputs must, of course, be correct.But that’s not necessarily the case. Confidence isn’t the same as correctness. The fluency of a synthetic answer isn’t evidence of its accuracy.For AI-generated recommendations to be actionable, they should answer six questions:1. Was the evidence collected from the right people?2. Can every conclusion be traced to its source?3. Was the respondent authentic?4. Did the analysis search for contradictory evidence?5. Is the evidence current?6. Is it strong enough to support a business decision?Every finding acted upon should be explainable in one sentence: “At this moment, this customer said this, expressed this signal with this level of intensity, which indicates this potential business risk or opportunity.”​If that can’t be done, it’s an opinion or hypothesis, but it’s not evidence.The Provenance Problem​If you ask why a specific AI-driven decision was made, the honest answer in most organizations is: “Nobody can tell you.” That’s a problem, because the more you rely on AI to help with operations and marketing, the more important it becomes to explain and justify the decisions made and actions taken.Today’s AI agents can run a large number of tasks, from updating records to flagging accounts for escalation to drafting renewal outreach. That saves organizations time and money while also ensuring that no customer falls through the cracks. However, some organizations have a governance gap that is growing wider in an environment where the pace of agentic AI is accelerating.We’re not talking about a single failure mode or bad decision. We’re talking about a cascade that can quickly reach avalanche proportions.Consider a CRM record that is updated based on a flawed input. That record shapes the next follow-up. The follow-up shares the forecast, which shapes the renewal strategy. By the time a human agent reviews the account, even though every step looks internally consistent, the original error has multiplied, potentially across several systems.The National Institute of Standards and Technology's AI risk management framework identifies explainability as a foundational characteristic of trustworthy AI, along with transparency and interoperability. Transparency explains “what happened.” Explainability addresses “how” a decision was made within the system. Interpretability answers “why” a decision was made and its user context.Most AI governance conversations focus on what these systems are permitted to do. Fewer focus on whether the decisions made can be traced after the fact. Provenance helps bridge the two.Chain Of Evidence For AI OutputsEstablishing provenance doesn’t require rebuilding existing systems, but it should entail three commitments:1. Source citation by default. Any AI output that influences a business decision should include the specific evidence it drew from. A risk flag with no traceable source is an assertion, not evidence.2. Log agent actions with the inputs that drove them. An agent that triggers an escalation, updates a forecast or sends a communication should leave a record: what it knew, where that knowledge came from and when it was last verified. Without that log, review is guesswork.3. Build human review gates for consequential actions. Define thresholds in advance by decision type. A pricing change warrants a different gate than a follow-up email. Enforcing those thresholds and reviewing the audit trail regularly is what AI governance looks like in practice.Your Goal: Developing An Audit Trail That Can Be Used To Support AI Outputs​Take any significant AI-driven decision from the past quarter—a churn-risk flag, a deal advanced in the pipeline or a pricing recommendation that was accepted. Ask whether you can reconstruct the specific evidence and inputs that led to the decision.​If the answer is “no,” the system is not auditable, and that’s a liability. Without provenance, you have an assertion. What you don’t have is evidence to support that assertion.The executives who get ahead of this won’t be simply asking what the AI said. They’ll be asking what the customer said, when they said it, how they expressed it and how strongly they expressed it. Those are the questions a well-governed AI system can answer, and they’re the only questions with answers I believe are genuinely worth acting on.​​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?