Measuring AI Value: Metrics That Speak to Boards and Regulators

CIOs face a dual mandate with AI: they oversee the infrastructure that sustains trust in data and AI systems, but they must also narrate that infrastructure persuasively. Technical excellence means little if boards see only cost centres and regulators see only opacity. The session addresses how to measure and communicate AI value in terms that resonate beyond IT. Effective measurement requires distinguishing two forms of transparency that executives often conflate. Data lineage describes how information flows through systems: origins, transformations, dependencies. Data provenance records the documented history of specific values: who created or modified them, when, under what parameters. Lineage answers architectural questions relevant to planning and change management; provenance answers forensic questions relevant to audit and dispute resolution. Metrics derived from each capability speak to different audiences. Time to audit readiness concerns board risk committees; value reconstruction time concerns legal and compliance; data preparation hours concern COOs and budget owners. The session presents a metrics framework that translates governance capabilities into business language. Participants will examine how lineage coverage, provenance depth, error detection time, and downstream impact incidents can be reported in ways that demonstrate value rather than merely documenting activity. The goal is not dashboards for their own sake, but communication that builds confidence among stakeholders who will never examine the underlying systems directly. Key Takeaways: - Lineage and provenance serve different purposes and speak to different audiences. - Metrics must translate technical capabilities into business outcomes. - Time-based metrics (audit readiness, error detection, value reconstruction) communicate operational value. - Governance framed as enabling rather than defensive builds broader organizational support.