Trusting Enterprise Agents: Value, Ownership and Autonomy Evidence

AI Engineer15m 4s
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    Video summary

    Michele Grant separates a working demonstration from operational adoption and net business value. A hypothetical merger-integration reporting agent illustrates three measures: report cycle time, reassigned analyst capacity after costs, and the time needed to detect a material slip. The merger values and savings are scenario assumptions, not audited results of a deployed AI system.

    Michele Grant assigns control ownership to business management, oversight to risk and compliance, and assurance to internal audit. A named daily reviewer must understand boundaries, interpret context, override or reverse outputs and stop the workflow safely. A human work standard maps consequential decisions to authorized roles, evidence and escalation paths.

    Michele Grant proposes a decision log, model-inventory record and autonomy scorecard. Deterministic approved calculations and original telemetry are the evidence, not the model's explanatory prose. Progression from shadow mode to exception-focused review remains conditional on sampled missed issues, error severity, overrides and measurable value; fewer overrides alone may reflect weaker scrutiny.

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    Michele Grant in a blue top beside the blue-and-white headline “TRUST NEEDS EVIDENCE” against a black background. Framed in blue with WWW.ARTIFICIAL-INTELLIGENCE.VIDEO, 13 October 2026 and duration 15m 4s.

    Michele Grant argues that enterprise agents earn trust through measurable business value, named accountability and auditable evidence for each increase in autonomy.