Enterprise AI governance shifts to workflows and controls
SiliconANGLE: Three insights you may have missed from theCUBE’s coverage of Workiva’s Amplify event
Enterprise AI governance is moving into the data, workflows and controls that determine what automated systems can do. As agents take on consequential work in finance, audit, compliance and sustainability, organizations need results that are grounded in business context, traceable to their sources and governed by clear accountability. TheCUBE’s preview of Workiva’s Amplify event highlighted […] The post Three insights you may have missed from theCUBE’s coverage of Workiva’s Amplify event a
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Enterprise AI governance is shifting focus from data quality alone to include the workflows and controls that determine what automated systems can actually do. As AI agents move from making recommendations to executing consequential work in finance, compliance, and other regulated areas, organizations must establish clear accountability through auditable records and traceable decisions. A central challenge exists between confidence in AI outputs and actual readiness, with executives expressing confidence in AI-assisted reporting while lacking sufficient data quality and process maturity. Success requires defining appropriate levels of agent autonomy for different business contexts, redesigning workflows alongside governance structures, and evaluating each process individually for automation readiness rather than applying uniform standards.
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