AI governance now has to account for systems that perform work rather than merely assist employees.

That shift is especially relevant for Workiva Inc., which operates in reporting, audit and compliance workflows where errors carry serious consequences. As companies introduce AI agents into finance and other regulated functions, plausible output is no longer enough. Businesses must be able to trace information, identify approvals and reconstruct the actions an agent took on their behalf, according to Krista Case (pictured, left), principal analyst at theCUBE Research.

“We need to understand things like where did this information and where did these insights come from, who approved a particular action, and can you maybe trace what happened if an AI agent is taking an action on your behalf,” Case said. “When you think about finance and these other regulated processes, it’s not really good enough for the action or the response to just look plausible. We have to really make sure that it can be substantiated.”

Case spoke with fellow host Alison Kosik (right) at Workiva’s Amplify event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how companies can let AI perform consequential work without losing trust, accountability or control. (* Disclosure below.)

AI governance starts with reliable data

AI can process information faster and across more workflows, but that reach also magnifies weaknesses in the underlying data. Fragmented systems, inconsistent definitions and unclear ownership existed long before generative AI. Agents make those problems more urgent because they can turn flawed information into decisions at greater speed and scale, Case explained.

“I think it’s commonly understood that our AI is only as good as the data that it’s built on,” she said. “What we talked about more specifically here at Workiva Amplify was the fact that if we have fragmented data stores, if we have inconsistent definitions and inconsistent ownership over data, these are not necessarily new problems that were created as a result of the enterprise adopting AI. They’re problems that existed before.”

The next challenge is determining when an agent can act independently and when a person must approve its work. Requiring human review for every action can erase much of the efficiency promised by automation. That leaves companies needing controls built around risk, with monitoring and exception handling becoming more important as AI use cases expand, Case noted.

“What’s interesting is that going back to the conversation around speed, if a human has to review and approve every action, then really the whole point or much of the value is moot,” she said. “We need to kind of make decisions around when is AI assisting a human, when can it execute on its own, when does it need approval? I do think that some of these boundaries are still being defined, and I think they’re going to evolve and change over time, especially as the business use cases evolve.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Workiva’s Amplify event:

(* Disclosure: TheCUBE is a paid media partner for Workiva’s Amplify event. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE