AI Agents in Finance Demand Auditability, Not Just Plausibility

The nature of AI governance is shifting as organizations deploy systems capable of performing autonomous work rather than simply supporting human decision-making. This distinction carries particular weight for Workiva Inc., a company focused on reporting, audit and compliance—domains where mistakes have material consequences. When AI agents operate in finance and other regulated sectors, mere plausibility of output falls short of what businesses require. Instead, organizations need the ability to track data sources, document approvals and reconstruct the complete sequence of steps an agent executed, according to Krista Case, 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. 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.

Krista Case, principal analyst at theCUBE Research

Case shared her observations during a conversation with host Alison Kosik at Workiva's Amplify event, broadcast exclusively on theCUBE, the livestreaming platform operated by SiliconANGLE Media. The discussion centered on how organizations can enable AI to handle high-stakes work while preserving trust, accountability and human oversight.

Data quality becomes the foundation

While AI systems can process information more rapidly and across broader operational domains, expanded deployment also exposes underlying data weaknesses. Problems like disconnected data repositories, varying definitions and ambiguous data stewardship predate generative AI but become more pressing when agents can convert poor information into actions at accelerated speed and volume, Case explained.

I think it's commonly understood that our AI is only as good as the data that it's built on. 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.

Krista Case

The subsequent challenge involves establishing boundaries for autonomous agent action versus scenarios requiring human sign-off. Mandating human approval for each decision can eliminate much of the efficiency gains that automation promises. This reality forces organizations to construct controls centered on risk assessment, with monitoring and exception management becoming critical as AI deployments broaden in scope, Case noted.

https://www.youtube.com/embed/8Kafp1RFE4Q?feature=oembed

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. 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.

Krista Case