Connected Data Emerges as Foundation for Trusted AI at Workiva's Amplify Event

Workiva is expanding beyond financial reporting into risk, compliance and sustainability, Case said. The opportunity depends on giving AI enough context to produce defensible results. The keynote was more technically oriented and included a product demonstration, she said. Workiva Chief Product Officer Deepak Bharadwaj addressed the event and was scheduled to appear on theCUBE later that afternoon.

Enterprises are assigning AI more consequential tasks, but fragmented records and undocumented human knowledge limit what those systems can do reliably, Case said. Generative AI produces probabilistic answers, while regulated operations demand precise and repeatable outcomes. A filing or compliance decision cannot simply appear or sound convincing. Organizations need to verify its source, understand how it was created and defend the result to auditors or regulators.

“How do we think about building that trust when the answer, the work product can't just look or sound right?” Case asked. “It needs to be very trusted. Part of that is making sure that it's very traceable and defensible.”

Achieving that standard requires more than improving the model, Case said. Information may be scattered across enterprise resource planning and customer relationship management systems, spreadsheets or employees' institutional knowledge. Connecting those sources gives AI a fuller picture of the business conditions surrounding each task.

“What I'm looking at as Workiva's bigger opportunity is creating that context and being that connective tissue across those disparate platforms and bringing in that more tribal knowledge to be able to make sure that as we are starting to ask AI to actually execute functions, that it can be trusted because it does increasingly have that context behind it,” she said. “They are historically very strong in things like compliance and reporting and very regulated and audited workflows that are very regulated. They are beginning to make some steps into areas like sustainability.”

Context also determines whether a business figure has practical meaning, Case said. A number drawn from a source system cannot guide action without information about what it represents, how it is changing and who owns it. Connected data turns an isolated figure into an accountable business record. CRM and ERP systems may hold different pieces of information, but users need to understand the context behind the numbers and who is responsible for them.

Trust becomes harder as AI agents move beyond analysis and begin completing multistep processes such as preparing regulatory filings, Case said. Companies must validate the source data, and they also need visibility into the agent's decisions. Industry rules, historical records and company-specific conditions all shape whether an action is appropriate.

“First, we need to make sure that we can trust the underlying data, the raw data that we're actually pulling from these systems,” she said. “We also need to make sure that we are trusting the decision-making capabilities of an AI agent … we need to make sure that if we are using AI, that it has that understanding of our industry.”

TheCUBE is a paid media partner for Workiva's Amplify event, and sponsors of theCUBE's event coverage do not have editorial control over content on theCUBE or SiliconANGLE, according to the disclosure accompanying the broadcast.

Editor's Summary

TheCUBE Research analyst Krista Case said at Workiva's Amplify event that connected data and business context are necessary for AI agents to perform regulated, auditable work reliably. Workiva is positioning its platform as a connective layer across ERP, CRM, spreadsheets and institutional knowledge as it expands beyond financial reporting into risk, compliance and sustainability. The discussion highlights that trust in AI depends on traceable data and visibility into agent decisions.