Workiva positions connected data as the trust layer for enterprise AI

Artificial intelligence systems need access to connected business data to handle consequential work reliably. Many enterprises are expanding AI's role into higher-stakes functions, yet the reality of disconnected records and undocumented expertise creates obstacles to trustworthy outcomes. Workiva Inc., an enterprise software provider, is building its platform to act as a bridge between disparate business systems while broadening its reach from financial reporting into risk, compliance and sustainability domains. The foundation for this strategy rests on equipping AI with sufficient business context to generate results that can withstand scrutiny, according to Krista Case, principle analyst at theCUBE Research.

This morning's keynote was a little bit more technically oriented, and we saw a demo of the product. We heard from Workiva's Chief Product Officer, Deepak Bharadwaj, who is going to be on theCUBE this afternoon. When we think about trust, I think he really talked through some of what that means in terms of the Workiva product and what customers really should be looking for.

Krista Case, principle analyst at theCUBE Research

Case shared her analysis with theCUBE host Alison Kosik during Workiva's Amplify event, broadcast exclusively through theCUBE, SiliconANGLE Media's livestreaming platform. Their conversation centered on why unified data and business context matter for creating AI agents that stakeholders can trust.

Connected data puts business information in context

Generative AI systems produce probabilistic outputs, but compliance-heavy operations demand precise, repeatable results. A regulatory filing or compliance determination cannot rely on mere plausibility. Organizations must trace its origins, document how it was derived and present a defensible explanation to auditors and regulators, Case explained.

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

Krista Case

Meeting this requirement demands more than refining the underlying model. Enterprise data often lives in separate locations—enterprise resource planning systems, customer relationship management platforms, spreadsheets and the accumulated expertise of individual employees. Integrating these sources provides AI with a more complete understanding of the business circumstances surrounding each task, Case noted.

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. 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.

Krista Case

Context and trust shape accountable AI decisions

Business context also determines whether a metric carries meaningful value. A number extracted from a source system cannot inform decisions without understanding what it measures, how it is trending and who bears responsibility for it, Case stressed. Unified data transforms an isolated metric into a business record that can be acted upon with confidence.

Like we were talking about earlier, there might be a CRM and ERP system that has different pieces of information, but we need to understand the context behind it. We need to go beyond just the number. We need to understand its context and who's responsible for it at the end of the day.

Krista Case

Establishing trust becomes more complex when AI agents shift from analysis to executing multistep workflows like regulatory filing preparation. Organizations must verify the quality of source data, but they also require transparency into how the agent reached its conclusions. Industry standards, prior records and organization-specific factors all influence whether an action is justified, Case observed.

https://www.youtube.com/embed/XS24OU9KdGo?feature=oembed

First, we need to make sure that we can trust the underlying data, the raw data that we're actually pulling from these systems. 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.

Krista Case