Workiva Adds Generative AI to Drive Faster Decision Making With Data

Human Capital Reporting

Workiva introduced generative AI enhancements to its cloud platform. The company hopes the new features will help customers boost productivity and efficiency, as well provide insights for faster data-driven decision-making.

Founded in 2008, Workiva delivers a cloud platform centered on regulatory, financial and ESG reporting. The company’s goal is to “power transparent reporting” by connecting teams with data insights.

Workiva’s new generative AI features will streamline workflows by offering users the ability to author, edit and rewrite content across the company’s platforms. The additions will also allow employees to get help drafting documents, summarizing content and brainstorming and researching topics. That, the company said, should free up time for “greater value-add tasks.” In addition, users will have access to a “digital thought partner” and a “productivity enhancer” that can answer free-form questions.

The new capabilities are still in testing, though they’re available to some customers, Workiva said.

Tailored AI Solutions

Workiva claims the new generative AI features are different from other products because of their ability to provide content and answers that are more relevant to accounting, finance, risk, audit and ESG users.

In addition, the company said its open-ecosystem approach will let customers decide which large language model, including those from Google Cloud and Microsoft Azure, best fit their needs. Moreover, neither Workiva nor its technology partners will cache, store or use customer data to train models, Workiva said.

In May, Workiva and KPMG expanded their collaboration to help organizations facilitate ESG reporting. The companies said they’d incorporate Microsoft Cloud for Sustainability to enhance data management processes and accelerate regulatory analytics in order to “meet evolving ESG reporting requirements, address ESG reporting concerns including inconsistent data and siloed data sources, limited data validations and controls, lack of traceability, and highly manual and inefficient data processes.”


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