Workiva (WK) Status update summary

Scaling workflows and data foundations

• Automations that work for a small number of reports can become brittle as systems, fund structures, parameters and exceptions multiply; scalable design needs shared architecture and a documented reporting data mart.

• Standardized definitions, calculation narratives and reusable templates should support growth; clear ownership is needed to maintain integrations and enforce standards.

• Common warning signs include slow performance, extra process steps, undocumented workflows and key-person dependency.

• Trusted data requires authoritative sources, lineage, business context and codified definitions; even AUM can mean NAV, committed capital, invested capital or fee-paying AUM.

• Structured data supports deterministic outcomes in financial reporting and GRC; AI can also help interpret unstructured commentary.

Applying AI with controls

• AI accelerated delivery teams’ Wdata query work and GRC configuration using contextualized query and YAML libraries, helping produce initial outputs sooner for client review.

• Use deterministic automation for repeatable tasks such as running monthly queries; AI is more suited to building queries, combining data across sources and analysis.

• Limit AI context to data relevant to the business problem; test and reproduce recommendations before relying on them, and require human approval for execution affecting financial data.

• Maintain approval lineage and review of executed actions; a robust workflow combines AI interpretation and orchestration, deterministic calculations and rules, and human judgment and sign-off.

• Keep human review for material outputs, including executive presentations and regulatory filings, and use guardrails, audit trails and fallback paths.

Governance and operating model

• Encourage experimentation in sandboxes, then evaluate solutions for business value, enterprise fit and build-versus-buy; harden promising prototypes before production use.

• Route AI architecture and solution intake through IT, with business partnership and clear ownership for individual agents, updates and ongoing operations.

• Centralized skills libraries and rules can set boundaries; solutions that reach multiple teams or business-critical processes need stronger IT governance and support.

• Start with a high-value workflow and prove it in production; focus on the business problem, understand data context, experiment within defined bounds and keep codifying data knowledge.