Case Study
Morningstar: AI Gave the Request Queue a Better Answer Than ‘Yes to Everything’
Structured AI intake helped teams score demand, route research content, and make roadmaps less reactive.
AI improves prioritization when the organization first agrees on the criteria for value, complexity, cost, completeness, and capacity.
Requests arrived through email, chat, and informal conversations, often without enough context, leading to overcommitment and unclear roadmaps.
A roadmap becomes fiction when every hallway request is urgent and every request receives a polite yes. Morningstar's retirement team standardized the front door, then used AI to find information gaps and summarize requests. Complex work could be scored consistently rather than winning by volume, title, or proximity to the coffee machine.
AI Studio named and summarized requests, identified missing information, scored complex work, routed approved items, and coordinated content-pipeline steps.
Asana reports the savings and time reductions; they should be understood as customer-story outcomes. The replicable design is transparent scoring with human approval, capacity checks, and a visible queue. AI can help evaluate the packet, but leadership still owns the uncomfortable sentence: this valuable idea is not the next idea.