At U.S. Hunger, What Clicked Was a Shared Space Rather Than a Data Pull
In AWS's telling, Rick saw a chance to explore U.S. Hunger's data directly — following questions as they arose instead of asking the technology team for a fresh data pull each time; the nonprofit had first encountered Quick through an AWS Social Entrepreneur Accelerator event.
Direct exploration was the starting point, but the shift came with Spaces, which give colleagues a shared place to work with documents, definitions and analyses relevant to a particular part of the business.
The payoff is agreement on meaning: shared definitions of key performance indicators help colleagues settle what a given measure actually represents.
AI-generated discussion · Different perspectives
Seventeen colleagues got access — executive leadership, the technology team, the Full Cart program, finance, compliance and support functions. That is a small club, and the interesting choice is who is in it: the people who argue about measures and money, not everyone who touches a spreadsheet.
Chat is still the top executive's primary day-to-day AI workspace, even though Spaces is the capability he uses most. Shared context does not replace the prompt box; it is what keeps the prompt box from giving a different answer every time.
The concrete example: inside a Space holding Salesforce call reports, Rick is drafting instructions for the technology team — which measures to include, where the data comes from, what each measure means — and he can comment on the generated draft and regenerate it with changes folded in. The instructions stay next to the source reports instead of being ferried through another tool.