Carolyn Woodard talks with Joe Robbins, Chief Product Officer at Food Rescue US, about a real-world AI project built from the ground up: a predictive model that flags volunteer pickups at high risk of a last-minute cancellation.

Food Rescue US connects volunteers with food donors and receiving agencies through an app similar to a delivery service, coordinating about 150,000 pickups a year across 27 states. When a volunteer cancels within 24 hours of a pickup, site coordinators scramble to cover it or risk the relationship with the donor. 

Joe and his team partnered with a supply chain researcher at Michigan State University to build an algorithm that predicts cancellation risk and flags pickups at high-risk for cancellation. Rather than automating any decisions outright, the AI triggers an alert to the site coordinator, who checks with the volunteer about confirming the pickup or cancelling early. During the pilot and rollout the AI tool  flagged thousands of rescues that would have been last minute cancellations. 

Joe shares the practical lessons from a project that took a full year to scope, pilot, and improve: why good data capture has to come before any AI project, how a tightly scoped pilot funded through a grant reduced risk, and why keeping a human in the loop kept the tool trustworthy for staff and volunteers alike. 

He also offers a framework for nonprofits without an in-house AI researcher or technical staff to get started using AI for more than productivity tools.

Joe and Carolyn discuss:

• How a predictive model helps Food Rescue US flag volunteer pickups at high risk of last-minute cancellation, giving site coordinators three to eight days of notice instead of a same-day scramble.
• Why the project deliberately keeps a human in the loop: the algorithm surfaces a risk score, but a site coordinator decides whether to act on it.
• Why clean, well-understood data capture has to happen before any AI project, and how to audit what data your organization already has.
• How building a pilot into a grant proposal made it easier to test small before rolling out more broadly.
• A framework for nonprofits without an in-house AI researcher: start with free assistant tools, learn what context AI needs to be useful, then look for a tightly scoped, well-documented problem to solve where you already have the data to use.
• What's next for Food Rescue US: using AI to take logistical pressure off site coordinators so they can focus on building community relationships.

Resources Mentioned:

• Food Rescue US: https://foodrescue.us
• Tech Soup AI Impact Hour interview with Joe: https://engage.techsoup.org/c/upcoming-events/ai-impact-hour-for-nonprofits

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