AI Recovery Agent: Turning Failed Deliveries into Recovered Revenue
The Problem
Failed deliveries are expensive. Each one costs the business a re-delivery attempt, consumes call center agent time, and risks a permanent churn. We had a high-volume call center team whose primary job was calling customers after failed delivery attempts — confirming new addresses, rescheduling, and re-routing.
The economics were brutal: a human agent call costs ~$2–4. With hundreds of failed deliveries per day, annual costs hit $72K+ just for this one workflow. And human agents had inconsistent quality, variable hours, and couldn't scale instantly during peak periods.
Delivery rate is a core unit economics metric in e-commerce. Every 1% improvement in delivery rate has outsized impact on gross margin. Automating recovery wasn't just cost savings — it was also faster recovery (AI responds in seconds, not hours) and better data capture.
My Role
Product Owner. I identified the opportunity by auditing call center cost data, scoped the AI solution, wrote the PRD, designed the conversation flows, and partnered with engineering to build and launch the agent.
Key Decisions
I chose fully autonomous (no human in the loop) because the tasks are rule-bound: confirm address, pick time slot, re-route. There's no ambiguity requiring human judgment. Human-in-the-loop would have captured 50% of the savings at best.
Rather than designing from scratch, I analyzed the top 10% of human agents' calls (by successful recovery rate) and modeled the AI's script on their patterns. This ensured the AI started at the performance ceiling, not the average.
Set low escalation triggers: if the customer shows frustration signals or the address update fails validation twice, escalate immediately. Recovery rate matters more than deflection rate.
Experimentation
Ran a 2-week shadow mode where AI made calls but human agents also called the same customers. Compared success rates. AI matched human agents on Day 1 and exceeded on Day 7 as we iterated on failure patterns.
Launch
Phased rollout: started with 20% of failed deliveries in one city, expanded to full deployment over 3 weeks after validating recovery rates matched or exceeded human agents.
What I Learned
“The insight that changed my AI product approach: for high-frequency, rule-bound workflows, AI doesn't just match humans — it eventually beats them because it doesn't have bad days, doesn't rush, and learns from every interaction systematically.”