Unlocking Enterprise Rescheduling Self-Service Through Agentic AI
As part of Lowe’s Conversational AI team, I led the design and evolution of an enterprise delivery rescheduling capability - transforming a limited, rules-based experience into an agentic AI system grounded in business logic, API orchestration, and dynamic dialogue.
The goal was to enable scalable self-service for delivery changes where none previously existed - increasing containment, reducing associate workload, and improving customer control over post-purchase experiences.
Timeline
1 Month
Ongoing - Analyzation & Optimization
Project Type
IVA - Intelligent Voice Agent
Roles
AI Product Designer & Prompt Experience Designer & Data Analyst
Customers often need to reschedule their delivery due to changing availability. Currently, this process requires associate assistance even for simple, eligible rescheduling opportunities. This contributes to higher call volumes, longer handle times, and increased customer wait times.
The Problem
100K - 120K monthly calls about rescheduling a delivery
Customers could wait 2 to 30 minutes on hold, sometimes with calls going unanswered
Program Pause & GenAI Transformation
Due to an unrelated upstream large bug and changing portfolio initiatives, the project was put on hold.
Eventually when the project started back up, the focus of the team changed to creating generative agentic experiences.
Moving to GenAI
Translated structured flows → Instruction-based system
Designed guardrails & eligibility logic within prompts
Leveraged prior API + business insights
Built reusable orchestration patterns
Shift: Deterministic → Dynamic Conversation
Results
10% containment (Up from 0%)
Reduced associate handling for simple cases
Positive customer task completion
But… High transfer rate due to eligibility rules.
Deep Dive Analysis
I conducted post-release analysis:
Reviewed recorded sessions
Leveraged internal analysis agent
Identified top transfer reasons
Compared AI limitations vs. associate actions
Key Insight: Associates could override eligibility rules we could not.
Strategic Insight
Core issue wasn’t AI quality.
It was business logic constraints:
Majority of deliveries were marked as ineligible
Automation ceiling was structurally limited
Next step direction:
Expand eligibility logic
Integrate override pathways
Proactive messaging for customers to call back after missed delivery
