Unlocking Faster, More Confident Call Handling with an AI Summary Assistant
Problem
Call center representatives spent 2–3 minutes per call searching through dense, legacy work order pages to answer basic status and delay questions. With a target of 80 work orders per day, this friction directly impacted call efficiency and response quality.
my role
UX Designer — Owned research, AI summary definition, system prompt design, and the AI chat experience on the work order page. Partnered closely with Product and Engineering.
timeline
May 2025 – July 2025
project goals
Reduce time-to-context for work order understanding. Enable faster, accurate responses during live calls without replatforming legacy UI
constraints
Legacy codebase limited structural page changes. Work orders serve many roles (dispatch, service, invoicing, validation). Sensitive internal and vendor data could not be exposed externally.
primary outcome
A strategically scoped AI summary and chat experience launched internally and externally, improving access to critical work order context without redesigning the core page.
Problem
Call center representatives spent 2–3 minutes per call searching through dense, legacy work order pages to answer basic status and delay questions. With a target of 80 work orders per day, this friction directly impacted call efficiency and response quality.
my role
UX Designer — Owned research, AI summary definition, system prompt design, and the AI chat experience on the work order page. Partnered closely with Product and Engineering.
timeline
May 2025 – July 2025
project goals
Reduce time-to-context for work order understanding. Enable faster, accurate responses during live calls without replatforming legacy UI
constraints
Legacy codebase limited structural page changes. Work orders serve many roles (dispatch, service, invoicing, validation). Sensitive internal and vendor data could not be exposed externally.
primary outcome
A strategically scoped AI summary and chat experience launched internally and externally, improving access to critical work order context without redesigning the core page.
How I Approached This
Research & Design Process
How I Approached This
Research & Design Process
01
Identify the Highest-Leverage Bottleneck
Shadowed call center reps to understand where calls slowed down. Rather than auditing the entire page, I focused on moments where reps paused, searched, or cross-referenced information during live calls.
lens used
Cognitive load, workflow efficiency
intent
Pinpoint the specific friction that most directly impacted call duration and quality
KEY QUESTION
Where is time being lost during a live call, and why?
outcome
Confirmed that understanding work order history, delays, and related work was the primary bottleneck.
01
Identify the Highest-Leverage Bottleneck
Shadowed call center reps to understand where calls slowed down. Rather than auditing the entire page, I focused on moments where reps paused, searched, or cross-referenced information during live calls.
lens used
Cognitive load, workflow efficiency
intent
Pinpoint the specific friction that most directly impacted call duration and quality
KEY QUESTION
Where is time being lost during a live call, and why?
outcome
Confirmed that understanding work order history, delays, and related work was the primary bottleneck.
02
Choose Augmentation Over Replacement
Designed an AI layer instead of redesigning the legacy page.
02
Choose Augmentation Over Replacement
Designed an AI layer instead of redesigning the legacy page.
03
Define What “Useful” Means for AI Output
Designed the AI summary around decision-making, not completeness.
03
Define What “Useful” Means for AI Output
Designed the AI summary around decision-making, not completeness.
04
Pivot from Passive Summary to Active Call Support
Shifted from review-oriented summaries to question-driven interaction.
04
Pivot from Passive Summary to Active Call Support
Shifted from review-oriented summaries to question-driven interaction.
05
Expand Value Without Expanding Risk
Adapted the experience for external clients safely.
05
Expand Value Without Expanding Risk
Adapted the experience for external clients safely.
Key Decisions
Solution & Reasoning
Key Decisions
Solution & Reasoning
decision made
Solve for time-to-context with AI, not page cleanliness
decision made
Ship a Standardized Payroll Summary Report
decision made
Prioritize common questions over exhaustive summaries
decision made
Design Reporting Around Post–Pay Run Workflows
decision made
Add verification guidance for AI responses
decision made
Make All Costs Traceable to Totals
decision made
Enable shared understanding before scaling to clients
decision made
Make All Costs Traceable to Totals
My Results
Outcomes & Impacts
My Results
Outcomes & Impacts
Time-to-Context
Reduced
Reps no longer need to sift through logs and related work orders to understand status and history.
Time-to-Context
Reduced
Reps no longer need to sift through logs and related work orders to understand status and history.
Call Readiness
Immediate
Common caller questions are answered directly through AI during live calls.
Call Readiness
Immediate
Common caller questions are answered directly through AI during live calls.
Cognitive Load
Lowered
Work order history, delays, and related work are summarized into a single, interpretable view.
Cognitive Load
Lowered
Work order history, delays, and related work are summarized into a single, interpretable view.
Question Coverage
3/3
Highest-frequency caller questions are addressed by default.
Question Coverage
3/3
Highest-frequency caller questions are addressed by default.
Adoption Signal
Tracked
Engagement data and qualitative feedback confirm real-world usage and perceived value.
Adoption Signal
Tracked
Engagement data and qualitative feedback confirm real-world usage and perceived value.
UX Researcher & Designer
2026 Designed by Emma Blackwell. All rights reserved.
2026 Designed by Emma Blackwell. All rights reserved.