CASE STUDY TWO
Reducing Repair Delays by Designing for the 94%
Role: Product Designer, UX Researcher
Tools: Figma, Heap Analytics
Tools: Figma, Heap Analytics
The Problem
As adoption of Fleetio's Maintenance Shop Network grew, a new bottleneck emerged: repair approvals.
Once a vehicle arrived at a shop and a repair estimate was submitted, work often stalled while fleet managers reviewed and approved the repair order. Drivers waited. Shops waited. Vehicles sat idle. At first glance, the approval workflow appeared to be functioning as intended. But a closer look revealed a different story.
94% of repair orders were approved exactly as submitted. Despite this, every repair order required users to complete a separate approval workflow. We had created a process optimized for scrutiny when most users just needed a fast way to say "yes."
Lean Research Results
Through workflow analysis and customer feedback, we identified a mismatch between user behavior and product design. Fleet managers weren't looking for more control. They were looking for less effort.
The existing experience treated every repair order like a high-stakes procurement decision, even though the overwhelming majority required no changes at all. The opportunity wasn't to improve approvals. It was to remove unnecessary approval work.
The Approach
Rather than redesigning the entire workflow, we focused on reducing friction at the point of decision. We brought repair approvals directly into Fleetio and introduced bulk approval actions that allowed fleet managers to approve routine work without leaving their existing workflow.
The goal was simple: spend less time reviewing routine repairs and more time investigating exceptions.
The Impact
The response was immediate. Within 48 hours of launch, fleet managers clicked the Approve All action more than 1,000 times.
While the metric itself was encouraging, the larger signal was behavioral. Users discovered the feature organically, understood it immediately, and incorporated it into their daily workflow without training or intervention.
When friction disappears from a high-frequency task, adoption doesn't need an explanation. Thousands of repair line items that previously required repetitive review were reduced to a single action, helping vehicles get serviced faster and reducing delays across the maintenance lifecycle.
Looking Forward
Most repair orders are routine. The challenge is identifying the few that aren't. We're exploring how historical pricing data and LLM-powered analysis can surface repair orders that appear unusually expensive, inconsistent, or potentially risky. Rather than reviewing everything, fleet managers can focus their attention where it matters most. The future of the workflow isn't faster approvals. It's fewer decisions.
Most repair orders are routine. The challenge is identifying the few that aren't. We're exploring how historical pricing data and LLM-powered analysis can surface repair orders that appear unusually expensive, inconsistent, or potentially risky. Rather than reviewing everything, fleet managers can focus their attention where it matters most. The future of the workflow isn't faster approvals. It's fewer decisions.
CASE STUDY TWO