Visual Assist | Project Teaser

Glad I Can Go Home On Time

Strategic Project Highlights (An overview)

Problem Space

For every delivery stop, transporters spend valuable minutes in the cargo area searching for packages. When faced with extreme weather, this seemingly brief period can critically impact their safety and well-being; for example, summer temperatures in Arizona regularly hit 100°F by mid-morning, often soaring past 114°F (45.6°C) by noon.

At Amazon's vast scale, with 100,000+ transporters delivering to customers daily, this urgent challenge presents a powerful opportunity to apply design and technology.

What and How

During a cross-functional UX workshop to define a 10-year vision, a simple sketch of a projector in the cargo area emerged. This idea sparked a deep conversation about the package seek problem and opportunity, ultimately becoming Phase 1 of our 10-year vision roadmap.

Despite very limited human resources, the product team secured funding, enabling our collaboration with a Boston-based consultancy. Working from a Boston station, we rapidly iterated on design using a low-fidelity working prototype within the station. Within three months, we transitioned to prototyping in a Ford delivery van. While the initial pilot with a single transporter revealed many flaws that are so valuable, the team was impressed by the overwhelming user positivity towards a simple, life-changing solution.

Transitioning from the Ford van to the Rivian EDV allowed us to optimize for package/route capacity. This breakthrough enabled us to launch a small pilot with a few transporters running full route deliveries using the pre-Alpha prototype. The immediate, compelling metrics from this pilot convinced senior leadership to transition the project directly into a production program.

Indoor Lo-Fi experiment: Q1 2023
Soft launch production full route pilot 2025

Impact

We captured rewarding results from early full-route pilots. Despite a small sample size (N=25), we achieved an average 67% cognitive load reduction and 4.4/5 satisfaction score, a remarkable outcome for an alpha-stage deployment.

While the system still faced early reliability challenges, the response from transporters and the team alike was energizing. These results reaffirmed our belief that even small interventions, when grounded in real pain points can deliver outsized value and begin to transform daily work experiences for transporters at Amazon scale.

Key UX and Work performance indicators:

67% average cognitive load reduction (Mental: 62% vs. current system; Physical: 72% vs. current system)
4.4/5 user satisfaction (impressive for a first full-route pilot with alpha prototype)
30+ minutes average time savings per transporter per full route
Zero delivery rescue interventions for any pilot transporter

Transporter anecdotes:

“I can’t believe, I can finish at 6PM and go home to celebrate birthdate with my sister"

“Can we keep this in our vans for a longer time?

And?

This AI assisted innovation reminded me that transformational ideas often start from a sketch - and gain power when grounded in real-world pain points. What began as a casual idea in a workshop evolved into a deeply validated, human-centered solution—precisely because we stayed close to the problem, the user, and the conditions they face every day.

This wasn’t about inventing new technology for the sake of it. It was about understanding that even small moments—like searching for a package in 110°F heat—can compound into major stress, fatigue, and operational inefficiency at scale. And it was about choosing to act, even with limited resources, by co-creating with transporters and letting their feedback guide the evolution.

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