Labor Intelligence Platform

I led a budgeting relaunch that replaced a three-day reporting lag with real-time labor cost guidance and increased adoption by 15%.

Role
Product designer
Team
1 designer · 3 PMs · 4 developers
Timeline
Apr to Dec 2025
Shipped
Real-time labor budgeting
Outcome
+15% adoption

Problems

Why are we making changes?

Clients built budgets in Excel or on paper. Every cycle meant re-entering data and reconciling disconnected sheets.

Manual & Time-Consuming

Problem Statement

How might we help operators

make staffing decisions with confidence,

with numbers they trust?

User Needs

What we heard from operators, onsite.

data points
6,000+
interviews
12
key findings
11

Finding severity across 11 synthesized findings

n=11 design-team findings. Severity reflects synthesis priority, not respondent frequency.

Critical
4
High
4
Medium
3
I print the schedule and write costs in pen.
Manager
The budget didn't update until the next morning.
Shift Lead
Schedule, budget, calculator. Every day.
Manager
We were $6K over. Nobody told me.
Director
I find out after the shift happened.
Shift Lead
Show me sales, labor, and schedule together.
GM
The overtime alert came six hours late.
Shift Lead
My new AM needed three weeks.
Director
Just show me if I'm staffed right.
Shift Lead

Existing State

Powerful but manual & disconnected from scheduling.

Labor budgeting user flow
The existing budgeting flow exposed the handoffs between forecasting, allocation, and scheduling.

Existed

  • Revenue-based budgeting
  • Hours-based budgeting
  • Hybrid model support
  • Org hierarchy sync
  • Historical revenue data
  • Historical labor data
  • Manual overrides

Missing

  • Real-time schedule sync
  • Inline scheduling guidance
  • Agentic recommendations
  • Integration with live systems

Competitor Research

How adjacent tools handle forecasting and scheduling.

Heuristic assessment of public product surfaces. Axes use a Low / Medium / High rubric based on how directly each tool supports an operator decision and how clearly it explains the recommendation.

Solution 1

Smart defaults that autofill the setup process.

Design Challenge 1

Simplifying Distribution.

Distribution pattern explorations

Picture an umbrella rental shop. You'd put 75% of your annual budget into summer. Then 75% of that into weekends. Then 60% into peak afternoon hours. Each decision flows into how many people you hire and when.

Previous iteration 1Previous iteration 2

Design Challenge 2

Manual before agentic.

Manual before agentic setup flow

The first 4 months were manual so setup was clean before we handed inputs and outputs to ML.

Solution 2

Decision guidance inside scheduling.

Design Challenge 1

Documenting current state.

Annotated scheduling grid documentation

The scheduling grid predated design systems department entirely and had no docs. We mapped patterns, states, and components.

Design Challenge 2

Building the system.

Building the systemLabor totals componentsScheduling grid documentation

We rebuilt the grid atomically from the ground up for budgeting, then helped engineering ship it to the global library for QA.

Building Together

Same room. Every sprint.

Devs, PMs, and design systems worked in the same room. We resolved logic with engineers, built components with systems, and iterated with the PM all in the same cycle.

Spec & Design

Takeaways

Collaboration drove the strongest decisions

Manual-first design made AI possible

Trust requires transparency at point of decision

Resilience under organizational change

Design systems as a force multiplier

Setup is a conversation, not a form