AI · Finance Case Study · NDA

Budget Dashboards & AI Assistant

Budget tracking dashboards and an AI assistant for Finance Business Partners and org leaders managing spend across Meta.

Company
Meta
Role
IC6 Product Designer
Scope
Strategy · Design 0→1 · AI
Team
Financial Planning
Year
2025–2026
Domain
Enterprise · Finance · AI
Budget Tool travel budget tracker interfaceIllustrative data

Illustrative data: every figure, name, and org shown in these screens is fabricated. See the full NDA note below.

0.2% → 63%
Budget coverage across 4 launches
8.6K–33K
Hours saved annually via AI
44%
Reduction in travel reporting effort

Finance Business Partners (FBPs) and org leaders managed multi-billion-dollar spend across OpEx, headcount, and travel without dedicated tooling, manually stitching together Concur exports, Workday ledgers, and FP&A data in spreadsheets. Budget coverage sat at just 0.2%.

The work came in two parts: build a unified real-time tracking foundation, then layer AI on top of it to answer the routine financial questions that were consuming hours of manual effort.

The goal

Give org leaders and their delegates a budget view they can act on themselves, accurate enough to trust and simple enough to use without a finance background, and make it work across every category of spend at Meta.

Finance Manager persona
Finance Manager
"I need to see the full financial picture across every pillar to catch risk early."
Org Leader persona
Org Leader
"I'm accountable for my budget, but I don't have time to become a finance expert."
Org Delegate persona
Org Delegate
"I manage day-to-day budget details while my leader needs summaries."

Budget accountability at Meta flowed hierarchically, moving from company-wide down to pillars, org leaders, and individual positions, trips, or purchase orders. I designed every surface to mirror that exact shape.

Whether monitoring the whole company or a single pillar, users operated at different altitudes within the same mental model. That consistency mattered beyond navigation: an AI assistant is only as trustworthy as the structured data underneath it.

Each tracker (Travel, OpEx, Headcount) operated as an independent pod with its own PM, Finance partner, 5 to 7 engineers, and shared UX research support. As the sole consistent design thread across all three, I drove cross-surface consistency in practice.

While another designer originated early concepts for Travel and Headcount, I independently designed OpEx from 0 to 1, establishing the component patterns and system foundation used to elevate Travel and Headcount in Phase 2.

Travel and Headcount had a Phase 1 before I took them on. It proved the demand and exposed four problems, and each one set the direction for what I built next.

Phase 1
What I changed
Phase 1

Leaders did not trust the numbers

Dense tables left executives questioning accuracy, with no story in the data.

What I changed

Digestible without losing detail

Visualizations for leaders who are not finance experts, and tables kept only where the detail was the point.

Phase 1

There was nowhere to go deeper

A rushed MVP used modal pop-ups, so there was no real detail behind any number.

What I changed

Dedicated detail pages

Real pages with consistent drill-down, so any figure traces back to its source.

Phase 1

There was no system to build on

One-off patterns meant every new spend category would need its own mental model.

What I changed

One paradigm, three trackers

Every data visual standardized, from variance framing through drill-down.

Phase 1

Answers took digging

Finding a specific number meant hunting through tables and filters.

What I changed

Ask the page

An assistant scoped to on-screen data, with every answer verifiable against the table beside it.

NDA note: Screens for this project are confidential under NDA. Everything shown here was rebuilt from memory to convey the design decisions. Every figure, name, org, and data point in these screens is fabricated and does not reflect real Meta budgets, headcount, financials, or organizational structure.

Travel Budget Tracker (Phase 2)

Delivered 8 design enhancements and 11 new capabilities focused on data visualization and resolving reporting discrepancies.

"This is looking really good... you guys should package it up and sell it to other companies."

VP of Finance, Meta

OpEx Budget Tracker (0–1)

Led the 0 to 1 design of a comprehensive budget owner dashboard, streamlining planning and increasing expense visibility by roughly 22 percent.

Headcount Budget Tracker (Phase 2)

Delivered Phase 2 enhancements covering approximately 40.4 percent of Meta's budget. To close a major data gap, I introduced headcount debt, tracking positions still open after a plan committed to closing them as a first-class metric alongside Filled and Unfilled roles.

Component Standardization

Partnered closely with design and engineering to unify the tool experience across all three surfaces into a single consistent shell and navigation pattern. The same interaction patterns carry across every tracker: one metric card that opens its own composition, one control for re-cutting a card by a different dimension, and one hierarchy that expands from the whole company down to a single manager.

AI Budget Assistant

Co-led strategy and design for an in-workflow AI assistant, which shipped while I was on the team. Because financial decisions carry high consequences, the interaction model prioritized trust and transparency through several principles:

  • Show Your Work: Responses broke down reasoning by org with the exact underlying numbers, and cited the system each figure came from.
  • Actionable Outputs: Answers surfaced direct next actions, such as opening an affected org or modeling a fix.
  • Scoped to the Page: The assistant answered only against the data in view, so any number it returned could be checked against the table beside it.

I concept-tested with all three personas. Three findings changed the design.

−44%
Reduction in travel reporting effort
3,000
Hours saved in ad-hoc reporting
8.6K–33K
Projected annual hours saved by the AI assistant
40.4%
Of Meta’s budget with headcount data gaps closed
+22%
Expense visibility for OpEx budget owners
53→75%
Trust in travel data, Phase 1 to Phase 2

Keep Doing

Building a component library for product-specific elements to hold consistency. Leveraging AI inside the solution so people can decide and act faster. Designing for trust in both the data and the AI responses.

Improve

Find ways to make this a more personalized experience per persona and per organization. Stress-test terminology with every organization earlier in the process. Incorporate AI workflows from day one rather than layering them on later.

Before leaving Meta, I was co-leading exploration into an AI-first budget transformation: proactive anomaly detection that would flag a risk like a bad accrual before anyone thought to ask, autonomous budget management, and personalized dashboards tailored to how individual leaders plan.

I designed this work in code rather than Figma, using Claude Code inside VS Code, and merged my own changes into the product. Working directly in the running system meant every exploration was real and testable the same day, with no gap between a mockup and something the team could actually put in front of a budget owner.

This work sits at the intersection of making complex systems feel simple and operating at the strategic layer where product direction is set. Building these budget trackers from scratch required deep engineering collaboration and close partnership with Finance. Layering high-stakes AI trust models on top of that foundation represents some of the most forward-looking work of my career.

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