Executive Operations & P&L Command Center

Case Study — Confidential Work (Anonymized)

The problem

One of the world's largest mobility platforms operates across multiple strategic product lines in Brazil: large cities, new city launches, category expansion, and a portfolio of proof-of-concept markets. Each project had its own resource allocation, performance targets, and P&L — but headquarters needed one unified view of national performance, one reliable forecast, and one answer to the question: are we on track to hit our EBIT and market-share targets this year?

The Brazil mobility team had once been five people. I inherited all of their responsibilities and ran them solo — with AI agents as my analytical support team.

What I owned

• National P&L consolidation — merging four strategic project lines into one coherent financial picture, covering gross revenue, net revenue, incentives burn, contribution margin, and full operating expenses.
• YTG (Year-To-Go) forecasting — weekly modeling of actual performance to predict monthly and year-end results, adjusting the delivery plan as market conditions shifted.
• OKR management for all mobility teams — tracking market share and EBIT targets cascaded from headquarters, ensuring every team's contribution aligned with the national commitment.
• Resource allocation governance — setting budgets and targets for resource managers across each project, monitoring deployment efficiency, and escalating variances to directors.
• Director and VP reporting — weekly communication of results, forecast revisions, and target adjustments to project directors, the Senior Director of Mobility, and the Strategy & Planning / FP&A teams.
• Data infrastructure for AI adoption — structuring operational data to support analytical automation and AI-driven decision support.

The operating scale

Key metrics I tracked daily

The decision that mattered most

Mid-year, year-to-date results showed we were outperforming our initial plan. The question from leadership: should we hold the original target, or commit to more?

I ran a scenario analysis — alone, using SQL, Python, and our internal BI tool — to model the market-share impact of increasing our EBIT delivery. The analysis showed we could reach our 2028 EBIT target two years early, delivering a significant incremental margin improvement for headquarters while simultaneously expanding market share through new-city launches.

The Senior Director and VP accepted the recommendation. The revised commitment was communicated to headquarters as an accelerated timeline — a result that required re-forecasting every project's P&L, re-allocating resources, and coordinating four project directors to align on stretch targets.

Tools and methods

• SQL for querying operational and financial data warehouses
• Python for scenario modeling, forecasting, and data validation
• Google Sheets / Excel for collaborative P&L consolidation and director-facing reporting
• Internal BI platform (similar to Tableau / Power BI) for marketplace and financial dashboards
• AI agents for data analysis, modeling support, and documentation automation

What changed because of this work

• Headquarters received a revised national commitment — 2 years ahead of the original timeline.
• Four project directors operated from a unified forecast, not four separate spreadsheets.
• Weekly target adjustments became systematic rather than reactive.
• A solo analyst with AI support delivered what previously required a full team — demonstrating that analytical operations can scale with automation, not just headcount.

Confidentiality note

All figures are approximate, indexed, or expressed as directional change. Exact trip counts, EBIT percentages, and internal targets are omitted. Platform scale metrics are order-of-magnitude only. This case study is based on publicly permissible descriptions of the role and responsibilities.