Case Study: Building an AI-Powered Finance Function for a Global Retail Company Using Forward Deployed Finance Engineering

Engagement Snapshot

• Industry: Retail & E-Commerce
• Employees: 6,500
• Annual Revenue: $2.8 Billion
• Region: Europe
• Engagement: 20 Weeks

Services Delivered

• Forward Deployed Finance Engineer
• AI Strategy
• Finance Data Platform
• Executive Analytics
• Financial Process Automation

Background

A multinational retail company operated hundreds of physical stores alongside a rapidly growing e-commerce business.

Every day, millions of financial transactions flowed through ERP systems, inventory platforms, CRM applications, and payment providers.

Although enormous amounts of data were available, finance teams struggled to convert that information into actionable insights.

Preparing weekly executive reports required multiple analysts working across different countries.

Forecast updates were often delayed because collecting operational inputs consumed so much time.

Senior leadership wanted to introduce AI into finance but was concerned about data quality, governance, and reliability.

The Challenge

The organization faced several obstacles.

• Financial data originated from more than twenty operational systems.
• Different business units calculated KPIs differently.
• Inventory reporting varied by geography.
• Forecasting processes differed across regions.
• Executives frequently waited days for answers to relatively simple financial questions.

The organization had experimented with public AI tools but quickly realized generic AI models lacked the business context necessary for financial decision making.

G7's Engagement

Rather than launching a large consulting program, G7 embedded a Forward Deployed Finance Engineer within the finance transformation office.

The engineer worked directly with:

• FP&A
• Accounting
• Treasury
• Data Engineering
• IT Architecture
• Executive Leadership

Daily collaboration allowed rapid decision making and continuous delivery.

Establishing Trustworthy Data

The engagement began with one objective: create financial data executives could trust.

Working alongside finance leadership, the FDE designed standardized financial models covering:

• Revenue
• Gross Margin
• Inventory
• Cash Flow
• Working Capital
• Operating Expenses
• Forecast Accuracy

Every calculation became centrally governed. Every dashboard referenced identical business definitions.

AI Built on Financial Context

Once the semantic layer was complete, AI capabilities were introduced gradually. The first release focused on executive reporting.

Finance leaders could request:

• Executive summaries
• Regional performance reviews
• Inventory analysis
• Forecast explanations
• Margin commentary
• Cash flow narratives

The AI generated draft analyses grounded entirely in approved company data. Every statement included supporting metrics.

Finance professionals remained responsible for final approval, preserving governance while dramatically accelerating reporting.

Automating Operational Finance

The Forward Deployed Finance Engineer then focused on repetitive operational work. Several manual processes were redesigned.

• Budget templates synchronized automatically.
• Store performance reports refreshed every morning.
• Variance reports generated without analyst intervention.
• Regional controllers received automated alerts when unusual financial patterns appeared.

Instead of searching for issues, finance teams could immediately investigate exceptions.

Enabling Self-Service Analytics

Historically, executives relied on finance analysts for every custom request. The new architecture enabled self-service access through natural language.

Executives could simply ask questions such as:

• Which regions missed forecast?
• How did inventory turnover change?
• What explains margin improvement?
• Which product categories generated the strongest growth?

Answers were available within seconds.

Organizational Impact

The project changed more than reporting. Finance became significantly more proactive.

Business leaders began requesting scenario analysis more frequently because generating new reports no longer required days of manual work.

Controllers shifted their focus toward strategic recommendations instead of report preparation. FP&A teams invested more time improving forecasting models and less time collecting data.

Results

Within twenty weeks, the organization achieved:

• 60% reduction in manual reporting activities
• 50% faster executive reporting
• AI-generated financial commentary across every business unit
• Daily automated performance dashboards
• Consistent KPI definitions globally
• Increased adoption of self-service analytics among executive leadership

Why the Engagement Was Successful

The company already possessed modern technology. Its challenge was execution.

The Forward Deployed Finance Engineer combined finance knowledge, software engineering, AI implementation, and enterprise architecture into a single role. This eliminated the traditional handoffs that often delay transformation programs.

Every solution was designed, built, tested, and refined with finance users throughout the engagement.

Looking Ahead

Following the success of the initial engagement, the organization expanded the model by embedding additional Forward Deployed Finance Engineers into Treasury and Supply Chain Finance.

With a trusted financial data foundation now in place, the company plans to introduce advanced forecasting models, autonomous financial agents, and AI-driven planning workflows over the coming years.

The engagement demonstrated that successful AI adoption is not primarily about choosing the right model or platform. It is about combining finance expertise with engineering execution to deliver solutions that finance teams trust and use every day.