Case Study: How a Global Manufacturing Company Launched AI Across Finance in Six Weeks Using a Forward Deployed Finance Engineer

Industry: Manufacturing

• Employees: 4,800
• Annual Revenue: $1.2 Billion
• Region: North America
• Engagement: 12 Weeks

Services Delivered

• Forward Deployed Finance Engineer
• AI Strategy
• Data Engineering
• Financial Automation
• Executive Reporting

Background

A rapidly growing manufacturing company had invested heavily in modern finance technology over several years.

Its finance ecosystem included an ERP platform, business intelligence software, cloud data warehouse, budgeting solution, and dozens of operational reporting tools.

Despite these investments, the finance team still relied heavily on spreadsheets and manual reporting.

Monthly reporting consumed nearly two weeks. Management commentary required several days of manual work. Executives frequently requested analyses that finance teams struggled to deliver quickly.

At the same time, the executive leadership team wanted to begin adopting Artificial Intelligence but had no clear strategy for implementation.

The CFO summarized the situation simply: "We have all the technology, but none of it works together."

The Challenge

Several consulting firms had previously delivered transformation roadmaps. Internal IT teams had built numerous integrations. However, nobody owned the intersection between finance, engineering, and AI.

The organization needed someone capable of understanding finance processes while also building production-ready technical solutions.

Rather than launching another large consulting engagement, G7 Consulting Group proposed embedding a Forward Deployed Finance Engineer directly within the finance organization.

Week One

The FDE spent the first week meeting with FP&A, Accounting, Treasury, IT, and executive leadership.

Instead of documenting hundreds of requirements, the engineer focused on identifying high-value opportunities that could be implemented rapidly.

Five initiatives were selected:

• Automated executive commentary
• AI-powered variance explanations
• Consolidated financial dashboard
• Cash flow forecasting assistant
• ERP data automation

Building Instead of Planning

Unlike previous consulting engagements, implementation began immediately.

Within the first ten days:

• ERP data pipelines were optimized.
• Financial datasets were standardized.
• Business definitions were documented.
• A semantic financial model was created.

This semantic layer became the trusted foundation for every AI capability that followed.

AI Implementation

The Forward Deployed Finance Engineer then integrated enterprise AI services directly with the organization's financial data.

Instead of exposing raw data to users, AI responses were grounded in approved financial metrics and governance rules.

Executives could ask questions such as:

• "Why did gross margin decline this month?"
• "Summarize revenue performance by region."
• "Explain forecast changes compared to last quarter."

The responses included supporting calculations, source data, and business context.

Executive Reporting

The FDE redesigned executive reporting around automation.

Board packages that previously required multiple analysts over several days could now be generated in hours.

Narrative commentary was produced automatically and reviewed by finance leadership before distribution.

Interactive dashboards replaced static spreadsheets. Leadership gained access to live financial information rather than waiting for scheduled reporting cycles.

Knowledge Transfer

Throughout the engagement, finance team members worked directly with the FDE.

Rather than operating as an external consultant, the engineer paired with analysts, FP&A managers, and controllers while building solutions.

This collaborative model accelerated adoption and increased confidence across the department.

By the end of the project, internal teams could extend many of the solutions independently.

Results

Within six weeks, the organization had successfully deployed production AI capabilities across finance.

Within twelve weeks:

• Monthly reporting cycle reduced by 45%
• Executive commentary preparation reduced by 80%
• Financial data refresh became fully automated
• Dashboard adoption increased significantly across leadership
• Finance analysts recovered hundreds of hours previously spent on repetitive reporting tasks

Most importantly, the finance department shifted its focus from producing reports to interpreting business performance and supporting strategic decision making.

Why the Engagement Succeeded

The organization had previously invested in excellent software. What it lacked was someone capable of connecting technology with finance operations.

The embedded Forward Deployed Finance Engineer eliminated communication barriers between finance and IT, accelerated delivery through rapid iteration, and ensured every technical decision was tied directly to measurable business value.

Rather than receiving another strategic roadmap, the client received working AI solutions that became part of everyday finance operations.

Lessons Learned

The engagement demonstrated that successful AI transformation in finance is rarely limited by technology. More often, it is limited by execution.

Organizations do not necessarily need more software platforms or larger consulting teams. They need professionals who understand finance deeply enough to recognize high-impact opportunities and possess the engineering skills to build solutions quickly and responsibly.

The Forward Deployed Finance Engineer model provided exactly that balance, enabling the client to achieve measurable results in weeks rather than months while creating internal capability that continued to generate value long after the engagement ended.

Today, many leading finance organizations are moving away from large, document-heavy transformation programs toward smaller, execution-focused engagements. The FDE model represents this shift by embedding technical expertise directly into the business, delivering practical AI capabilities, and ensuring transformation becomes an ongoing capability rather than a one-time project.