Case Study: How a PE-Backed SaaS Company Reduced Its Monthly Close by 40% with a Forward Deployed Finance Engineer

Engagement Snapshot

• Industry: B2B SaaS
• Employees: 850
• Annual Revenue: $180 Million ARR
• Region: North America
• Engagement: 16 Weeks

Services Delivered

• Forward Deployed Finance Engineer
• Financial Systems Integration
• AI for Finance
• Executive Reporting
• Process Automation

Background

Following a recent private equity investment, a fast-growing SaaS company faced increasing pressure to improve financial visibility and operational efficiency.

The company had experienced several acquisitions in a short period, leaving finance with multiple disconnected systems, inconsistent reporting structures, and duplicate business processes.

Although the organization had invested in NetSuite, Datarails, Power BI, Salesforce, and Snowflake, much of the monthly reporting process still relied on manual spreadsheet consolidation.

Every reporting cycle required dozens of exports, manual reconciliations, and repetitive validation work before management could review financial performance.

As the company continued to grow, these manual processes became increasingly difficult to sustain.

The CFO recognized that the organization did not need another strategic consulting engagement. Instead, it needed someone capable of working directly with finance to build practical solutions.

The Challenge

Several issues were slowing the finance organization.

• Revenue reporting differed across business units.
• Salesforce and NetSuite data were not consistently aligned.
• Forecast assumptions varied between departments.
• Management reporting required nearly five business days after month-end close.
• Executives often questioned whether reports reflected the latest available information.

Perhaps most importantly, AI initiatives had stalled because no trusted financial data model existed.

The G7 Approach

G7 embedded a Forward Deployed Finance Engineer directly inside the FP&A organization.

Rather than beginning with documentation workshops, the engineer joined weekly planning meetings, month-end close activities, executive reporting sessions, and forecasting reviews.

This allowed the engineer to understand how finance actually operated instead of relying solely on requirements documents.

Phase One: Creating a Single Financial Foundation

The first objective was establishing consistency. Working alongside FP&A managers and finance systems administrators, the FDE standardized:

• Revenue hierarchies
• Department mappings
• Product classifications
• Customer segmentation
• Financial KPIs
• Management reporting definitions

A centralized semantic layer was created within Snowflake, allowing every dashboard, report, and AI assistant to reference identical business definitions.

For the first time, executives, finance, and operations were working from a single source of truth.

Phase Two: Automating Reporting

The engineer then redesigned the executive reporting process.

Instead of manually assembling PowerPoint presentations from multiple spreadsheets, automated reporting pipelines generated standardized board-ready outputs directly from the financial data platform.

Power BI dashboards updated automatically after each data refresh. Management commentary templates were connected to AI-generated draft narratives based on approved financial metrics.

Finance leaders could now focus on validating insights rather than assembling reports.

Phase Three: AI Enablement

With trusted financial data available, the organization introduced its first production AI capabilities.

The Forward Deployed Finance Engineer implemented internal finance assistants capable of answering questions such as:

• Why did operating expenses increase?
• Which customer segments exceeded forecast?
• What changed in recurring revenue this month?
• Which departments contributed most to margin improvements?

Rather than generating generic responses, every answer referenced approved financial metrics and supporting calculations.

Executives quickly adopted the new tools because they trusted the accuracy of the underlying information.

Cross-Functional Collaboration

The engagement extended beyond finance.

• Sales Operations began using shared metrics.
• Customer Success gained visibility into revenue performance.
• Executive leadership received consistent KPI reporting across departments.

Instead of creating another isolated finance system, the FDE ensured financial intelligence became available across the organization while maintaining governance and security.

Results

After sixteen weeks, measurable improvements included:

• 40% reduction in monthly close reporting effort
• 70% reduction in manual spreadsheet consolidation
• 85% reduction in report preparation time for executive meetings
• Standardized KPI definitions across every department
• First production AI finance assistant deployed company-wide
• Significant increase in executive confidence in financial reporting

Why It Worked

The success of the engagement did not come from implementing new software. The company already owned excellent technology.

The breakthrough came from embedding someone capable of connecting finance operations, engineering, and AI. Instead of handing work between consultants, IT, and finance, one individual owned the complete solution from business problem to production deployment.

Lessons Learned

Finance transformation succeeds when execution keeps pace with strategy.

By embedding a Forward Deployed Finance Engineer directly into the organization, the company accelerated delivery, reduced operational complexity, and created a foundation capable of supporting future AI initiatives without additional large-scale transformation programs.