Transforming FinOps – 50% Faster With GenAI
CLIENT
In a heavily regulated industry like energy and utilities, timely reporting is critical. One large energy and utilities provider operating across North and South America, with thousands of employees in multiple countries, found that preparing quarterly reports and compliance submissions was consuming enormous resources.
CHALLENGE
The finance team was under recurring pressure. Preparing reports meant pulling data from multiple ERP systems, regional subsidiaries, and external compliance sources. Consolidation required hours of manual reconciliation, often involving slightly different formats of spreadsheets.
The finance team spent more time chasing consistency than analyzing results. Missed updates created reputational risks with regulators and delayed decision-making for executives. Hiring more staff was not sustainable and conflicted with sensitive data handling rules.
The company approached REW Technology after several years of working together on ERP integrations. Having already delivered development and infrastructure projects, we were asked to extend the cooperation into AI. We already knew their systems and how data moved through them, and they wanted a solution that built on that foundation.
SOLUTION
Our first step was mapping the reporting workflow end to end: where the numbers originated, how they moved through systems, and where bottlenecks occurred. We compared options, from building a new reporting layer from scratch to automating workflows with existing ERP connectors. A standalone reporting platform would mean retraining staff and adding cost. A better approach was to insert an AI-driven layer into the tools they already trusted.
The system ingested raw ERP exports, subsidiary reports, and compliance updates, then used AI models to normalize formats, highlight inconsistencies, and draft initial reporting packs. Finance teams could verify outputs quickly, instead of rebuilding reports manually.
We kept one principle in mind: AI works best where processes are predictable, but people bring the context. The assistant could generate consolidated reports in minutes, but validation still rested with finance experts. In practice, AI could generate but only people could verify. That balance gave executives confidence the system was speeding up the process without replacing human judgment.
Technology stack:
Instead of presenting a long list of disconnected tools, here’s how the system fit together:
- Data flowed directly from SAP ERP and Oracle Financials into the reporting engine.
- Reports and compliance submissions stored in SharePoint were scanned automatically for version mismatches.
- A combination of Claude and ChatGPT v5 provided the reasoning power: Claude to process long financial tables and dense regulations, ChatGPT v5 to summarize and draft polished outputs.
- Azure Cognitive Search created a knowledge layer linking financial policies to reporting requirements, so outputs aligned with compliance expectations.
- Power Automate handled ingestion workflows and alerts.
- Access was secured through Azure Active Directory, giving only the finance leadership team visibility into draft reports.
RESULT
The impact was visible in the very first quarter. Time to assemble consolidated reports fell by nearly half. Finance teams cut reconciliation work from several days to less than 24 hours. Executives saw draft reports a full week earlier than before, which meant decisions were based on timely data rather than after-the-fact corrections.
One example: a regional subsidiary submitted a late adjustment just two days before a compliance filing was due. The system highlighted it immediately, flagged the specific items it changed, and updated the consolidated report without a manual rebuild.
Want to see how AI can simplify your FinOps?
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