The situation
A multi-entity enterprise CFO manages financial operations across four business units, three ERP instances, two procurement platforms, and a growing portfolio of SaaS applications that accumulated over years of decentralized technology purchasing. Monthly close requires days of manual reconciliation across systems that speak different entity languages. A customer in the CRM is an account in the ERP and a contract party in the AP system, and none of them share a common identifier. The close is always late. The board numbers are always qualified with caveats about timing and system alignment.
Working capital management is reactive by design — AR aging reports are pulled weekly, inventory positions are reviewed monthly, and payment terms are negotiated by individual procurement managers without visibility into the portfolio effect across the enterprise. The CFO’s team knows working capital is improvable. They do not have a connected, trusted picture of where the improvement lives or how large it is.
The expense environment has grown in complexity as AI tools, SaaS subscriptions, and automated payment systems accumulated without centralized oversight. Three separate teams manage procurement, AP, and contracts, each with a partial view. Duplicate vendor relationships exist across the three ERP instances. Automated payment tools deployed by individual business units are processing transactions without central finance visibility. The CFO’s team suspected waste and misalignment in the expense base. They lacked the data foundation to find it, quantify it, and act on it with confidence.
The solution
The PolyPhaze Knowledge Fabric™ connected the three ERP instances, two procurement platforms, the CRM, and the AP system. Entity resolution reconciled the customer, vendor, and contract identities across all connected systems into canonical Trust-Scored records. Every financial signal carries a PolyPhaze Trust Score™ and full lineage to its source.
The Optimize capability deployed seven domain agents against the governed financial data foundation. Expense anomaly detection surfaced unused SaaS licenses — 247 seats at $143,000 annual cost, confirmed across the SSO log, the HR system, and the contract vault in a three-system cross-confirmation. Duplicate vendor payment detection found 14 vendor relationships that existed under different names or identifiers across the three ERP instances. Rate violation scanning surfaced 12 purchase categories where invoiced rates exceeded contracted maximums. A legacy contract renewing at premium rate was flagged 90 days before auto-renewal.
Working capital optimization ran against the same trusted data foundation. AR aging intelligence identified the accounts with the highest recovery probability weighted by likelihood of collection, giving the collections team a prioritized call list rather than a sorted spreadsheet. Inventory positioning analysis surfaced carrying cost by product category and recommended rebalancing across locations. Payment terms analysis identified the suppliers whose terms were improvable given their negotiation history and the enterprise’s current cash position.
AI Decision Trace established a governed baseline of the finance automation environment. The Coverage Discovery capability identified three automated payment tools operating outside the sanctioned finance workflow, processing transactions at velocities and patterns consistent with algorithmic, not human, authorization. All three were reviewed. No material adverse positions were found, but the governance gap was closed and a new oversight protocol established.
The savings pipeline organized findings by implementation timeline: $2.1 million in quick wins actionable within 30 days and $2.6 million in strategic opportunities over 30 to 90 days. Every dollar carries a Trust Score. Every finding has a suggested owner. The CFO did not present a cost reduction recommendation to the board. She presented a validated, auditable pipeline with a Trust Score on every number.