With Spend Visibility, Prudential Financial Is Ready for AI
Prudential Financial knew large-scale change starts with clean, connected spend data. Quarterly Excel reports from a small team couldn't keep pace with the decisions the business needed to make. "Without measurable spend visibility, you get slower decision-making and unreliable outcomes," says Richard Porcaro, VP and Global Head of Sourcing & Category Management at Prudential.
The company partnered with SpendHQ in autumn 2024. The focus: unify, cleanse, and categorise years of global spend so teams could move faster with confidence.
From quarterly snapshots to a global view
Before the project, analysis was "relatively limited" and mainly covered "US-based spend on a quarterly basis," explains Rausey Mason, Data Analysis & Strategy Lead at SpendHQ. "It lacked the ability to look across time periods and certainly didn't have the global visibility they now have."
By Christmas 2024, SpendHQ had cleansed, classified, and loaded almost five years of data from the US and Japan. Soon after, the rest of the world was incorporated.
What changed-fast
- Nearly five years of global spend consolidated
- Almost US$40bn of spend normalised and categorised
- 600,000 "uncleaned vendors" converted to cleansed vendor records
- 99% of spend now categorised
- 150 active users "leveraging spend data to drive strategies and make decisions"
This is the kind of foundation that turns spend analytics from a rear-view report into a daily operating system for finance and procurement.
Why finance leaders care
- Sharper category strategies with real benchmarks by region, business unit, and supplier
- Faster budget cycles using fact-based forecasts and trend analysis across multiple years
- Improved working capital through payment term insights and supplier consolidation opportunities
- Better compliance by spotting contract leakage and off-contract spend
- Risk visibility across third parties with a single, cleansed vendor view
If you want a quick primer on spend analysis fundamentals, this overview is useful: What is spend analysis?
How to replicate Prudential's momentum
- Centralise sources: AP, P-Card, T&E, and PO data into one pipeline.
- Standardise taxonomy: Agree categories and subcategories that work globally.
- Clean the vendor file: Deduplicate, standardise names, and map parent-child relationships.
- Set a refresh cadence: Monthly (or better) to keep insights current.
- Enable the users: Give budget owners and category managers self-serve access and quick training.
- Measure adoption: Track login activity, saved views, and actions taken (e.g., sourcing events, renegotiations).
Ready for AI-because the data is ready
Clean, categorised, and complete data makes practical AI use cases viable. Think anomaly detection on invoices, early alerts on price variances, duplicate payment prevention, supplier clustering for consolidation, and more accurate run-rate forecasting. None of that sticks unless the underlying data is consistent and trusted.
Prudential's shift shows the sequence that works: fix the data, roll out self-serve analytics, then layer in AI to speed up decisions and reduce waste.
Next steps for your team
- Audit your current spend data sources and gaps.
- Define a company-wide taxonomy and vendor standards.
- Pick a platform with proven classification and vendor cleansing.
- Start with a 12-16 week sprint to deliver wins to budget owners.
Looking to level up your stack and skills? See curated options here: AI tools for finance.
Your membership also unlocks:
