Case Study
Pricing AI for supply chain improves conversion rates
Results
2 pts
Increased conversion rates by 2 points
6FTEs
Saved 6+ FTEs annually
88%
Reduced quote turnaround time by 88%
Company Profile
UK division of a global building-materials major, part of one of the world’s largest integrated manufacturers of cement, aggregates, and ready-mixed concrete. Operates a vertically integrated UK supply chain from quarried raw materials through downstream concrete delivery, serving infrastructure, industrial, and construction customers in a market where smaller, faster-quoting independents win on speed.
Challenge
Inconsistent, manual price quoting
Sales managers at the firm were stitching together every customer quote by hand: pulling pricing fragments from disparate spreadsheets, layering on their own market knowledge, and routing each contract through SAP for approval before answering customer inquiries that had come in by phone or email. The process was slow, and what one manager priced one way another priced differently the next day.
The inconsistency was costing the firm: leaving margin on the table on some contracts and missing sales entirely on others. Without visibility into its own pricing decisions, leadership had no levers to tighten the process.
Solution
Peak Pricing Agent
The team built an AI-driven pricing solution that took inquiries entered into SAP and pulled contract-relevant pricing fragments from across the firm’s data sources, then handed them to a Pricing AI engine in real time. The engine simulated conversion likelihood across thousands of candidate price points and surfaced the optimal price band for each contract. For time-sensitive materials such as aggregates, sales managers could flip the optimization from margin to volume, protecting revenue when the alternative was scrap.
To build trust, the engine showed sales managers comparable won and lost opportunities alongside each recommendation, so the AI-suggested price was anchored to history a sales manager could verify. Leadership rolled the workflow out in phases: half of inquiries routed through the new engine and half through the existing process, with the engine’s share scaling as confidence grew.
As a result, sales managers got back a defensible, consistent quote in roughly an hour instead of the eight-hour assemblage they had been doing by hand. The firm gained visibility into its own pricing logic, lifted conversion on more contracts, and freed sales managers from the spreadsheet-chasing portion of the day.
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