AIOptimizer
Enterprise agentic AI for trade, MAP & pricing optimization
Overview
Leading the Enterprise AI Optimizer Project — AI agents with proprietary optimization to improve trade, MAP, and pricing decisions across all categories. Cross-functional delivery with Sales, Revenue Management, Category, and Finance.
Key Metrics
Case Study
The Problem
Trade, MAP, and pricing decisions across dozens of categories and customers were made manually, creating inconsistency and leaving optimization opportunities unrealized. No single system connected strategy to execution.
The Approach
Designed a multi-agent architecture with specialized agents for trade, MAP, and pricing domains — each with domain-specific reasoning and optimization logic
Integrated the optimization engine with real-time data from internal systems, ensuring agents always operate on current information
Built a cross-functional delivery framework that embedded domain experts from Sales, Revenue Management, Category, and Finance directly into the development loop
Iterated agent decision logic with frontline business users so outputs matched how teams actually think and work
The Impact
Covers all product categories enterprise-wide — the broadest scope of any analytics tool in the organization
Drives consistent, data-backed decisions across the full trade and pricing lifecycle
Enables Revenue Management and Category teams to run optimization scenarios in minutes instead of days
Created a reusable agentic AI framework that other teams can extend for new use cases
Lessons Learned
"Stakeholder adoption requires embedding domain knowledge into agent behavior — not just surfacing outputs and expecting users to interpret them"
"Multi-agent systems need clear handoff protocols and shared state to avoid conflicting recommendations across domains"
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