Agentic AI for B2B Order Management and Customer Service

Problem Statement

The organization experienced operational inefficiencies in Order Management (OM) due to strict truckload weight requirements.
Approximately 40% of orders were placed on hold for failing to meet the 3,000 lbs minimum weight.
Resolving these exceptions was a manual process taking a median of 6 hours per order.
The lack of standardized resolution protocols frequently led to order cancellations and revenue loss, while diverting OM agents from high-value service functions.

Orders being held

Resolution time per hold

A two-phase transformation strategy was adopted

Phase 1 (Process Optimization): Mapping account-level processes to identify "As-Is" process, account level nuances and aligning stakeholders on a standardized "To-Be" workflow.
Phase 2 (Implementation): Focused on data mapping, defining automation scale, and creating a roadmap to transition from Human-in-the-Loop (HITL) to fully autonomous Agentic AI.

Resistance to Standardization: Fears that customer experience for large customers would impact business.
Management: Demonstrated data-driven benefits to client and their customers, including a projected increase in On-Time In-Full (OTIF) rates from 78% to 94%.

Data Access Limitations: Restrictions on data that could be shared.
Management: The team prioritized foundational features while negotiating data provisioning with client in phases to allow design and build to proceed in parallel.
Trust & Adoption: Doubts about AI accuracy and consistency.
Management: Extensive Proof of Concepts (POCs) to demonstrate capability, starting with human supervision and review based solution (Human in the Loop) to build confidence.

Developed an Agentic AI based solution that would perform

Consolidation: The AI Agent would identify other pending orders from the same customer that could be combined.
Recommendation: The AI Agent would analyze customer’s order history, inventory, and promotions to suggest relevant add-on items.
Review: The AI Agent would recommend consolidation and addition options to the human Customer Service (CS) agent. The human agent would review the suggestion. Once approved, the Agent would communicate with the customer.
Execution: Once an agreement is reached between the Customer and the Human CS agent, the AI Agent would autonomously amend the order in the ERP, confirm the changes to the human agent, resolve the exception/hold and respond back to the customer about the changes.

Operational Efficiency Gain

(Human in the Loop)

Operational Efficiency Gain

(Autonomous Agentic AI)

Reduction in Order Cancellations