Outcreating Order and Claims Efficiency with Gen AI-Led Automation

About Client

The client is a global leader in consumer food and snacking products, operating across multiple markets and categories.

It manages large-scale order and claims volumes across a complex partner ecosystem. As the business expanded, it required scalable, technology-driven processes to support growth, improve responsiveness, and enhance customer experience.

Need for Change

As the business scaled, order and claim volumes increased significantly, placing pressure on existing processes and teams.

The organization relied heavily on manual workflows and analyst-driven operations. This limited scalability, slowed response times, and increased operational costs.

At the same time, evolving customer expectations demanded faster, more transparent, and self-service-driven experiences. The existing model could not support these expectations or future growth.

To address this, the organization sought to modernize its order and claim creation processes with a scalable, intelligent, and user-centric platform.

Key Challenges

• Order and claim analysts faced heavy dependency on manual creation processes, slowing turnaround time and limiting scalability as volumes increased.
• Operations teams struggled with an OCR solution that required large training datasets, making it difficult to handle new document formats and scenarios.
• Claims processing teams lacked OCR support for claims, increasing manual effort due to complex validations and lookups against existing orders.
• Customers, partners, and consumers had no self-service access, relying on analysts for submissions and status updates, leading to delays and repeated communication.
• Customer support teams managed frequent status queries through emails and calls, increasing operational overhead and reducing efficiency.
• Business and operations leaders faced rising manpower dependency during peak demand periods, increasing costs and affecting service consistency.
• IT and transformation teams were constrained by vendor-dependent legacy tools, limiting flexibility and slowing adoption of newer technologies.

LTM’s Solution

LTM implemented a unified order and claim creation platform using ServiceNow Manufacturing Commercial Operations (MCO), integrated with SAP as the system of record.

A self-service portal was designed for customers, partners, and consumers, enabling direct order and claim submission, status tracking, and support requests. This reduced dependency on analysts and improved turnaround time.

Gen AI-powered Now Assist DocIntel (NADI) replaced the legacy OCR tool, enabling intelligent document processing for both orders and claims. Its prompt-based capabilities eliminated the need for large training datasets and improved adaptability to new scenarios.

Dedicated workflows were configured for order types and claim scenarios, including overage, shortage, damage, and returns. A unified workspace enabled analysts to manage orders, claims, and cases from a single interface.

Seamless integration with SAP ensured continuity of downstream processing without disruption. Real-time notifications and dashboards improved visibility across stakeholders.

Cross-functional collaboration across business, IT, and operations teams ensured alignment with process requirements and scalability goals.

Phase-Based Implementation Approach

• Phase 1: Immediate priorities

Focused on critical order and claim workflows to stabilize operations and deliver immediate value

• Phase 2: Core enhancements

Expanded automation capabilities and improved process coverage across scenarios

• Phase 3: Future optimization

Introduced advanced features to enhance experience, scalability, and continuous improvement

Key Capabilities Delivered

1. Gen AI-powered OCR for order and claim creation
2. External self-service portal for customers, partners, and consumers
3. End-to-end workflows for orders and multiple claim scenarios
4. Unified analyst workspace for operations management
5. Real-time notifications and status tracking

Business Benefits

• Enabled a self-service-driven operating model, reducing reliance on analysts and improving scalability.
• Accelerated order and claim processing through GenAI-powered OCR and workflow automation.
• Improved customer experience with real-time tracking, notifications, and reduced response times.
• Reduced operational costs by minimizing manual effort and dependency on temporary workforce.
• Increased process visibility through centralized dashboards and unified workflows.
• Shifted from vendor-dependent tooling to a flexible, technology-driven ecosystem.

Conclusion

The transformation established a scalable, self-service-driven model for order and claim processing, improving efficiency, visibility, and customer experience.

By embedding Gen AI into core workflows, the organization is now better equipped to handle growing volumes with reduced manual effort.

Ongoing efforts focus on expanding automation, enhancing AI-driven insights, and extending self-service capabilities. These initiatives position the organization to continuously improve responsiveness, optimize operations, and scale with evolving business demands.