Outcreating enterprise support with AI-powered customer engagement for a global energy leader.

Our Client

Our client is a global energy enterprise operating across complex business, IT, customer service, and vendor ecosystems. Its support environment serves employees, customers, partners, and suppliers across multiple geographies. As support needs grew, service teams faced increasing volumes of routine requests and users needed faster access to information and issue resolution. The organization sought to create a scalable engagement model that could simplify support while improving experiences across its diverse user base.

Industry Trends

Energy and utility organizations are adopting intelligent virtual assistants, conversational automation and Generative AI for enterprise for enterprise support. Rising service expectations and operational complexity are pushing organizations beyond reactive, ticket-based models toward proactive digital engagement. AI-powered support can connect enterprise knowledge, automate workflows, and provide contextual assistance across business functions. Multilingual capabilities are also becoming increasingly important for organizations operating across global markets and diverse user communities.

Challenges

• High volume of manual support activities: Support teams spent significant time creating tickets, classifying issues, and handling routine requests. This increased operational effort and reduced capacity for more complex support needs.
• Slow resolution cycles: Users often struggled to find the right information, resulting in repeated requests, longer resolution times, and greater dependence on support personnel.
• Fragmented enterprise systems: Critical information was distributed across JD Edwards and other business applications. This made it difficult for support teams to provide unified and contextual responses.
• Diverse user communities: The organization needed a common support capability that could address different needs across employees, customers, and vendors.
• Global language requirements: Support needed to serve users across multiple geographies while maintaining a consistent experience regardless of language preference.

LTM Solution

LTM implemented a scalable conversational AI platform that created a unified intelligence layer across the client’s support ecosystem. The solution integrated JD Edwards and other enterprise applications, enabling users to access information, initiate transactions, and resolve issues through natural-language interactions.

Key capabilities included:

• AI-powered customer support: A conversational interface provided contextual assistance across customer service, business processes, and operational support scenarios.
• Intelligent incident management: The platform automated incident creation and routing, reducing manual effort associated with support requests.
• Guided issue resolution: AI used enterprise knowledge and standard operating procedures to guide users through troubleshooting and resolution.
• Automated error handling: The solution supported automated workflows for known errors, reducing the need for manual intervention and enabling faster resolution.
• Enterprise system integration: Integration with JD Edwards and other business applications enabled the assistant to provide relevant information and support business-process execution from a single interface.
• Multilingual engagement: The platform supported conversations across 60+ languages, extending consistent support experiences across the organization's global user base.

By bringing people, processes, knowledge, and enterprise systems together through intelligent conversations, LTM created a scalable support model that could evolve with the organization's needs.

Business Outcomes

• 25% improvement in Mean Time to Resolution: Guided troubleshooting and intelligent support automation helped users resolve issues faster.
• ~30% reduction in support tickets: Automated error handling, SOP-driven resolution, and self-healing capabilities eliminated approximately 30% of support tickets.
• Reduced manual support effort: Routine inquiries, incident creation, and issue-handling workflows were automated, allowing support teams to focus on higher-value activities.
• Faster access to enterprise information: Users could retrieve relevant business and operational information through natural-language interactions rather than navigating multiple applications.
• Improved user experience: Employees, customers, and vendors gained access to contextual and personalized support through a common conversational interface.
• Global accessibility: Support across 60+ languages improved accessibility and consistency for users across regions and business units.
• Streamlined business-process execution: Conversational workflows simplified interactions with complex enterprise applications and reduced time spent navigating fragmented systems.
• Scalable AI foundation: The platform established a reusable foundation for expanding AI-driven support and business-process capabilities across the enterprise.

Conclusion

LTM helped the client move beyond traditional helpdesk models toward a more intelligent way of engaging with employees, customers, and vendors. By connecting conversational AI with enterprise systems and business processes, the organization established a flexible support foundation that can evolve as its needs change. The platform provides a path to expand intelligent automation across more interactions, workflows, and business functions, supporting the client's broader journey toward an AI-enabled enterprise.