Eliminated 30% of support tickets
through auto-healing, SOP guidance, and automated error handling.
Energy companies run on large, distributed workforces, deep vendor ecosystems, and business-critical ERP platforms that people must navigate every day. When help depends on ticket queues, manual triage, and single-language support desks, productivity and experience both suffer. A global integrated energy company faced this challenge across its employee, customer, and vendor interactions. LTM helped the company move from reactive, ticket-driven support to intelligent self-service by deploying an enterprise chatbot framework built on Microsoft Azure Bot Service, showing the real-world impact of conversational AI in energy and utilities.
New Ways of Working
LTM built a plug and play, cloud-powered AI assistant framework that gives employees, customers, and vendors instant, multilingual, self-service support inside the applications they already use.
We owned outcomes
Improved mean time to resolution (MTTR) by 25%
with automated incident creation and guided resolution.
Enabled real-time, contextual support
in more than 60 languages.
Our Client
The client is a global integrated energy company with operations spanning multiple geographies, a large and diverse workforce, and an extensive network of customers and vendors. Its core business processes run on Oracle JD Edwards alongside a range of third-party applications.
As the organization scaled, it sought to modernize how employees get help, how customers are served through its call center, and how vendors resolve day-to-day queries, without adding support headcount.
Industry Trends
Energy and utilities companies are rethinking the digital workplace, and conversational AI in energy is becoming central to boosting productivity and resilience. Leading organizations are increasingly:
Embedding enterprise chatbot capabilities directly into ERP and line-of-business applications
Shifting to AI-powered IT support that replaces ticket queues with self-service and self-healing models
Offering multilingual digital support to global workforces and partner ecosystems
Using conversational AI to modernize call center and vendor engagement
Building reusable, cloud-native AI frameworks that scale across business units
Challenges
The company faced several challenges that slowed support and limited the experience it could offer employees and partners.
No scalable AI foundation: The organization needed a scalable, cloud-powered enterprise chatbot framework that could be reused across business functions and applications rather than built one use case at a time.
Language barriers: A global user base required intelligent, multilingual support to deliver a consistent experience across regions.
Manual IT support and incident management: Support requests, incident logging, and routine fixes relied heavily on manual handling, driving up ticket volumes and resolution times and highlighting the need for AI-powered IT support.
Limited customer and vendor self-service: Customers and vendors depended on human agents for routine how-to queries, creating an avoidable load on call center and support teams.
Together, these challenges increased support costs, slowed issue resolution, and diverted employee time away from higher-value work.
LTM's Solution
LTM implemented an enterprise chatbot as a pluggable, cloud-powered AI assistant, using natural language processing (NLP) and machine learning (ML) to hold intelligent conversations across user groups.
Enterprise AI assistant on Microsoft Azure: Bots built on Microsoft Azure Bot Service were invoked directly within the JD Edwards application, meeting users where they work.
Employee self-help: AI-based conversations gave internal users on-demand business process know-how and IT self-help resolutions.
Customer and vendor engagement: Conversational AI supported customers in call center operations and helped vendors resolve how-to queries without agent intervention.
Enterprise integrations: LTM developed integrations with JD Edwards and other third-party applications so the assistant could deliver real-time, contextual responses.
Intelligent advisors: Advisory bots guided users through processes to create a differentiated customer experience.
Multilingual bots: The assistant supported more than 60 languages, providing consistent service across the company's global footprint.
Support automation and auto-healing: Bots handled automated incident creation, standard operating procedure (SOP) execution, error handling, guided resolution, and auto-healing of known issues, delivering AI-powered IT support at scale.
Business Benefits
The solution demonstrated the value of conversational AI in energy operations, delivering measurable improvements in support efficiency and user experience.
Eliminated 30% of tickets as AI-powered IT support proactively resolved issues through auto-healing, SOP automation, and error handling.
Improved MTTR by 25% through automated incident creation and guided resolution.
Streamlined business processes and improved decision-making with real-time, contextual responses from integrated systems.
Delivered consistent multilingual support in more than 60 languages for employees, customers, and vendors.
Established a reusable, cloud-powered AI framework that can be extended to new applications and use cases.
Conclusion
By embedding Azure-powered conversational AI in energy workflows, starting with JD Edwards and adjacent systems, LTM helped the energy company transform support from a manual, ticket-driven function into an always-on, self-healing experience. Employees now spend less time waiting for help and more time on meaningful work, while customers and vendors gain faster, multilingual answers. The scalable framework positions the organization to extend intelligent assistance across the enterprise.
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