Outcreating AI-Driven Enterprise Service Operations: 20–25% Faster Resolution with ServiceNow X to Now

Our Client

The client is a global manufacturer specializing in HVAC air distribution, ventilation, and air movement products for residential and commercial buildings. With approximately $1.7 billion in annual revenue and operations spanning more than 25 locations worldwide, the organization delivers a diverse portfolio of air management and filtration solutions. Built on more than a century of engineering expertise, the company continues to expand its global footprint while modernizing internal operations to support digital platforms and scalable enterprise services.

Industry Trends

Manufacturing organizations are increasingly under pressure to deliver fast, consistent, and reliable service experiences as business growth, geographic expansion, and rising digital dependency drive higher service volumes and operational complexity. Internal service teams, from IT support and customer service to procurement and workplace operations, must support a growing ecosystem of users and systems that depend on responsive and reliable service delivery to keep operations running smoothly.

At the same time, many organizations continue to operate with service workflows shaped by years of platform customizations and manual processes, limiting their ability to scale service operations efficiently. As digital platforms become central to business operations, delays in incident resolution or change management can directly impact the ability of teams across the enterprise to perform their roles effectively. For the client, ServiceNow was already established as the strategic platform to unify service delivery across global operations. However, increasing service demand and evolving operational needs created a clear opportunity to modernize service workflows and embed AI-driven capabilities to improve efficiency, decision-making, and overall service experience. 

Key Challenges

• Absence of Gen AI-enabled cross functional operating model
  Despite growing industry adoption of generative AI, the organization lacked a unified framework to integrate AI capabilities into core IT service workflows.
• Manual, effort-intensive service operations
  Critical activities such as incident triage, change planning, and incident documentation required significant manual effort, slowing response times.
• Legacy platform customizations limiting AI Adoption
  Extensive ServiceNow customizations created rigidity, making it difficult to rapidly adopt new AI capabilities and out-of-the-box platform features.
• Limited decision support across service workflows
  Processes such as incident classification, change risk evaluation, and incident reviews lacked consistent and intelligent decision support.
• Rising service volumes outpacing team capacity
  Increasing demand across IT service functions placed pressure on support teams to maintain responsiveness and service quality.

LTM’s Solution

LTM partnered with the client to embed ServiceNow X to Now capabilities directly into enterprise service workflows, enabling AI-driven automation, contextual insights, and intelligent decision support across IT service operations. The approach focused on embedding AI capabilities directly into everyday ITSM workflows to improve decision-making, automate routine tasks, and strengthen operational consistency.

Key elements of the solution included:

1. AI-powered incident management workflows
   X to Now capabilities were deployed to generate incident summaries, recommend resolution steps, and draft response communications, helping agents quickly understand context and resolve issues faster.
2. Automated incident resolution documentation
   Generative AI automatically produced resolution summaries and response emails based on incident context, reducing manual documentation effort.
3. AI-driven knowledge creation
   X to Now analyzed closed incidents and generated draft knowledge base articles from resolution data, enabling agents to review and publish knowledge quickly while strengthening the organization’s knowledge repository.
4. Intelligent change management support
   AI-generated change request summaries and automated risk explanations provided decision support for change planning and governance.
5. Conversational AI assistance within the ServiceNow workspace
   The X to Now panel enabled agents to interact with incident records conversationally, quickly retrieving contextual information without navigating multiple screens.
6. AI-enabled collaboration summarization
   Sidebar discussion summarization captured key insights from agent and stakeholder conversations, improving communication clarity and documentation.
7. Sentiment-aware incident analysis
   AI-driven sentiment analysis evaluated the tone of incident communications, helping service teams prioritize urgent issues and improve responsiveness.

Through these capabilities, LTM established an intelligent service delivery framework that reduced manual effort while maintaining governance, accuracy, and human oversight.

Business benefits

The AI-enabled transformation delivered measurable improvements across service efficiency, productivity, and user experience.

• 20-25% faster service resolution: AI-driven triage, contextual summaries, and guided resolution recommendations accelerated incident handling across service operations.
• 30-40% reduction in manual operational effort: Automation of triage, change planning documentation, and knowledge creation significantly reduced administrative workloads.
• Up to 30% increase in agent productivity: AI-generated insights and contextual assistance enabled service teams to handle higher volumes without increasing headcount.
• Up to 25% improvement in service experience: AI-enabled chat assistance and faster response cycles improved the experience for both service agents and end users.

Conclusion

By embedding ServiceNow X to Now capabilities within existing service workflows, the organization successfully moved from fragmented manual operations to an intelligent, AI-enabled service model. Automated triage, AI-driven knowledge generation, and intelligent decision support improved operational efficiency while strengthening service governance and responsiveness. The transformation established a scalable foundation for expanding AI-driven automation and intelligent service delivery across the enterprise.