Our Client

Our client is a North America-based multi-line insurer offering auto, home, life, farm, and commercial insurance, with customers across 19 US states and a Gross Underwritten Premium of approximately US$4.9 billion. The organization is consistently ranked on the Fortune 1000 list for revenue and recognized for its strong financial performance over multiple decades. Built on more than a century of insurance services, the organization embarked on a strategic journey to modernize internal service operations and scale efficiently as digital dependency deepened across distribution, underwriting, and claims workflows.

Challenges

The client faced increasing pressure to deliver fast, consistent, and reliable services across all business streams. Business growth, rising digital dependency, and the migration from data center operations to cloud have significantly increased service volumes and complexity. ServiceNow had been established as the strategic enterprise platform for service delivery, but the platform itself had accumulated rigidity through years of customization. This made it difficult to rapidly adopt modern AI capabilities. Service operations had become a shared dependency across IT support, business operations, and the command center, while delays in incident resolution, underwriting workbench responsiveness, and customer service directly impacted policyholder experience and broker relationships.

Specific operational pain points spanned environment, application, infrastructure, and security:

•    Environment & application issues: Reliability, health, and tech debt; data, integration, API, job-failure, and outage incidents; automation failures were rampant.

•    Infrastructure: 60% data-centre resource utilization issues were there; ~90% of service requests comprised of batch job re-runs; high provisioning time and low automation; rising EOSL (End of support life) tech-debt licensing exposure.

•    Security: Lack of integrated tooling and unified visibility; manual processes for network security, access control, and delays in vulnerability management.

•    Operating model: Absence of a Gen-AI-enabled cross-functional operating model; high manual effort across IT, business operations, and service desk; legacy customizations limited AI activation.

LTM Solution

The client recognized the need to transform run operations end-to-end as layers of technologies created operational inefficiencies and fragmentation.

iRUN is a disruptive AI platform-led single delivery operating model built around business outcomes. Through business creativity, it combines the very best of human expertise with contextual AI that reasons across systems, acts before disruption, and keeps humans in the loop for advanced decisions.

LTM partnered with the client to implement a unified iRUN delivery built on ServiceNow Now Assist out-of-the-box skills and Agentic AI workflows, truly integrating application and infrastructure operations end-to-end. The engagement is anchored by five constructs that frame how iRUN delivers:

•    AI-Native Delivery Model: An enterprise knowledge fabric provides augmented intelligence at every step, from support workflows to SDLC processes, so institutional knowledge stays in the system.

•    FAIR Engagement Construct: Contractually Flexible, with clear Accountability, Integrated governance, and Reliability built into the operating model.

•    Customer Delight: Self-service tools, persona-driven services, and productivity-led design improve experience for employees, agents, and policyholders.

•    Accelerated Future State: Niche expertise, proven accelerators, and a curated partner ecosystem reduce time-to-value across the modernization roadmap.

•    Business Innovation: Pre-built solutions, InsureTech connectors, hyperscaler innovation, and LTM-funded investments are infused into delivery.

Key capabilities of the solution include:

•    AI-powered IT Service Management: AI-generated incident summaries, risk insights and resolution guidance; automatic categorization, assignment, major-incident and problem linkage; AI-driven change implementation, backout and test planning.

•    Smarter customer service operations: Intelligent triage using Retrieval-Augmented Generation for automated interaction-type and subtype assignment, reducing manual effort and improving routing accuracy.

•    Enterprise-wide Agentic Automation: Automated record retrieval, updates, linking, and comment updates; AI-generated resolution summaries; web research agents creating troubleshooting steps for complex issues.

•    Agentic AI principles applied: Strong contextual grounding for accurate LLM responses, RAG with historical data to minimize hallucinations, modular reusable agents for scalability, and phased migration of NLU topics to LLM-driven topics for incremental accuracy gains.

The technology spine spans ServiceNow, Salesforce, Guidewire, Siebel, and Azure as core platforms, with ITSM, Workplace Service Delivery, and Customer Service Management modules, and AI/automation delivered through ServiceNow IT Ops Management (ITOM) and the agentic AI ecosystem.

Business Benefits

•    30% net cost reduction across IT operations: Structural cost-out delivered through unified delivery, automation, and AI, not labor arbitrage.

•    25%+ faster resolution: AI-driven triage, classification, and guided resolution across IT and customer service.

•    30–40% reduction in manual effort: Change planning, post-incident reviews, HR cases, and customer-case triage shifted from hours to minutes.

•    30–35% improved accuracy and service consistency: Automated categorization, routing, and AI-assisted change planning strengthen SLA adherence and governance.

•    25% improvement in end-user experience: Consistent experience for employees, agents, and administrative teams through AI-driven triaging, faster resolutions, and clearer communication.

•    30% infrastructure cost reduction: AI-led FinOps right-sizes capacity and removes batch-rerun toil through proactive remediation.

•    25% improvement in application scalability and performance: Without incremental run-rate cost, through observability, Site Reliability Engineering (SRE), and AI-led optimization.

Conclusion

By pragmatically embedding Now Assist and Agentic AI within existing ServiceNow workflows via iRun, our client moved beyond AI experimentation to production-grade adoption across enterprise service operations. Through effective change management, previously manual and fragmented service operations were transformed into a scalable, intelligent operating model. As a result, accelerating resolution, improving decision quality, and elevating employee and customer experiences, while providing a strong foundation and roadmap for continued expansion of AI-led intelligent automation across the enterprise.