Owning Outcomes for Specialty Insurance with Run Ops: 25% MTTR Improvement and 20% Cloud Savings

Our Client

Our client is a leading global specialty insurer with broad capabilities and deep expertise in addressing complex specialty insurance needs, built on long-standing relationships with brokers and clients. With approximately US$9.3 billion in annual revenue from its insurance segment, they are recognized as a leading specialty insurer focused on underwriting niche, hard-to-place commercial and personal risks. The US and Bermuda operations embarked on a strategic journey to deliver resilient, scalable, and cost-efficient infrastructure through managed services across hybrid environments.

Challenges

The client faced rising pressure to deliver fast, consistent, and reliable services across business streams. Business growth and increasing digital dependency drove a steep rise in service volumes and complexity. It caused operational bottlenecks and an inconsistent user experience across multiple TechOps streams:

• CloudOps: No structured incident or change management process; Infrastructure-as-Code not integrated into pipelines; reactive alerting routed through the service desk; absence of defined SLAs, SLIs, and SOPs for incident response.

• SRE & Observability: Fragmented monitoring and alerting without business context; limited visibility (no transaction tracing, dashboards, or consistent non-prod monitoring); partial automation with no end-to-end orchestration; siloed teams driving conflicting priorities and fragmented reliability.

• DevSecOps: Pipeline templates lacking reusability and modular design; no automated change management integrated with deployment pipelines.

• IT Service Management: Lack of cohesive process management; gaps in operational hygiene; immature CMDB; weak process ownership and enforcement.

LTM Solution

To address these challenges, including fragmented operational models an layering of technology the client partnered with LTM to implement a unified application and infrastructure operations model centered on automation and AIOps called iRun. It had two anchoring goals:

1.  Optimizing technology operations through strategic centralization, automation, and effective adoption of AI/gen AI capabilities; and streamlining workflows, reducing manual effort

2.  Enhancing data-driven decision-making through standardized operating models that enable consistent, cost-effective delivery while supporting future growth.

Key capabilities of the solution include:

• Converged Operations: AI-enabled CognAItive Command Center for rapid resolution and “eye-on-the-glass” monitoring; alert correlation and suppression; resolution guidance and work-volume elimination using virtual engineers; automatic categorization, assignment, major-incident and problem linkage; performance tuning.

• DevSecOps: CI/CD automation up to production with minimum release failures; code quality and security checks; quick failure recovery; automated infrastructure provisioning; secure software delivery pipelines; proactive vulnerability management; regular upskilling and cross-skilling.

• SRE & Observability: Datadog and Azure-native services delivering 100% granular observability, a deeper, broader, and more holistic view of overall system health; predictive analytics, anomaly detection, real-time dashboards, entity-based monitoring, and CMDB visualization; automated remediation and self-healing for optimal availability and performance.

• ITSM and Application Support: Establish a Service Management Office (SMO) with defined incident, problem, change and release management roles aligned to ITIL; reusable workarounds, scaled KB usage; App-Infra synergy for uptime and faster resolution; measurable SLAs and KPIs supported by ServiceNow dashboards; self-service portal for recurring ad-hoc needs.

The technology spine includes ServiceNow and Datadog as core platforms, with modules across CloudOps, DevSecOps, SRE & Observability and IT Service Management. This is underpinned by agentic AI delivery (observability agents, triage agents, data-analysis agents and resolution agents) integrated into the operating fabric.

Business Benefits

The transformation is delivering measurable benefits while reshaping the operating foundation:

• Future-ready converged model: >20% productivity improvement over 3 years and >25% MTTR improvement, leveraging unified SRE, CloudOps and DevSecOps foundations focused on automation, AIOps, unified observability and predictable service quality.

• >20% cost reduction in cloud spend: FinOps foundations optimize cloud cost while reducing toil and shifting left through automation and AIOps.

• Predictive and proactive operations: Anomaly detection, real-time dashboards, and self-healing remediation prevent disruption before it materializes.

• Stronger application-infrastructure synergy: Improved uptime, faster access to resolutions, and consistent operational hygiene across the estate.

Conclusion

By optimizing technology operations through strategic centralization, increased automation, and effective adoption of AI and gen AI, the organization is moving from manual and fragmented service operations to a scalable, intelligent operating model . Thereby, they are accelerating resolution, improving decision quality, and building a true partnership grounded in customer-centric delivery while providing a strong foundation and roadmap for continued expansion of AI-led intelligent automation across the enterprise.