Our Client

Our client is a leading software and services company for travel, expense, and meetings & events, listed on the New York Stock Exchange and reporting US$2.7 billion in full-year 2025 revenue. They completed a transformative acquisition in September 2025with approximately US$155 million in identified annual run-rate synergies and full-year 2026 revenue guidance of US$3.2–3.3 billion (19–21% growth). The client  undertook a strategic consolidation of its IT operations to enable AI-driven cost transformation, share gains, and product innovation at scale.

Challenges

As a mature outsourcer with established multi-vendor managed services, the client carried sophisticated tooling and seasoned operating processes. However, the post-acquisition reality created a step-change in complexity. Two heritage IT estates needed to converge while business-critical traveler-facing services stayed live around the clock. Without intervention, the synergy case risked being eroded by integration friction:

•  Fragmented multi-tool landscape: AIOps, observability, monitoring, ITSM, and CMDB tooling spanned both estates with overlapping functions and inconsistent context — producing alert noise without correlated insight.

•  Manual triage at scale: The majority of alerts required human triage; mean time to acknowledge stretched to 45+ minutes during peak periods, with SLA breaches impacting traveler-facing services.

•  Siloed expert teams: L2/L3 engineers operated within technology towers, with limited cross-skilling and tribal knowledge trapped in individuals — a structural risk during integration.

•  Reactive, not predictive: Static runbooks and reactive firefighting were the default operating mode; pattern recognition across incidents was manual and inconsistent.

•  Labour-arbitrage limits: Traditional MSP models had reached their structural ceiling — incremental cost reduction required a different operating model, not more offshore headcount.

LTM Solution

Our client sought to modernize their run operations to address rising complexity driven by evolving technologies and fragmented operational models.  Thus, LTM responded with iRUN — a platform-led, AI-orchestrated managed services model purpose-built for mature outsourcers seeking the next operating-model jump. The engagement consolidated heritage tooling, unified the knowledge layer, and introduced a multi-agent autonomous operations mesh that augments human engineers without replacing the institutional muscle the client had built.

We created a multi-agent operations mesh, a Knowledge fabric that federates context across estates with zero data copy, and an L1/1.5 + AI-assisted L2 operating model. The model had human-in-the-loop guardrails in production changes, resulting in 20% operational efficiency and a platform for continuous application-support productivity gains.

The solution is anchored on three Business Creativity pillars:

• From scattered knowledge to shared intelligence: LTM’s knowledge fabric federates CMDB, incident history, runbooks, and topology across both heritage estates with zero data copy. Knowledge lives in the system, growing stronger with every incident, decision, and outcome, regardless of which heritage tool produced it.
• From reactive triggers to pre-emptive action: AI agents reason across systems, diagnose root causes, predict failures, and execute within governance and audit trails. Triage agents, observability agents, data-analysis agents and resolution agents operate as a cooperative mesh, with human-in-the-loop checkpoints on production change.
• From siloed operations to an integrated model: L1.5 full-stack engineers cross-skilled across application and infrastructure absorb 55–65% of incidents. AI-assisted L2 Pods focus on complex, judgment-heavy work. The legacy run change ratio inverts in favor of engineering and innovation.

Critically, iRUN unified the tooling estate without forcing rip-and-replace. Existing AIOps, observability, and ITSM investments were retained, with the knowledge fabric acting as the federation layer and agentic AI agents wrapping native capabilities. It protects the client’s prior investments and accelerates time-to-value. Cross-vendor AI integration ensured that ITSM Copilot, AIOps correlation, and orchestration platforms cooperated under a single agentic control plane.

The operating model includes a redesigned ITSM construct: incident triage in under 90 seconds, knowledge-graph-driven problem management, and topology-aware change management. Closed-loop learning from every incident outcome continuously sharpens the platform’s decision quality, turning each resolution into durable institutional intelligence.

Business Benefits

•  20% operational cost efficiency: Structural cost-out delivered through platform-led delivery, not labor arbitrage — underpinning the post-merger synergy case.

•  A platform for continuous application-support productivity: iRUN provides the agent inventory, knowledge graph, and operating constructs that drive sustained productivity gains across application support ,multi-year, not one-time.

•  Faster, more consistent resolution: AI-driven triage and contextual diagnosis materially compresses mean time to resolution across application and infrastructure incidents.

•  Estate convergence without disruption: Two heritage IT estates unified under one operating model, with tooling investments protected and traveler-facing services preserved throughout integration.

Conclusion

Mature outsourcers do not need more of the same. They need a different operating model. LTM introduced iRUN with Knowledge fabric, multi-agent autonomous operations, and AI-assisted delivery to simplify post-acquisition complexity at scale. The 20% efficiency gain is an outcome; the durable win is a platform that compounds productivity gains across application support. iRun is turning run operations into a source of strategic value rather than a cost to be managed. With LTM as Business Creativity partner, the client is positioned to scale, integrate, and innovate with confidence as the synergy case translates into measurable, year-over-year outcomes.