Somewhere in your enterprise right now, a monitoring tool is flagging a latency spike. A few minutes later, the service desk fields its first ticket from a user complaining about a slow app. Two teams are working the same incident from opposite ends, neither aware of the other.
To understand why, it helps to look at how IT service and operations evolved. IT service management (ITSM) has its roots in help desk software, while IT operations management (ITOM) grew from infrastructure monitoring. Different vendors, different buyers. Even when vendors offered both, the data models stayed separate because they didn’t share a configuration management database (CMDB).
Most enterprises still run on that legacy architecture. Monitoring on one side, ticketing on the other, and integrations passing events between them as foreign objects.
Why the gap is so hard to close
When tools optimize for different metrics, integration becomes messy because there’s no shared definition of success. For example, operations teams are measured on availability and mean time to resolution (MTTR). Meanwhile, the service desk is measured on service level agreements (SLAs) and customer satisfaction scores (CSAT).
The cost to IT shows up in four ways:
- Duplicated investigations: Two teams, same incident, with no visibility into the other’s investigation.
- Slow root cause analysis: The service desk doesn’t see the database latency or the recent change. They troubleshoot upward from the symptom.
- Change-driven outages: Changes get approved without complete dependency data. Yesterday’s “low-risk” change becomes today’s P1.
- Major incident delays: Everyone’s in the war room, but nobody’s looking at the same view.
What changes when signals come together
Unifying ITSM and ITOM on a single platform where AI is native, not bolted on, turns separate signals into a shared view teams can act on. AIOps detects early anomalies, correlates them across tools, and produces one actionable incident with full context attached, including the affected service, recent changes, dependency chain, and related open tickets.
When the pattern matches a known issue, AI can execute the remediation end-to-end, relying on human expertise only when escalation is needed. Every remediation feeds into a continuous learning loop, helping prevent the incident from happening in the future.
“The issue that would have produced hundreds of tickets next week never happens,” says Jason (Jay) Perry, product marketing manager for ITSM at ServiceNow.
Most organizations bring signals together first, then move toward a single data model. However, treating integration as the finish line, instead of a step, is where many organizations experience failure.
Fonterra shows what success looks like. As one of the world’s largest dairy operators, the New Zealand-based company produces 16 billion liters of milk annually, sold across 140 countries and accounting for 30% of global exports. Operating as a dairy cooperative with over 5,000 New Zealand farmers, Fonterra’s focus is to provide the best possible tools and services to farmer shareholders so they can focus on what they do best: producing world-class milk.
Prior to ServiceNow, Fonterra relied on disparate systems and tools for IT services, which prevented it from seeing an accurate view of its IT infrastructure and service performance across 40 manufacturing sites and 50 offices around the world. Fonterra needed a single technology platform to work seamlessly across the enterprise and minimize disruptions to manufacturing operations. It also prioritized maintaining accessible and easy-to-use services for over 21,500 employees and contingent workers to help boost productivity and deliver better services to customers.
“ServiceNow is the AI platform for business transformation,” says Scott Pyles, CTO at Fonterra. “We can take advantage of the built-in automation to connect the dots across the enterprise and make our processes more efficient.”
How to become a proactive IT organization
Here are three ways to move from reactive response to proactive operations:
- Get the CMDB right for one critical service before scaling. When correlation initiatives stall, the diagnosis is almost always bad or stale data. Skipping this is a common reason AIOps looks great in demos and falls apart in production.
- Pick one high-cost pattern of fragmentation, not all of them. Wire up event correlation against the service graph. Use the win to fund the next one.
- Give the work an owner. Service desk owns tickets. Operations owns alerts. Nobody owns the seam between them, which is why the seam stays broken.
This is where ServiceNow ITSM and ServiceNow ITOM help IT move from integration to true operational alignment.
From response to prevention
When monitoring signals and service desk tickets speak the same language — and AI helps connect the dots — the costs stop adding up. Instead of duplicating investigations, hunting for root causes, and recovering from avoidable outages, teams can spend more time preventing problems in the first place.
Automation takes care of the routine tasks, while the work that drives the business stays in the hands of people.
See how ITSM and ITOM can work together to catch and resolve incidents before they happen.