How AIOps Scaled Autonomous ITOps for a Global Manufacturer

A newly independent global discrete manufacturer in the resilient energy and industrial power solutions sector had to stand up enterprise-scale IT operations from the ground up, with a separation deadline that could not slip.

The work was not limited to a single team or a single platform. It spanned six pillars, including the service desk, SAP, data center, network, cloud, DevOps, and applications, and it had to function as one environment, not six separate islands. 

This was not a company choosing to modernize at a comfortable pace. It was a decision to build an operational backbone under contract-level pressure, while the business kept moving.

What Kind of IT Operations to Build?

With the deadline fixed and the baseline missing, the real question was what kind of IT operations to build. A traditional approach would have meant standing up processes, staffing, knowledge management, and ticket routing first, then layering AIOps on later when the dust settled.

The client chose a different path. They built with AI in mind from day one, because retrofitting later would have forced them to modernize twice. They needed a way to compress time, scale knowledge quickly, and reduce manual load before it became the default operating model. 

That is an important distinction for leaders thinking about enterprise AIOps. In many organizations, AIOps gets treated as an add-on. In this case, it was part of the foundation for how work would flow, how knowledge would be captured, and how resolution would happen across the environment.

Standing Up the Operating Model

The scope was not small. There were 11,200+ support tickets in scope, and 4,602 of those tickets sat in the service desk, representing 41% of total volume. That mix matters because it shows the balance of end-user needs and deeper operational work across systems. 

The early work had to solve two problems at once. 

Problem one

The organization needed a service desk and support model that could handle volume with consistency.

Problem two

The organization needed a way to build shared understanding quickly, because there was no inherited baseline for knowledge management, resolution processes, or support patterns.

This is where AIOps started to look less like a tool and more like an approach. The aim was not to “add AI” to tickets. Instead, it was to build an IT operations model where knowledge compounds, and where repetitive work moves toward automation over time.

How Capability Compounded Over Time

It helps to think about the rollout as an operational evolution rather than a sequence of feature deployments. The AI had to learn before it could reliably assist, and it had to assist before it could take on higher-frequency automation.

1. Knowledge Had to Become Central and Usable

The starting point was foundation work. Centralized knowledge management was stood up, and the AI began ingesting SOPs, tickets, and resolution patterns so it could learn how the environment behaved. Over that period, 1,010 knowledge articles were auto-created via AI, which is a meaningful signal that the system was not only consuming information but also structuring it in a usable way. 

Just as important, that foundation work starts to solve a knowledge infrastructure problem. A knowledge graph turns scattered facts into a connected map of what relates to what, so triage moves from “search and guess” to “connect and validate,” which is where speed and confidence come from.

This is an important lesson for enterprise IT operations teams as they start to see that service desk automation does not start with automation. It starts with knowledge that can be trusted, found, and reused across teams.

2. The System Shifted From Learning to Resolving 

As the knowledge base matured, the system moved into active work across multiple pillars. More than 60K tickets were analyzed, and the AI began resolving issues at scale in ways that reduced time-to-resolution across the environment. 

Two metrics make the shift tangible:

• 2,457 incidents were resolved via knowledge, which shows structured knowledge turning into operational outcomes.
• Incident enrichment reached 100%, meaning every incident was touched by AI, and that became the baseline rather than the exception.

That combination shows coverage and consistency. It also helps explain why MTTR reduction accelerated. When enrichment is complete and knowledge is consistently applied, resolution becomes less dependent on finding the right person at the right moment.

3. High-Frequency Work Is Moving Toward Automation

The current phase is active and expanding as of Q2 2026. The organization is now asking a different question than it asked at the beginning. The earlier question was whether they could build a functioning model under the separation deadline. The current question is what work can stop being manual. 

Here’s the data that captures that trajectory clearly:

• 47% of 53 SOPs are ready for automation now.
• 52% of 11,200+ tickets are addressable via AI assist, which creates a clear map of where autonomous IT operations can expand next.

This is how AIOps becomes a compounding system. It starts with knowledge, grows into consistent resolution, and then moves into repeatable automation where the volume and patterns justify it. 

What the Numbers Mean for the Organization

It is easy to list metrics in a block and call it results. It is more useful to connect the metrics to what changed in daily work. In IT operations, numbers matter most when they show a shift in how the organization runs, not only how fast it can close tickets.

Speed Became the New Baseline

The most immediate change shows up in MTTR reduction, particularly in associate experience.

• MTTR for Associate Experience dropped by 93%.

That’s a huge improvement that represents a structural shift in how quickly the organization can get people back to work.

The pattern held across pillars, which is what makes it meaningful for leaders evaluating whether AIOps results are repeatable.

• MTTR for the Service Desk dropped by 53%.
• MTTR for the Data Center dropped by 41%.
• MTTR for SAP dropped by 27%.

SAP is the most technically complex pillar in the scope, so even a 27% MTTR reduction matters. It signals that the model is not only helping with simpler issues, but also improving resolution discipline where the work is hardest.

Scale Improved Without Adding the Same Manual Load 

Scale is not only volume. It is the ability to handle volume without turning every spike into an escalation cycle.

The knowledge resolution rate reached 96.4%, which is a strong indicator that knowledge-based resolution became a reliable path, not a one-off success. Combined with 60K+ tickets analyzed and 1,010 knowledge articles auto-created via AI, the system became better at capturing, structuring, and reusing what the organization learns. 

That matters because IT operations teams rarely struggle due to lack of effort. They struggle because effort does not compound. This is what compounding begins to look like.

Experience Improved for Users and for the Service Desk

Service desk automation is often framed as an efficiency project, but the human outcomes still matter, especially in a newly built operating model where trust is being established.

CSAT improved by 28%, which shows that speed did not come at the expense of the user experience. 

On the service desk side, here’s how the work changed: 

• Voice handling without human routing reached 60%.
• AHT reduction for Knowledge Assist reached 33%.

Put simply, the service desk spent less time routing calls and less time handling repeatable questions, which creates capacity for more complex work.

The Trajectory Points Toward Autonomous IT Operations

By this stage of the rollout, whether AI can support the team is no longer in question. The focus is now on how much work can move from AI-assisted handling into workflow automation, without adding risk or inconsistency.

Ticket auto-resolution is at 7.5%, which shows automation is already happening in production and is still building.

Additionally, 52% of 11,200+ tickets are addressable via AI assist, which points to a large share of the workload where AI can consistently improve intake, enrichment, routing, and resolution support. 47% of 53 SOPs are ready for automation now, which shows that many repeatable tasks have already been identified and prepared for automation.

Taken together, those two figures describe a practical path toward autonomous IT operations that starts with high-frequency, well-understood work, then expands as processes and controls mature. 

The operational impact is also visible in direct savings. Password automation is saving approximately 2 FTE per month, which reduces repetitive load while the broader program continues driving MTTR reduction across the environment. 

The Work That Remains

The most interesting part of this story is how the organization’s questions changed as the model matured.

At the beginning, the question was whether a newly independent manufacturer could build IT operations fast enough to meet a separation deadline, across six pillars, with no inherited baseline.

Now, with Phase 3 active and expanding as of Q2 2026, the question is becoming more operational and more direct. What work can stop being manual, and what decisions can move from knowledge-based resolution toward context-aware automation?

That shift is where autonomous IT operations become real. This is a direction of travel that is already visible in the numbers, the workflow readiness, and the compounding logic behind the rollout.

For a closer look at how connected operational knowledge supports that shift, read our related blog on how knowledge graphs power agentic IT operations.

Is your organization looking for similar results? Contact our team today to learn how we can help you transform the way your teams work.