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5 AI Use Cases That Significantly Reduce the Burden on IT Operations

From the service desk to AIOps: AI can ease the burden on IT organizations if use cases are clearly prioritized and integrated into existing processes in a controlled manner.

Increasing complexity, rising ticket volumes, high availability requirements, and a noticeable shortage of skilled workers: The pressure on IT operations is mounting, while expectations for speed and quality continue to rise. This is precisely where AI comes into focus. Many IT organizations are exploring how they can effectively leverage generative AI, AI agents, automation, and AIOps.

The key question, therefore, is no longer whether AI is relevant to IT operations. Rather, the crucial question is: Where does AI provide concrete benefits without creating additional complexity or uncontrolled risks?

This is exactly where it’s worth taking a structured look at suitable use cases. After all, not every AI idea automatically translates into a good use case. The key factors are clear benefits, available data, seamless process integration, governance, and realistic implementation. Those who are just beginning to identify use cases will find a suitable methodological starting point in the atrete article on implementing AI through the right use cases.

Blog

Implementing AI through the right use cases

by Yannik Hauser

This article builds on that and highlights five specific AI use cases that are particularly relevant for IT leaders.

Why AI Needs to Be a Priority in IT Operations

In the IT world, AI is often associated with big promises: less manual work, faster responses, better decisions, more stable operations, and lower costs. These goals are realistic, provided that AI is not viewed in isolation as a tool, but rather as part of an operational model.

In IT operations, it’s rarely just about implementing a chatbot or a new monitoring feature. It’s about how data, processes, automation, and human responsibility interact. Especially when it comes to critical IT processes, it must be clear which tasks AI is allowed to support, where decisions must be approved, and how results are verified.

A pragmatic approach therefore starts with a limited MVP: small enough to gain experience quickly, but structured enough to be scalable later on. This concept also aligns with the design thinking approach in IT automation: understand the problem, focus on the solution, test early, and improve iteratively. Read more about this in the article “Design Thinking, IaC, and Automation.”

Blog

Design thinking & IaC automation: achieving impact faster with a clear focus

by Daniel Schweizer & Thomas Somogyi

The following use-case map shows the five application areas that are particularly well-suited for a structured approach. It links tangible benefits in IT operations with the fundamentals necessary for controlled implementation: data quality, governance, security, integration, and human-in-the-loop.

Use Case 1 – AI-Powered IT Service Desk and Self-Service

For many organizations, the IT Service Desk is an obvious starting point. Many inquiries are repetitive: password resets, access requests, software requests, simple "how-to" questions, or status inquiries regarding existing tickets.

AI-powered chatbots or AI agents can pre-screen, answer, or forward such standard inquiries to the appropriate support channel. This reduces the workload on first-level support while enabling users to receive an initial response more quickly.

The benefit does not lie in replacing support. Rather, the goal is to reduce repetitive tasks and improve the quality of service. This use case becomes particularly valuable when the AI agent can access a well-maintained knowledge base and is seamlessly integrated into an ITSM system.

A key element here is a clear escalation process: Critical, unclear, or security-related inquiries are automatically forwarded to the appropriate person. This ensures that the service desk remains manageable and efficient.

Use Case 2 – AIOps for Proactive Monitoring and Incident Handling

AIOps is one of the strongest use cases for AI in IT operations. Traditional monitoring often relies on static thresholds, individual dashboards, and numerous isolated alerts. In complex IT environments, this is often no longer sufficient.

AI can analyze logs, events, metrics, and monitoring data across various sources. This makes it possible to detect anomalies earlier, classify incidents more effectively, and narrow down potential causes more quickly. Instead of rigid limits, dynamic thresholds can account for a system’s normal state and flag deviations before they lead to a disruption. The added value lies not in yet another dashboard, but in the improved correlation of signals from different systems.

Here’s an example: Instead of reporting a CPU alert, a network error, or a storage bottleneck in isolation, an AIOps approach can detect patterns, identify affected services, and narrow down the most likely source of the issue using Fault Identification & Diagnostics. Depending on the level of maturity, this results in automated tickets, prioritized recommendations for action, or prepared remediation steps.
A practical example of this was provided by the Virtual AI Event “Artificial Intelligence in IT Operations.” The focus was not on a traditional service desk chatbot, but rather on an AI-supported operations workflow: Alerts and operational data are consolidated, relevant information from monitoring, logs, and the ITSM context is enriched, possible causes are narrowed down, and a ticket is prepared with context, confidence levels, and proposed solutions for IT operations.

Virtual AI Event Recap

Artificial Intelligence in IT Operations

Use Case 3 – Automated IT Operations, Runbooks, and Recurring Tasks

Many IT teams spend a significant portion of their time on recurring tasks: ticket routing, standard checks, scripting, provisioning, deprovisioning, reporting, or simple operational troubleshooting.

AI can support these activities by preparing runbooks, suggesting scripts, consolidating information, or triggering structured workflows. This creates significant leverage, especially when integrated with existing automation platforms, CI/CD processes, and Infrastructure as Code.

Controlled use is crucial here. In production IT environments, AI should not carry out changes in an uncontrolled manner. A tiered model makes more sense: AI analyzes, suggests, documents, and prepares. Critical actions are safeguarded through defined approvals, role-based models, and auditability.

This means that AI does not become a risk, but rather an accelerator for standardized operational processes—provided the foundation is right. The article “CI/CD and Infrastructure as Code” shows how structured automation, versioning, and controlled implementation lay this foundation.

Blog

CI/CD and IaC

by Thomas Somogyi

Use Case 4 – AI-Based Knowledge Management and Documentation

Outdated documentation is a common problem in many IT organizations. Knowledge is scattered across tickets, emails, chat histories, wikis, runbooks, monitoring systems, or even held solely by individual key personnel. In day-to-day operations, this leads to dependencies, longer onboarding times, and repeated clarifications.

AI can help make this knowledge more accessible. Tickets can be consolidated, lessons learned from incidents can be organized, runbooks can be prepared, and knowledge base articles can be generated. Internal support bots can also help employees find relevant information more quickly.

The benefits are particularly significant when information is available but difficult to find or documented inconsistently. AI can bring structure to this and make knowledge more scalable.

Nevertheless, quality assurance is essential. While AI can be used to create or update documentation, it should not be relied upon uncritically as the sole source of knowledge. Expert review, clear ownership, and an established review process remain crucial.

Use Case 5 – Predictive Maintenance and Capacity Planning

The fifth use case shifts IT operations from a reactive to a proactive approach. While AIOps monitors the current state and responds to disruptions, predictive maintenance and capacity planning use historical operational data to identify future bottlenecks and outages earlier.

Typical use cases include impending system failures, capacity bottlenecks in computing, storage, or networking, and foreseeable maintenance needs for critical components. Trend analyses can be used to estimate utilization and growth over time, allowing for the planning of expansions and investments before a bottleneck affects operations.

In our webinar, “Data and AI for Modern Network Infrastructures,” this forward-looking perspective was explored, ranging from a description of the current state to predictions of future needs and concrete recommendations for action.

Webinar Recording

Data & AI for Modern Networks

It’s important to note that predictive maintenance is not a magical early-warning system. Its quality depends heavily on the available data, measurement points, historical data, and model validation. Not every failure gives advance warning. Some components fail suddenly, without any reliable indicators having been visible beforehand. Incomplete data, missing measurement points, or extraordinary events can also lead to incorrect assessments.

However, when used correctly, predictive maintenance becomes a management tool for IT leaders. It supports more predictable operations, better investment decisions, and more proactive capacity planning.

Which use cases should we tackle first?

Not every organization should start with the same use case. The key factors are the current situation, data quality, process maturity, risk, and expected benefits.

Use Case	Target Vision	Good MVP candidate if…
IT Service Desk / Self-Service	Fewer Tickets, Better User Experience	There are many recurring standard inquiries
AIOps / Incident Handling	More stable operation, faster response	Monitoring, log, and event data are available
Operations Automation	Less manual work, fewer errors	Runbooks and approval logic are clearly defined
Knowledge Management	Making Knowledge Scalable	Relevant sources and review processes are available
Predictive Maintenance	Proactive Operations, Better Planning	Historical data and measurement points are reliable

A good starting point is usually where value, data availability, and feasibility converge. Often, a clearly defined MVP is more suitable than a large-scale transformation program. This approach allows you to test assumptions, assess data quality, identify risks, and build acceptance.

Start pragmatically, scale in a controlled manner

AI can significantly reduce the workload on IT operations. However, the benefits do not arise automatically from the use of new tools. The key factors are the right use cases, a clean data foundation, clear processes, governance, security, and a deliberate “human-in-the-loop” approach.

For IT leaders, this means that AI should not be launched as an isolated technology project, but rather as a targeted enhancement of IT operations. The service desk, AIOps, operations automation, knowledge management, and predictive maintenance offer concrete starting points for this.

atrete helps IT organizations identify suitable AI use cases, evaluate them realistically, and implement them pragmatically. From potential analysis through architecture design and sourcing to the implementation of an MVP, we combine technical expertise, independent consulting, and experience in IT operations.

Would you like to find out which AI use cases offer the greatest potential benefits for your IT organization? We can help you turn your ideas into concrete, actionable projects.