How to Build a Successful AIOps Roadmap in 9 Practical Steps

When discussing AIOps with customers, many feel it is not mature enough for analysis, and some argue that its capabilities develop linearly, requiring prior assessment of event and alert handling maturity before adoption.

Although AIOps is not constrained by ITIL and can be implemented step‑by‑step, the industry still lacks concrete guidance. This article offers a nine‑step roadmap to establish AIOps best practices.

AIOps Quick Overview

Gartner identifies a shift in IT: traditional processes and tools no longer meet the challenges of modern digital business, which demand faster data transmission, diverse data types, and real‑time analysis.

The answer is AIOps , which integrates IT Service Management (ITSM), IT Operations Management (ITOM), and data‑level automation.

AIOps stores data on a big‑data platform that supports real‑time analytics and historical queries, leveraging machine‑learning for unattended data‑stream processing.

The core idea is that traditional IT tools remain useful—service management handles requests and events, performance management monitors metrics, logs, and events—while their data is correlated and analyzed with machine learning to enable faster, automated decision‑making.

Final State

The ultimate goal of AIOps is seamless data flow from multiple sources into a large data platform that can ingest, analyze, and post‑process data, using machine learning to refine algorithms.

The platform should automatically trigger workflows, feeding results back as secondary data sources, allowing the system to adapt, scale, and notify administrators as data volume, type, and source change.

Roadmap: 9 Steps

Identify current use cases

Reach consensus on system records

Define success criteria and start tracking them

Evaluate current and future data models

Analyze existing workflows

Begin automation implementation

Develop new analytical workflows

Help the organization adopt new skill sets

Customize various analytical techniques

Early Stage

Identify Current Use Cases

Start with familiar areas; many users lack use cases that address emerging technologies. List the use cases you are handling or planning to solve, then:

Outline how to achieve expected outcomes

Prioritize specific use cases

Highlight gaps between current capabilities, tools, skills, and target goals

This approach uncovers new possibilities and challenges, helping you bridge the gap from current state to desired outcomes.

Assess Data Freedom

The primary element of AIOps is the free flow of data from diverse tools into a big‑data store.

Evaluate the accessibility and frequency of data collected by your IT systems; the ideal model streams data in real time.

Most monitoring or service‑desk tools lack outward data streaming, though newer versions may offer REST APIs. Traditional relational databases (e.g., Oracle, SQL) were not designed for continuous data export and can impact performance, so they generally cannot support data streams.

Early AIOps planning should answer:

How can I extract data from current IT tools?

What type of data is available?

Can I retrieve it programmatically?

What is the data collection frequency?

Addressing these constraints may require shifting from batch uploads to streaming or replacing tools with those supporting real‑time data flow.

Reach Consensus on System Records

Collaboration between IT operations and service‑management teams is essential. Agree on the minimal data set needed to overcome current system limits, its location, and shared views and access permissions.

Traditionally, service desks store request, incident, and change data, but tools like Jira and APM introduce challenges for capturing multi‑source threats.

Implementing AIOps means identifying all effective outcome metrics across the application, service, and business value chain, and establishing a plan to aggregate this data.

Build dashboards on the big‑data platform to filter and create specific data views, starting with subsets fed back into existing record systems (e.g., Jira tickets, APM events).

Define Success Standards and Track Them

Successful IT management starts with clear KPIs and metrics. Actionable steps include:

Identify what to measure

Implement consistent, comprehensive measurements

Provide regular visual performance reports

Enable accountability for responsible parties

Most IT tools offer built‑in measurement templates, but raw numbers alone do not reveal causal relationships; thoughtful analysis is required to drive business improvement.

Mid‑Stage

Evaluate Current and Future Data Models

Understanding data models of each source is crucial so AIOps can recognize them and assess interactions and outcomes.

Many IT tools hide their data models, and organizations often lack insight into differences between NoSQL big‑data platforms and traditional SQL databases.

AIOps links data from various IT and non‑IT sources within a big‑data repository, enabling analysis and trend detection without additional development.

Key data structures include timestamps, attributes (key‑value pairs), historical records, effects (time‑based trends), and application/service/business models that support asset grouping, relationships, and deduplication.

By constructing robust time‑series data, AIOps can combine IT and non‑IT data, increase granularity (e.g., from 5‑minute to 1‑minute intervals), and perform real‑time or historical analysis.

Since captured events are often semi‑structured or unstructured, AIOps must normalize them for further analysis.

Analyze Existing Workflows

Beyond tool‑generated data, conduct periodic manual analyses to improve processes, reduce costs, and boost performance.

Iterative manual analysis helps identify automation opportunities, decreasing manual effort while increasing analysis speed and scope.

Start Automation Implementation

Automation value varies across teams. IT Ops focuses on automating tasks such as service‑desk operations, patching servers, and auto‑remediation, requiring coordination across tools.

DevOps targets automating development tasks and business processes to eliminate bottlenecks from waterfall practices, accelerating CI/CD pipelines.

Effective IT Ops must stay aligned with DevOps speed and agility, sharing information and collaborating to maintain visibility of code changes, environment impacts, and workload backlogs.

Late Stage

Develop New Analytical Workflows

After analyzing existing workflows, automate and extend the AIOps solution, then:

Assess the value of current workflows

Modify and improve them

Develop new workflows to address identified gaps

Use feedback from automated analyses to create new analytical workflows.

Help the Organization Adopt New Skill Sets

IT Ops staff transition from operators to auditors, shifting focus from device control to business‑data analysis.

While deep data‑science expertise isn’t required, understanding data handling and business goal alignment is essential for AIOps success.

Organizations should cultivate AIOps‑capable talent to drive structured, scientific transformation.

Customize Various Analytical Techniques

Implement data‑science practices by collaborating with data scientists, developers, and analysts to build algorithms that run on large data sets, using Python or R.

IT Ops personnel need not master mathematics or programming but must manage semi‑intelligent, semi‑autonomous system architectures and select appropriate vendor‑provided analytical solutions.

In daily operations, AIOps platforms can deliver real‑time, customized regression analyses to support decision‑making.