10 AIOps Best Practices

AIOps, which stands for artificial intelligence for IT operations, is a growing industry practice in which IT professionals use AI, machine learning, and automation techniques to improve their workflows, with a goal of improved efficiency and standardization.
Businesses everywhere are adopting the practice because, in theory, it should make their jobs easier. However, AIOps is a complex process that, when executed without a clear plan in place, can lead to more inefficient and convoluted IT processes.
To achieve the best results when first implementing AIOps in your organization, it’s important to understand not only how AIOps can benefit your team but what AIOps best practices you should follow from the outset.
AIOps Best Practices: Table of Contents
1. Invest in an AIOps Platform That Integrates With Your Existing Tool Stack
2. Define Concrete AIOps Operations and Goals in Advance
3. Identify Relevant Data and Data Sources
4. Maintain High Data Quality Standards
5. Don’t Skimp on Data Security Best Practices
6. Complete an AIOps Test Project
7. Establish an AI Use Policy for Your Organization
8. Regularly Monitor Your Network and AIOps Workflows
9. Document AIOps Processes as They Are Established
10. Don’t Limit AIOps Best Practices to IT Use Cases
Bottom Line: Implementing AIOps Best Practices
The Top AIOps Best Practices
1. Invest in an AIOps Platform That Integrates With Your Existing Tool Stack
AIOps is all about making your current artificial intelligence and IT processes more efficient, and that only happens if your AIOps tool(s) integrate with all of the other most important resources, data sources, and business applications in your tool stack.
For the best possible results, look for AIOps platforms with deep native integration libraries or a capable API that makes it easy to connect your AIOps platform across your IT ecosystem. Aim for minimal customization and fine-tuning requirements, at least at the start, so you can keep the AIOps platform running efficiently even as your other tools change and scale.
2. Define Concrete AIOps Operations and Goals in Advance
AIOps is only effective if you know what you’re using it for and what you’re hoping to improve with AI-driven automation and workflows.
Every organization’s goals and KPIs are different when it comes to AIOps, but regardless of how the individual goals look, you’ll achieve better outcomes if your goals are framed as part of larger IT, operations, and business objectives.
For business leaders who are not sure where to start, consider setting goals around the following IT operational tasks and improvements:

Improved mean time to resolution (MTTR)
Improved mean time between failures (MTBF)
Reduction of IT incidents and outages, particularly for critical services
Resource optimization and allocation across cloud and on-premises resources
Proactive issue detection and incident mitigation
Reduction of false positives and false negatives
Resource provisioning time
Capacity utilization across usage metrics
Quality and frequency of change impact predictions
Number of policy and compliance violations over time
IT operational expenses over time

3. Identify Relevant Data and Data Sources
Once you’ve completed initial tool research and goal setting, you’ll need to get a handle on all relevant data sources that need to be accessed, integrated, and migrated for effective AIOps.
Consider the different formats of data involved, how this data is used, and the different location(s) where this data currently lives in your network. Most important, determine if this data exists in different formats across your organization; if that’s the case, you’ll want to clean and standardize that data before adding it to your AIOps system.
4. Maintain High Data Quality Standards
Tools like SAP Master Data Governance can be used to manage data quality rules across AIOps workflows. Source: SAP.
AIOps relies heavily on data for both AI/ML training and operational queries. It’s important to hold your organization to high data quality standards so all data that you need is accessible, understandable, and in an actionable format.
Some of the steps you can take to improve data quality include using data cleansing, data mapping, data preparation, and data integrity management tools. You can and should also invest in data governance tools, as these tools can help improve data quality, security, and compliance while also assisting IT teams in monitoring and addressing changes in data quality over time.
5. Don’t Skimp on Data Security Best Practices
Data security best practices and tools are necessary for smooth operations, regulatory compliance, and maintaining business reputation and customer trust. Beyond investing in data governance tools as mentioned above, your AIOps teams should adhere to the following data security best practices as well:

Use only secure and trusted databases and data sources.
Keep your data storage systems updated and make sure all data storage practices align with regulatory and security requirements.
Set up mobile device use and data management policies for employees.
Designate a multi-tier access and authorization system for your most critical assets.
Whenever it’s necessary for larger groups of people to access or use sensitive data, look for additional ways to protect that data, such as data encryption, anonymization, and/or masking.

Bear in mind that if your organization is interested in using generative AI in its AIOps workflows, additional data security rules and best practices may be necessary.
Also read: Generative AI and Data Analytics: Best Practices
6. Complete an AIOps Test Project
Before setting up the entire network and several automations within an overarching AIOps framework, you should first complete a test run with a smaller project as a proof of concept. During this test run, you can monitor how the infrastructure performs and also make note of how the team handles this new workflow. From there, you can make adjustments to infrastructure, team training, cybersecurity tooling, and the AIOps plan accordingly after the test project is over.
7. Establish an AI Use Policy for Your Organization
Any organization, department, or team that uses AI needs to be held to an AI use policy and receive training on the technology and its role in operations.
The policy should particularly address questions about how AI is being used in the organization, individual roles and responsibilities when using AI, and how to maintain data security and integrity while using AI. If you’re looking for an example AI policy to use as a foundation for your own, this generic artificial intelligence ethics policy may be a good place to start.
8. Regularly Monitor Your Network and AIOps Workflows
Neptune.ai is an example of an AI monitoring tool that allows users to monitor AI and ML model performance as well as other relevant metrics. Source: Neptune.ai.
Your team should be monitoring network performance at all times, but it’s especially crucial when implementing a new operational practice like AIOps.
On a regular basis, you’ll want to use monitoring tools with features that support analytics and deeper dives into AI and ML performance. On a less frequent basis, your team or an objective third party will need to conduct deeper network audits to assess how each piece of the AIOps workflow is performing and how or if it’s impacting the rest of your network’s performance.
Some monitoring tools simply notify users when a possible issue arises, while others offer suggestions or take corrective action. You’ll want to select a monitoring tool that complements your team’s skill sets, budget, and other team requirements.
9. Document AIOps Processes as They Are Established
Documenting AIOps processes after they are quality-tested and approved is one of the surest ways to protect the integrity of the overall operation.
Concrete, detailed documentation also supports change management efforts, giving business leaders the resources they need to keep things running smoothly as team members, tools, and other components of the workflow change over time. Documentation can be kept in traditional data storage and database systems for easy access, but many businesses will benefit from storing this documentation in an additional, off-network or highly secure location.
10. Don’t Limit AIOps Best Practices to IT Use Cases
AIOps is technically focused on IT operations, but these best practices can be applied across an organization, especially as AI and advanced business technologies become a normal part of enterprise operations outside of IT departments.
Making sure all members of your organization across all departments understand how AI can automate their workflows and create new efficiencies could help your overall organization get roles and responsibilities, tool sprawl, and inefficient daily operations under tighter control.
Learn more about why AIOps best practices should be implemented across different business departments.
Bottom Line: Implementing AIOps Best Practices
AIOps is still a fairly new IT operations strategy, but because of the automations and new efficiencies it introduces to team workflows, it has quickly gained traction across industries and teams.
Despite its advantages, IT professionals need to remember that setting up AIOps can be a complex, multi-faceted process and getting it wrong may create even bigger operational headaches than existed before, not to mention potential new security and compliance issues.
If your team takes the extra time necessary to properly set up automated workflows with appropriate expectations and rules from the beginning, your AIOps plan stands a better chance for success in the long run.
Read next: Best Artificial Intelligence Software
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