Sam Altman, the key visionary behind the popular adaption of Gen AI and essentially the father of ChatGPT, deemed it “unthinkable” to have products and services without AI integration in the future. I’m sure that among other beliefs that inspired him to make such claims, the need for intelligence beyond efficiency in the modern digital ecosystem was a key one. It only makes sense to rethink IT management in this context and replace our traditional methods with the benefits of AIOps.

AIOPs, or AI for all your IT operations, is a proactive approach to predicting possible deviations and managing operational failures in real-time. In a world where recommendation engines decide our next meal, we cannot cling to traditional ITOps. That is why the AIOps market is reportedly growing at a CAGR of more than 38% from USD 3.7 trillion in 2023. Let us have a deeper look into the appeal of AIOps and its benefits that have forced businesses to claim significant improvements in service availability within 12 months of its adoption. 

What Traditional IT Operations Management Challenges Are Building Space for AIOps?

Traditional IT operations management already provided benefits such as managing the workload, streamlining IT operations, and appropriately reacting to performance failures. Then, what challenges created the void we now expect AIOps to fill:

• Too much data: The emergence of technologies like AI, ML, and analytics is championing data collection from every source possible. Traditional IT resources need to be equipped to handle this overwhelming amount of data. AIOps can ensure that monitoring tools keep up with the complex industry use cases and ensure smooth IT management even with large data volumes.
• Troubled Visibility: Another thing that overwhelms traditional IT management tools is the complexity of business functions that demand variegated architectures. Such microservices and containerization-based architectures limit the visibility of monitoring tools. AI capabilities are required to process all the logging data in real-time without derailing IT operations.
• Compromised Agility: Monitoring with limited visibility and too much data naturally causes delays in integrations and deployment. This fits poorly with the essence of agility or DevOps. As a surface-level observation, one can conclude that the ambitious innovations to digitize nuanced customer experiences are causing challenges for traditional ITOps.

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What is AIOps?

Artificial intelligence for IT operations (AIOps) is an advanced approach that uses AI, machine learning, and big data analytics to automate and enhance IT operations. AIOps integrates a wide range of tools and systems to help IT teams improve decision-making and streamline operations by analyzing vast amounts of data in real time.

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Types of AIOps

The problem with the one-size-fits-all AIOps approach is that the architectures and security needs vary across businesses. That is why, as per user needs, we have localized or used thorough approaches for AIOps implementation.

• Domain-specific AIOps: Specific domains can be picked for businesses not planning to implement AIOps at different aspects of their digital ecosystems. Such domain-specific AIOps resources can focus on either network, management, or security, depending on the business requirement.
• End-to-end AIOps: This is, of course, a more holistic approach to AIOps where, instead of siloed domains, the entire IT environment falls under the AIOps purview. The monitoring is more thorough and a single source of truth can be used to streamline the entire ITOps.

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Importance of AIOps in Modern IT Operations

Without AIOps, the IT operations handling modern software product engineering would remain bogged down in manual monitoring and reactive troubleshooting. That’s not an ideal situation for businesses while developing a surgery assistance platform, a neobank solution, or any other nuanced industry use case. Here are some points that make AIOps important in contemporary digital pursuits:

• Real-time data insights and predictive analytics
• Automated anomaly detection and response
• Seamless integration and operation across multiple environments
• Rapid adaption and response to frequently changing demands during the SDLC

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Key AIOps Technologies

Managing IT operations involves dealing with interconnected systems that are handling complex use cases. These systems are highly prone to downtime, performance degradation, or security issues. Therefore, employing AIOps to manage these systems seems a smart move only if its core technologies can handle various aspects of these vulnerabilities.

• Artificial Intelligence (AI):  So, the intelligence part of ITOps management is handled by AI which can help with proactive decision-making and autonomous operations. Tasks like resource allocation, incident resolution, system monitoring etc. can be managed through AIOps using this technology.
• Machine Learning (ML): All the historical data about operations can offer deep insights into peculiar vulnerabilities of the IT system. ML can help sift through this data and recognize patterns that would guide the AI-powered tools with actionable insights. 
• Data Analytics - The data coming from relevant metrics, logs, and other such sources need real-time analytics. Therefore, AIOps would require data analytics to ensure that large volumes of monitoring data can be correlated for relevant decision-making in IT.stems, and support decision-making to boost performance.

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How AIOps Transform IT Operations

If there’s any job that requires an overlap with firefighting skills, it's IT management. Too many variables in too little time and all of them have equal chances of going wrong.  The utility of AIOps emerges from this very predicament faced by IT management teams. Bringing technologies like AI, ML, and data analytics into this can help ITOps improve the ways it monitors, detects, diagnoses, and resolves any issues. Let’s have a look at the transformational benefits of AIOps on IT management.

Read more: Evolution from ITOps to DevOps Automation

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Benefits of AIOps in IT Operations

• Performance insights: The number of tools and data that traditional ITOps have to deal with can be overwhelming. AIOps can help with this struggle by streamlining the insights coming from these tools. This makes the diagnosis and resolution of performance bottlenecks more quickly.
• Continuous monitoring: Real-time monitoring of applications can be difficult given the vast amount of data that needs to be processed. AIOps allows Ops teams to analyze this data coming from different KPIs and detect any unusual deviations.
• Event correlation: The alerts in IT management can often be false positives. This has always required a manual intervention in ITOps. How AIOps helps here is by offering data correlation from these events. This correlation helps aggregate that alerts data in a meaningful and noiseless way and offers insights for proactive action.
• Security Management: A vast bandwidth of ITOps is also dedicated to maintaining a stable and reliable security posture. AIOps trumps traditional IT by offering improved alerting systems, vulnerability insights, and cybersecurity guidance.

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AIOps Use Cases

Owing to the capabilities of its core technologies AIOps can deal with a vast number of use cases in IT management. Here’s a list of the major ones.

• Environment monitoring: Machine learning and data analytics combine in AIOps to help sift through monitoring data. The analysis and the resulting insights can help make effective decisions on various environment variables, including network bandwidth, response times, storage utilization, event correlation, and more.
• Root cause analysis: Understanding its implications matters more than collecting data. Traditional ITOps take time to do This due to limited processing capacity. AIOps offers the ability to correlate different data points and easily identify the root cause of performance deviations.
• Quick Response: AIOps offers smart automation for proactive incident response. By leveraging pre-defined workflows and AI, data analytics, and other technologies to reduce MTTR, AIOps quickens the response time for network outages, application performance failures, and other such incidents.
• Security Monitoring: Unidentified security vulnerabilities can lead to unauthorized access and suspicious performance deviations. AIOps can help identify these vulnerabilities by offering specific insights focused on security aspects of the IT environments. 

Read how Resolve's AIOPs solution reduced downtime by 75%.

How Does AIOps Work?

Making AIOps work in your IT environment requires a step-by-step strategic approach. Here’s how you can achieve it:

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Step 1 - Data collection

The initiation requires identifying all the different data sources that can help observe, understand, and analyze the IT environment. These include health metrics, application performance data, error logs, cloud usage statistics, security logs, and more. The idea is to have as much relevant data as possible to ensure deep insights on the ITOps. Tools like Prometheus and Splunk can help with this data collection and indexing.

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Step 2 - Normalization

The data can then be cleaned for recognizable formats, easier understanding, and quick processing. Such normalization and pre-processing will ensure that only relevant data is taken forward and that no noise is discouraged in decision-making. For AIOps, it is also essential that this is done in real-time. AIOps tools relevant for this purpose include Apache Kafka, Databricks, and more.

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Step 3 - Pattern Recognization

The normalized and clean data can then be processed for repeated patterns. For this, the first requirement is to establish a standard baseline performance in different components of the application. This will help with easy anomaly detection using data analytics and AI tools like Tensorflow, Pytorch, and more. Any unusual spikes can be easily noted and further processed by ML models.

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Step 4 - Data Correlation

The next step in AIOps implementation is laying out a system for data correlation. Here, the different alerts and spikes data can be grouped using analytics tools, including Moogsoft, Bigpanda, and more. Any noisy data in his group can also be removed to ensure more focused insights. 

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Step 5 - Predictive Analytics

Once data correlation is in the picture, predictive analytics can be easily set up for AIOps. AI/ML and analytics tools like Azure machine learning, data robots, and AWS Sagemaker can help with trend analysis to predict any failure in the IT environment. Usage trends can also help with real-time resource forecasting for benefits like provisioning and allocation of resources.

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Step 6 - Report

AIOps need dashboards and other such reporting mechanisms to ensure real-time monitoring and correlation. These reports can include actionable insights on system health monitoring, network throughput, application response times, and more. The necessary tools for this purpose can include Grafana, Kibana, Tableau, and more.

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How would you describe your organization’s current level of AIOps maturity?

Based on how much engagement you have achieved with AIOps in your daily operations in the business, the AIOps maturity can vary for different businesses. Here’s how you describe these levels:

• Exploring: Businesses that have identified use cases for the implementation of AIOps.
• Active: Businesses that have implemented AIOps as a proof of concept.
• Operational: Businesses that leverage AIOps in production and see measurable value.
• Systematic: Businesses for whom AIOps is pervasive and requires organizational change.
• Transformational: Businesses that have integrated AIOps across our processes, products, and services, achieving human and AI synergy.
• N/A: Businesses for whom AIOps is not strategic for our organization.

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Challenges in Implementing AIOps

Implementing AIOps can bring a lot of benefits to your IT management strategies. However, its implementation needs a lot of challenges:

• Data Integration: AIOps is a highly data-dependent framework. However, integrating such data sources into the exosystem for nuanced logs and metrics capturing is not that easy. Aggregating data from disparate systems can get overwhelming, especially with cloud infrastructure and other networking devices. Moreover, filtering noisy data from consistent information is also challenging, and failure can highly mislead the AI models.
• Adaptability: Even with the successful implementation of AIOps, it is very important to maintain adaptability towards changing performance patterns. With time, the ML models can lose grip on system behavior and may fail to predict many vulnerabilities.
• Skill Gaps: Implementing AIOps not only needs skillset experienced in its core technologies but also familiarity with relevant tools and platforms. Having a workforce that culturally aligns with AIOps is difficult and needs active investment in upskilling.
• Legacy Resistance: Legacy systems make it hard for AIOps tools to engage with the digital ecosystems. Challenges in integration with AI-powered tools and automated tools for monitoring and analytics can lead to inefficient or even failed AIOPs.

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Best Practices for Implementing AIOps

The best practices in AIOps are guided by the ease with which organizations can navigate the AIOps challenges we discussed above. Here are some of the essential practices.

• Data Quality: Implement rigorous data cleansing, validation, and normalization processes to ensure that AIOps receives accurate data.
• Unified Data Sources: Create a data integration pipeline that collects information from various IT systems (e.g., cloud, on-premises, hybrid environments) into a central repository.
• Automation of Data Collection: Automate data collection and ensure that all relevant data sources are connected to the AIOps system for comprehensive analysis.
• Seamless Integration: Choose tools that can integrate with your existing monitoring, alerting, and incident management systems, such as Splunk, Datadog, or ServiceNow.
• Collaboration: Work closely with operations, security, and development teams to ensure AIOps fit within their workflows without causing friction.
• Workflow Automation: Gradually automate routine tasks and incident responses, ensuring there’s always a human in the loop for critical decisions.
• Performance Tracking: Monitor the effectiveness of AIOps models in real-time, ensuring they continue to deliver accurate predictions and insights.

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Conclusion

Just like any other aspect of software product engineering, management of IT operations without AI integration should be “unthinkable” for modern digital ecosystems. AIOps is a key player without which the various software product engineering teams cannot function. A consistent effort to navigate through its challenges and adherence to its best practices can help businesses build and run a robust IT environment.

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