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The Growing Interest in AIOps: ML and Generative AI Use Cases

1 Like 5 02 April 2025 Be the first to comment

Samuel Crompton

Associate Partner (Banking, Resilience and AI)

IBM

The hype surrounding generative AI is fuelling a wave of interest in AIOps, with a recent Kyndryl-IDC study revealing that 63% of companies plan to increase spending on automation and AIOps over the next two years, even as overall IT budgets remain flat or decline.

While AIOps has traditionally leveraged machine learning (ML) for insights and automation, the rise of Generative AI (GenAI) is pushing the boundaries, taking the field to a whole new level.

For those already familiar with the AIOps landscape, ML's role in solving IT Service Management (ITSM) challenges is nothing new. However, the renewed enthusiasm spurred by GenAI is prompting organisations to address more critical data-related challenges.

As data becomes the cornerstone of effective AI-driven insights, the need for high-quality data is growing exponentially. Organisations are realising that AI and ML models can only be as effective as the data they are trained on, which is amplifying the importance of data quality in the AIOps ecosystem.

High-quality, clean, and well-structured data is now paramount for achieving meaningful results in anomaly detection, root cause analysis, and predictive analytics.

Poor data quality—such as incomplete, inconsistent, or biased data—can significantly undermine the performance of AIOps systems, making data governance and data hygiene critical components of any AIOps strategy.

AIOps Use Cases Powered by ML and GenAI

As organisations dive deeper into AIOps, a few notable use cases have matured, demonstrating how both machine learning and generative AI are transforming IT operations:

Spotting Anomalies with Machine Learning
ML models sift through massive volumes of IT data to identify unusual patterns, helping to detect potential system failures before they escalate into significant problems. The better the data fed into the models, the more accurate and timely the anomaly detection becomes.
Root Cause Analysis
AI models help connect the dots between disparate IT events, making it easier to identify the root causes of issues and resolve them more quickly. This requires a continuous flow of high-quality data from various systems to maintain the accuracy of AI’s troubleshooting capabilities.
Predicting Incidents
By learning from historical data, ML models can recognise patterns that signal potential issues, enabling teams to address problems before they occur. To maximise the effectiveness of predictive models, it's essential to have a comprehensive, clean, and up-to-date dataset, which enhances the predictive power and accuracy of these models.

The Role of Generative AI in AIOps

Going beyond traditional ML applications, Generative AI is introducing a new set of rapidly evolving use cases, including:

Smart Incident Response Guides
Generative AI can automatically create customised playbooks for handling incidents, allowing IT teams to respond more efficiently. These playbooks are created based on historical incident data, so ensuring the data is clean and well-structured is crucial to producing effective and relevant guides.
Creating Synthetic Data for Testing and Training
Generative AI can generate realistic IT operation scenarios, providing a risk-free environment for testing and training without exposing sensitive data. By leveraging high-quality, representative data during training, organisations can ensure that the AI systems are robust and capable of handling real-world issues.
AI-Powered IT Assistants
GenAI-driven virtual assistants offer real-time, human-like responses to IT queries, streamlining support and improving the user experience. The performance of these assistants is heavily reliant on the quality and breadth of the training data. Well-structured, diverse, and high-quality data ensures that AI assistants provide accurate, contextually appropriate responses.

The Intersection of ML, GenAI, and Data Quality

By combining ML’s data-crunching power with GenAI’s creative problem-solving capabilities, AIOps is becoming increasingly intelligent and adaptive. However, for these technologies to reach their full potential, high-quality data is essential.

As companies refine their AI strategies and continue to integrate these technologies, the fusion of ML and GenAI will lead to more proactive, resilient, and efficient IT operations. The future of AIOps will be shaped not only by the sophistication of the algorithms but also by the quality of the data that powers them.

Ultimately, the question remains: will machine learning or generative AI prove to be more transformative in shaping the future of IT operations? The answer likely lies in their combined use, underpinned by a foundation of high-quality data.

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Samuel Crompton

Associate Partner (Banking, Resilience and AI)

IBM

Member since

17 Jul 2019

Location

London

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