In an ever-evolving technological landscape, the roles of DevOps, Site Reliability Engineers (SREs), and Platform Engineers have become increasingly crucial and demanding. These professionals are tasked with maintaining the stability, performance, and security of complex systems, often under intense pressure and ever-increasing backlogs. To alleviate their burden and enhance operational efficiency, it is critical to integrate Artificial Intelligence (AI) in platform engineering for Incident Response (IR) and Root Cause Analysis (RCA) activities.
Generative AI is considered strategically important for platform engineering, with 45% viewing it as a core component of their strategy.
Red Hat’s State of platform engineering in the age of AI, Nov 12, 2024
AI-driven tools are excellent at processing vast amounts of data quickly and accurately, easily surpassing human capabilities, even when using algorithmic approaches. Incident Response often involves repetitive and mundane tasks such as log analysis, alert correlation, and remediation processes. AI can automate these tasks, increasing efficiency, ensuring consistency, and reducing the risk of human error. For incidents that require RCA, AI solutions can assist in first-level incident triage, looking for incident correlation, agentically performing immediate responses to common issues, proposing potential remediation steps for review, and escalating complex problems to human engineers.
One of the significant advantages of AI in IR is its ability to predict and prevent incidents before they escalate into critical problems. Through AI-powered predictive analytics that crosses over multiple environments with historical data, potential issues can be identified and addressed proactively, minimizing the impact on system performance and user experience. This proactive approach not only enhances system resilience but also reduces the stress and workload on DevOps, SREs, and Platform Engineers, who can instead shift their focus from firefighting to strategic initiatives aimed at continuous improvement.
Root cause analysis is one of the most time consuming and repetitive tasks facing engineers as they deal with incidents. Traditional methods of RCA often involve manual sifting through logs, traces, and metrics while correlating events, which is time-consuming and error prone. By leveraging AI and machine learning algorithms, these tools can identify patterns and anomalies in system behavior, facilitating swift and precise RCA. AI eliminates manual effort, allowing engineers to pinpoint the root cause of issues and generating proposed remediation steps with far greater accuracy and speed. This reduces downtime, enhances system reliability, and greatly enhances the experience for platform engineers.
By 2027, 70% of professional developers will use AI-powered coding tools, up from less than 10% today.
Gartner’s Set Up Now for AI to Augment Software Development, Sep 21, 2023
As organizations grow and their systems become more complex, the volume and variety of data generated can eventually overwhelm the platform teams that handle IR and RCA. AI offers scalable solutions that can adapt to these growing demands. Solutions in this space are rapidly developing more refined skills, learning from new data to improve accuracy and effectiveness over time and gaining agentic capabilities. This adaptability indicates an investment in AI-driven RCA and IR tools will continue to produce benefits even as technology and organizational needs change.
What’s Next
We are just starting to see the potential impact of Generative AI across development and the entire platform engineering ecosystem. Along with benefits, this adoption presents many challenges. In the forefront is how to enable platform engineering teams to handle the influx of AI generated code and configurations.
39% of developers trust the quality of gen AI output only “a little” or “not at all”
DORA's Fostering developers' trust in generative artificial intelligence, Jan 28, 2025
Agentic solutions will play a part, and business will have to adapt and evolve AI and ML use across the organization holistically to lower their risk. Successful organizations will strategically pull in key consultants while hiring, training, and retaining senior and principal platform architects to design their environment for long-term success across the rapidly evolving ecosystem of AI and ML solutions.
The integration of AI in platform engineering for IR and RCA is not just a trend but a necessity in today's fast-paced and dynamic technological environment. By enhancing efficiency, reducing incident response overhead, automating repetitive tasks, and providing scalable solutions, AI lightens the workload on DevOps, SREs, and Platform Engineer teams. This, in turn, leads to more reliable systems, improved operational efficiency, and greater job satisfaction for engineering teams.
This IR and RCA use case is a great opportunity to develop organizational capabilities, then reuse and adapt that AI approach to impact other business aspects. Embracing AI is a crucial step towards a more resilient and agile organization, capable of meeting the challenges of the future head-on.
Please reach out to us if you would like assistance adopting effective AI solutions for your DevOps, SRE, and Platform Engineering teams.
Darren possesses over two decades of experience in diverse areas such as automation, public and private cloud infrastructure, security, monitoring, observability, high-performance computing (HPC), artificial intelligence (AI), Site Reliability Engineering (SRE), DevOps, and Platform Engineering. Complementing his robust technical background in Computer Science, Darren holds an Executive MBA and has extensive consulting and leadership experience. In his leisure time, Darren enjoys exploring the Rocky Mountains, fly fishing, camping, and hiking with his wife and three children.