Published on November 7, 2023, 10:29 am

TLDR: Transposit's annual study on DevOps automation and AI in incident management reveals that although a majority of organizations have defined incident management processes and adequate automation, they struggle to quickly resolve service incidents. The study highlights the potential of generative AI in addressing this issue by streamlining incident resolution and proactively detecting and preempting issues. Challenges faced by organizations include brittle automation scripts, excessive manual processes, and limited access to specialized knowledge. To improve incident management capabilities, organizations plan to enhance their tech stack and implement new tools such as site reliability engineering practices. The study emphasizes the importance of integrating generative AI capabilities into incident management tools to decrease the time it takes to create new automations and empower humans to use their judgment at critical decision points. By leveraging automation and AI while keeping humans actively engaged, organizations can achieve seamless incident resolution and drive operational excellence.

Transposit, an AI-powered incident management company, has released the results of its third annual State of DevOps Automation and AI research study. The study delves into the complex challenges faced by organizations when it comes to effectively managing incidents.

The findings reveal an incident management paradox: despite a majority of respondents (59.4%) having a defined incident management process in place and adequate automation (71.1%), organizations still struggle to quickly resolve service incidents. Over the past year, 66.5% of organizations reported an increase in the frequency of service incidents that affected their customers, putting them at risk of losing up to $499,999 per hour on average.

Generative AI emerges as a promising solution to address this incident management paradox. According to the research, 84.5% of respondents believe that AI can significantly streamline their incident management processes and improve overall efficiency. The use of generative AI, combined with automation and human judgment, holds the potential for not only expediting incident resolution but also proactively detecting and preempting issues before they escalate.

Reliability engineering teams face significant hurdles in incident management. Challenges include brittle automation scripts (59.7%), excessive manual processes (47.8%), and difficulty accessing specialized knowledge (47.2%). Moreover, some organizations struggle with effective implementation due to confusing documentation (41.3%), limited access to tools (40.4%), and reliance on institutional knowledge (39.7%). Over one-third of organizations report that only select team members have a comprehensive understanding of the defined incident management process.

Organizations also encounter hurdles in implementing automation within their incident management processes. Only 11-25% of incident management tasks or workflows are automated for one-third of respondents, highlighting opportunities for more extensive automation adoption.

To strengthen their incident management capabilities, organizations plan to enhance their tech stack over the next 12 months by implementing new tools such as site reliability engineering practices and platform engineering efforts. These strategic moves underscore organizations’ commitment to fortifying incident management and reducing mean time to resolution/repair (MTTR).

Findings from the study suggest that integrating generative AI capabilities into incident management tools or platforms is crucial for decreasing the time it takes to create new automations, allowing more time for high-value work. Additionally, there is a growing consensus that automation should empower humans to use their judgment at critical decision points, enhancing reliability and effectiveness.

Transposit’s research underscores the need for adaptive, machine learning-based automation solutions in incident management. By leveraging automation and AI while keeping humans actively engaged in the process, organizations can achieve seamless incident resolution and reduce MTTR. This approach enables teams to focus on delivering efficient solutions to complex problems, ultimately driving operational excellence.

In conclusion, organizations face intricate challenges in managing incidents effectively despite having defined processes and automation in place. The incorporation of generative AI holds immense potential for streamlining incident management processes and improving overall efficiency. Through strategic enhancements to their tech stack and embracing automation, organizations can strengthen their incident management capabilities and drive operational excellence in today’s data-driven landscape.

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