{"id":33832,"date":"2023-11-07T06:00:00","date_gmt":"2023-11-07T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33832"},"modified":"2023-11-06T10:30:22","modified_gmt":"2023-11-06T18:30:22","slug":"new-study-reveals-organizations-embrace-generative-ai-and-human-in-the-loop-automation-amidst-rising-costs-and-frequency-of-service-incidents","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/11\/07\/new-study-reveals-organizations-embrace-generative-ai-and-human-in-the-loop-automation-amidst-rising-costs-and-frequency-of-service-incidents\/","title":{"rendered":"New Study Reveals Organizations Embrace Generative AI and Human-in-the-Loop Automation Amidst Rising Costs and Frequency of Service Incidents"},"content":{"rendered":"\n

Transposit<\/a>, the AI-powered incident management company, today announced results from its third annual State of DevOps Automation and AI research study about the intricate challenges faced by organizations in managing incidents effectively. Findings uncovered an incident management paradox: despite a majority of respondents (59.4%) who have a defined incident management process in place and a level of automation that meets their needs (71.1%), organizations grapple with a surge in service incidents and still struggle to quickly resolve them. Nearly two-thirds of organizations (66.5%) reported an increase in the frequency of service incidents that have affected their customers over the past 12 months, a 3.6% increase from the 2022 survey. These downtime-producing incidents (i.e., application outages, service degradation) are putting organizations at risk of losing up to $499,999 per hour on average, according to 63% of respondents \u2013 a nearly 5% increase from 2022. Almost half (46.6%) also said downtime can cost anywhere from $100K to $2M. Research points to generative AI as a means to resolve the incident management paradox with 84.5% who either believe AI can significantly streamline their incident management processes and improve overall efficiency or are excited about the opportunities AI presents for automating certain aspects of incident management. Transposit surveyed more than 1,000 U.S.-based IT Operations, DevOps, site reliability engineering (SRE), and platform engineering professionals with the role of VP, Director, Manager, and engineer.<\/p>\n\n\n\n

\u201cThe insights unearthed in our research underscore the pressing need for adaptive, LLM-based automation that transcends mere task repetition and, instead, dynamically adapts to evolving circumstances by assimilating cues and context in real-time,\u201d said Divanny Lamas, CEO of Transposit. \u201cTraditional, rule-based automation tools are no longer sufficient for the demands of modern operations teams. Despite robust incident management processes within numerous organizations, the relentless surge in service incidents \u2014 with its consequential impact on customers and financial ramifications \u2014 mandates a transformative approach. The path forward lies in harnessing innovative solutions like generative AI, augmented by automation and guided by human judgment, to not only expedite incident resolution but also proactively detect and preempt potential issues before they escalate.\u201d<\/em><\/p>\n\n\n\n

Time Lags and Knowledge Gaps Lead to Inefficient Incident Management <\/strong><\/p>\n\n\n\n

In the realm of incident management, reliability engineering teams face significant hurdles. Nearly three-quarters (73.9%) of those responsible for reliability engineering experience challenges while trying to solve incidents including brittle automation scripts (59.7%), too many manual processes (47.8%), and difficulty accessing specialized knowledge (47.2%). Moreover, more than four in 10 (42.5%) organizations said their current incident management process is not effective or is only being used by some team members due to confusing documentation (41.3%), limited access to tools (40.4%), and reliance on institutional knowledge (39.7%).<\/p>\n\n\n\n

61.5% of organizations also cited an increase in the amount of time it takes to resolve incidents over the course of the last year, with nearly eight in 10 respondents (79.8%) saying it takes up to six hours on average to resolve incidents from the first alert to mitigating the issue. Beyond the extended incident resolution time, there’s an added layer of complexity in assembling the right team members, as indicated by 71.3% who reported this process can take up to 30 minutes. Adding to this, a significant portion of team members find it challenging to grasp and routinely apply the organization’s defined procedures. Over one-third of organizations (37.4%) report that only select team members have a comprehensive understanding of the defined incident management process and adhere to it consistently.<\/p>\n\n\n\n

Automation Hurdles Add to Service Incident Complexity<\/strong><\/p>\n\n\n\n

Organizations grapple not only with inefficiencies in incident resolution but also encounter hurdles in implementing automation. One-third of respondents (33.3%) cited only 11-25% of their incident management tasks or workflows are automated, showcasing an opportunity for more automation in organizations’ incident management processes. Delving deeper, respondents expressed keen interest in automating pivotal aspects of the incident lifecycle, such as incident setup (50.0%), communication protocols (44.2%), investigative processes (30%), and remediation (29%). <\/p>\n\n\n\n

Despite the interest in implementing automation, respondents cited these top four barriers to achieving it:<\/p>\n\n\n\n