Accenture research based on National Statistical Institutes and O*Net indicates that in the High-Tech industry 19% of working hours can be augmented by generative AI and 23% is in scope for automation with GenAI.

GenAI is changing IT operations service assurance processes and workflows. Generative AI can derive real, in-context value from vast stores of unstructured content, which until now may have gone largely unexploited. This will change and accelerate workflows. By synthesizing data, comprehending natural language both voice and text, and converting unstructured data into intelligence that delivers actionable insights, IT service assurance processes can be transformed to deliver much lower cost and higher quality service performance. Generative AI can provide both high probability insights for “likely fix” recommendations and accelerate progress towards fix automation. GenAI supports IT operations in working more efficiently and effectively and increasing the level of impact of their work.

The IT skills gap and the aging and retirement of many IT operations SMEs continues to be a growing industry concern. A global analysis by Korn Ferry estimates the digital skills gap will leave 4.3 million tech jobs unfilled by 2030. As IT SMEs retire, GenAI will help to fill the IT talent shortage.

Complex issues often take hours or days to determine the solution and resolve. Quick fixes may only resolve performance issues for a short period before the problem resurfaces. Determining the right fix may take hours and days and most often consist of manual, labor intensive analysis and pouring over sometimes vast amounts of unstructured data.

When symptoms of an underlying service issue occur across the service ecosystem that exhibit fault signals and performance issues across the network, applications, services and client infrastructure, determining the underlying problem and the right fix is exacerbated.

A North American Tier One MSO outsourced much of the manual analysis required to determine a recommended fix for complex situations. To improve resolution time and reduce service assurance cost, across service layers, they implemented Vitria VIA AIOps. GenAI embedded within the VIA AIOps solution recognizes patterns in data that may not be immediately apparent to humans. GenAI models are trained on incident tickets, war room chats/transcripts, and knowledge bases to determine likely fixes for incidents. These models are continuously learning and improving over time. Trouble shooting and remediation automation are both realizable with GenAI.

Soon after implementation of Vitria VIA AIOps, GenAI enabled quick analysis of large datasets and provided recommendations in real time. Outsourcing for complex analysis is planned to be discontinued within six months. Benefits received with the implementation of VIA AIOps included:

• Reduction of OPEX associated with costly manual analysis
• Accelerated network incident resolution and resulted in improving overall uptime and network performance
• Codification of SMEs knowledge that are nearing retirement age
• Planned automation for 90% of the incident resolution process