Cisco wanted to scale its digital assist engineer that assists its technical assist groups world wide. By leveraging its personal Splunk know-how, Cisco was in a position to scale the AI assistant to assist greater than 1M circumstances and release engineers to focus on extra advanced circumstances, making a 93+% buyer satisfaction score, and guaranteeing the crucial assist continues operating within the face of any disruption.
For those who’ve ever opened a assist case with Cisco, it’s doubtless that the Technical Help Middle (TAC) got here to your rescue. This around-the-clock, award-winning technical assist staff providers on-line and over-the-phone assist to all of Cisco’s prospects, companions, and distributors. In reality, it handles 1.5 million circumstances world wide yearly.
Fast, correct, and constant assist is crucial to guaranteeing the shopper satisfaction that helps us keep our excessive requirements and develop our enterprise. Nevertheless, major occasions like crucial vulnerabilities or outages can trigger spikes within the quantity of circumstances that slow response instances and shortly swamp our TAC groups, affecting buyer satisfaction consequently. we’ll dive into the AI-powered assist assistant that assists to ease this difficulty, in addition to how we used our personal Splunk know-how to scale its caseload and improve our digital resilience.
Constructing an AI Assistant for Assist
staff of elite TAC engineers with a ardour for innovation set out to construct an answer that would speed up difficulty decision instances by increaseing an engineers’ means to detect and remedy buyer issues. the was created — it’s greater than an AI bot and fewer than a human, designed to work alongside the human engineer.
Fig. 1: All circumstances are analyzed and directed to the AI Assistant for Assist or the human engineer primarily based on which is most applicable for decision.
By immediately plugging into the case routing system to investigate each case that is available in, the AI Assistant for Assist evaluates which of them it might simply assist remedy, together with license transactions and procedural issues, and responds on to prospects of their most well-liked language.
With such nice success, we set our eyes on much more assist for our engineers and prospects. Whereas the AI Assistant for Assist was initially conceived to assist with the high-volume occasions that create a big inflow of circumstances, it shortly expanded to incorporate extra day-to-day buyer points, serving to to cut back response instances and imply time to decision whereas constantly sustaining a 93+% buyer satisfaction rating.
Nevertheless, as the usage of the AI Assistant grew, so did the complexity and quantity of circumstances it dealt with. An answer that after dealt with 10-12 circumstances a day shortly ballooned into lots of, outgrowing the methodology initially in place for monitoring workflows and sifting by way of log knowledge.
Initially, we created a strategy referred to as “breadcrumbs” that we tracked by way of a WebEx house. These “breadcrumbs,” or actions taken by the AI Assistant for Assist throughout a case from finish to finish, have been dropped into the house so we may manually return by way of the workflows to troubleshoot. When our assistant was solely taking a small quantity circumstances a day, this was all we wanted.
The issue was it couldn’t scale. Because the assistant started taking up lots of of circumstances a day, we outgrew the size at which our “breadcrumbs” methodology was efficient, and it was not possible for us to handle as people.
Figuring out the place, when, and why one thing went flawed had grow to be a time-consuming problem for the groups working the assistant. We shortly realized we wanted to:
- Implement a brand new methodology that would scale with our operations
- Discover a resolution that would offer traceability and guarantee compliance
Scaling the AI Assistant for Assist with Splunk
We determined to construct out a logging methodology utilizing Splunk, the place we may drop log messages into the platform and construct a dashboard with case quantity as an index. As an alternative of manually sifting by way of our “breadcrumbs,” we may instantaneously find the circumstances and workflows we wanted to hint the actions taken by the assistant. The troubleshooting that may have taken us hours with our authentic methodology may very well be completed in seconds with Splunk.
The Splunk platform affords a sturdy and scalable resolution for monitoring and logging that permits the capabilities required for extra environment friendly knowledge administration and troubleshooting. Its means to ingest massive volumes of knowledge at excessive charges was essential for our operations. As an trade chief in case search indexing and knowledge ingestion, Splunk may simply handle the elevated knowledge movement and operational calls for that our earlier methodology couldn’t.
Tangible advantages of Splunk
Splunk unlocked a degree of resiliency for our AI Assistant for Assist that positively impacted our engineers, prospects, and enterprise.
Fig. 2: The Splunk dashboard affords clear visibility into capabilities to make sure optimized efficiency and stability.
With Splunk, we now have:
- Scalability and effectivity: Splunk screens the assistant’s actions to make sure it’s working accurately and supplies the power for TAC engineers to observe and troubleshoot workflows, permitting the assistant to effectively scale. The AI Assistant for Assist has efficiently labored on over a million circumstances to this point.
- Enhanced visibility: With dashboards that enable for fast entry to case histories and workflow logs of our assistant, the TAC engineers overseeing the processes save time on case opinions to ship quicker than ever buyer assist.
- Optimized processes with real-time metrics: The visibility into useful resource allocation permits us to optimize our enterprise processes and workflows, in addition to exhibit the worth of our resolution with real-time metrics.
- Proactive monitoring: Splunk ensures all APIs are absolutely functioning and screens logs to alert us of potential points that would affect our AI Assistant’s means to function, permitting for fast remediation earlier than buyer expertise is impacted.
- Larger worker and buyer satisfaction: Engineers are geared up to deal with increased caseloads and effectively reprioritize efforts, lowering burnout whereas optimizing buyer expertise.
- Lowered complexity: The dashboards have a easy interface, making it a lot simpler to coach and onboard new staff. The convenience of use additionally serves to enhance the capabilities of the people working our AI Assistant by enhancing their accuracy and effectivity.
By offering a scalable and traceable resolution that helps us keep compliant, Splunk has enabled us to take care of our dedication to distinctive customer support by way of our AI Assistant for Assist.
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