Cisco wanted to scale its digital help engineer that assists its technical help groups all over the world. By leveraging its personal Splunk expertise, Cisco was capable of scale the AI assistant to help greater than 1M circumstances and unencumber engineers to focus on extra complicated circumstances, making a 93+% buyer satisfaction score, and making certain the vital help continues working within the face of any disruption.
In case you’ve ever opened a help case with Cisco, it’s doubtless that the Technical Help Middle (TAC) got here to your rescue. This around-the-clock, award-winning technical help workforce providers on-line and over-the-phone help to all of Cisco’s prospects, companions, and distributors. In truth, it handles 1.5 million circumstances all over the world yearly.
Fast, correct, and constant help is vital to making certain the client satisfaction that helps us preserve our excessive requirements and develop our enterprise. Nonetheless, major occasions like vital vulnerabilities or outages can trigger spikes within the quantity of circumstances that slow response instances and rapidly swamp our TAC groups, affecting buyer satisfaction because of this. we’ll dive into the AI-powered help assistant that assists to ease this problem, in addition to how we used our personal Splunk expertise to scale its caseload and improve our digital resilience.
Constructing an AI Assistant for Help
workforce of elite TAC engineers with a ardour for innovation set out to construct an answer that would speed up problem decision instances by increaseing an engineers’ means to detect and resolve 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 Help or the human engineer based mostly on which is most applicable for decision.
By straight plugging into the case routing system to investigate each case that is available in, the AI Assistant for Help evaluates which of them it might probably simply assist resolve, together with license transactions and procedural issues, and responds on to prospects of their most popular language.
With such nice success, we set our eyes on much more help for our engineers and prospects. Whereas the AI Assistant for Help was initially conceived to assist with the high-volume occasions that create a major inflow of circumstances, it rapidly expanded to incorporate extra day-to-day buyer points, serving to to scale back response instances and imply time to decision whereas persistently sustaining a 93+% buyer satisfaction rating.
Nonetheless, as using the AI Assistant grew, so did the complexity and quantity of circumstances it dealt with. An answer that when dealt with 10-12 circumstances a day rapidly ballooned into a whole lot, outgrowing the methodology initially in place for monitoring workflows and sifting by log information.
Initially, we created a technique often called “breadcrumbs” that we tracked by a WebEx house. These “breadcrumbs,” or actions taken by the AI Assistant for Help throughout a case from finish to finish, have been dropped into the house so we might manually return by the workflows to troubleshoot. When our assistant was solely taking a small quantity circumstances a day, this was all we would have liked.
The issue was it couldn’t scale. Because the assistant started taking over a whole lot of circumstances a day, we outgrew the dimensions at which our “breadcrumbs” technique was efficient, and it was not possible for us to handle as people.
Figuring out the place, when, and why one thing went unsuitable had change into a time-consuming problem for the groups working the assistant. We rapidly realized we would have liked to:
- Implement a brand new methodology that would scale with our operations
- Discover a answer that would offer traceability and guarantee compliance
Scaling the AI Assistant for Help with Splunk
We determined to construct out a logging methodology utilizing Splunk, the place we might drop log messages into the platform and construct a dashboard with case quantity as an index. As an alternative of manually sifting by our “breadcrumbs,” we might instantaneously find the circumstances and workflows we would have liked to hint the actions taken by the assistant. The troubleshooting that might have taken us hours with our unique methodology may very well be completed in seconds with Splunk.
The Splunk platform presents a strong and scalable answer for monitoring and logging that allows the capabilities required for extra environment friendly information administration and troubleshooting. Its means to ingest giant volumes of knowledge at excessive charges was essential for our operations. As an business chief in case search indexing and information ingestion, Splunk might simply handle the elevated information move 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 Help that positively impacted our engineers, prospects, and enterprise.
Fig. 2: The Splunk dashboard presents clear visibility into features to make sure optimized efficiency and stability.
With Splunk, we now have:
- Scalability and effectivity: Splunk displays the assistant’s actions to make sure it’s working accurately and offers the power for TAC engineers to watch and troubleshoot workflows, permitting the assistant to effectively scale. The AI Assistant for Help has efficiently labored on over a million circumstances so far.
- Enhanced visibility: With dashboards that permit for fast entry to case histories and workflow logs of our assistant, the TAC engineers overseeing the processes save time on case critiques to ship quicker than ever buyer help.
- Optimized processes with real-time metrics: The visibility into useful resource allocation permits us to optimize our enterprise processes and workflows, in addition to display the worth of our answer with real-time metrics.
- Proactive monitoring: Splunk ensures all APIs are totally functioning and displays 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.
- Greater worker and buyer satisfaction: Engineers are geared up to deal with increased caseloads and effectively reprioritize efforts, decreasing burnout whereas optimizing buyer expertise.
- Lowered complexity: The dashboards have a easy interface, making it a lot simpler to coach and onboard new workers. 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 answer that helps us keep compliant, Splunk has enabled us to keep up our dedication to distinctive customer support by our AI Assistant for Help.
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