Cisco wanted to scale its digital assist engineer that assists its technical assist groups world wide. By leveraging its personal Splunk expertise, Cisco was capable of scale the AI assistant to assist greater than 1M circumstances and unencumber engineers to focus on extra advanced circumstances, making a 93+% buyer satisfaction score, and guaranteeing the vital 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 workforce companies on-line and over-the-phone assist to all of Cisco’s prospects, companions, and distributors. In actual fact, it handles 1.5 million circumstances world wide yearly.
Fast, correct, and constant assist is vital to guaranteeing 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 in consequence. we’ll dive into the AI-powered assist assistant that assists to ease this challenge, 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 might speed up challenge decision instances by increaseing an engineers’ capability 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 acceptable for decision.
By immediately plugging into the case routing system to investigate each case that is available in, the AI Assistant for Help evaluates which of them it could simply assist resolve, 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 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 cut back response instances and imply time to decision whereas constantly sustaining a 93+% buyer satisfaction rating.
Nonetheless, 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 rapidly ballooned into a whole lot, outgrowing the methodology initially in place for monitoring workflows and sifting by way of log knowledge.
Initially, we created a technique referred to as “breadcrumbs” that we tracked by way of a WebEx area. These “breadcrumbs,” or actions taken by the AI Assistant for Help throughout a case from finish to finish, have been dropped into the area so we might 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 would have liked.
The issue was it couldn’t scale. Because the assistant started taking up a whole lot of circumstances a day, we outgrew the dimensions at which our “breadcrumbs” technique was efficient, and it was now not possible for us to handle as people.
Figuring out the place, when, and why one thing went fallacious had grow to be a time-consuming problem for the groups working the assistant. We rapidly realized we would have liked to:
- Implement a brand new methodology that might scale with our operations
- Discover a resolution that would supply 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 way of 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 will have taken us hours with our authentic methodology could possibly be achieved in seconds with Splunk.
The Splunk platform presents a sturdy and scalable resolution for monitoring and logging that permits the capabilities required for extra environment friendly knowledge administration and troubleshooting. Its capability to ingest massive volumes of information at excessive charges was essential for our operations. As an business chief in case search indexing and knowledge ingestion, Splunk might simply handle the elevated knowledge move and operational calls for that our earlier methodology couldn’t.
Tangible advantages of Splunk
Splunk unlocked a stage 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 screens the assistant’s actions to make sure it’s working appropriately 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 thus 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 opinions to ship sooner 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 display the worth of our resolution with real-time metrics.
- Proactive monitoring: Splunk ensures all APIs are totally functioning and screens logs to alert us of potential points that might affect our AI Assistant’s capability to function, permitting for fast remediation earlier than buyer expertise is impacted.
- Increased worker and buyer satisfaction: Engineers are geared up to deal with larger caseloads and effectively reprioritize efforts, lowering burnout whereas optimizing buyer expertise.
- Decreased complexity: The dashboards have a easy interface, making it a lot simpler to coach and onboard new staff. The benefit 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 keep up our dedication to distinctive customer support by way of our AI Assistant for Help.
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