Cisco wanted to scale its digital assist engineer that assists its technical assist groups all over the world. By leveraging its personal Splunk know-how, Cisco was in a position to scale the AI assistant to assist greater than 1M circumstances and liberate engineers to focus on extra advanced circumstances, making a 93+% buyer satisfaction score, and making certain the important assist continues working within the face of any disruption.
In case you’ve ever opened a assist case with Cisco, it’s probably that the Technical Help Heart (TAC) got here to your rescue. This around-the-clock, award-winning technical assist workforce 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 all over the world yearly.
Fast, correct, and constant assist is important to making certain the client satisfaction that helps us keep our excessive requirements and develop our enterprise. Nevertheless, major occasions like important vulnerabilities or outages can trigger spikes within the quantity of circumstances that slow response instances and shortly swamp our TAC groups, impressioning buyer satisfaction in consequence. we’ll dive into the AI-powered assist assistant that assists to ease this concern, 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 Help
workforce of elite TAC engineers with a ardour for innovation set out to construct an answer that might speed up concern decision instances by increaseing an engineers’ capability to detect and clear up 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 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 clear up, 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 big inflow of circumstances, it shortly expanded to incorporate extra day-to-day buyer points, serving to to scale back response instances and imply time to decision whereas constantly sustaining a 93+% buyer satisfaction rating.
Nevertheless, as using 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 tons of, outgrowing the methodology initially in place for monitoring workflows and sifting by log knowledge.
Initially, we created a strategy referred to as “breadcrumbs” that we tracked by 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 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 over tons 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 improper had change into a time-consuming problem for the groups working the assistant. We shortly realized we wanted to:
- Implement a brand new methodology that might scale with our operations
- Discover a answer 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 our “breadcrumbs,” we might 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 unique methodology might be achieved in seconds with Splunk.
The Splunk platform gives a sturdy and scalable answer for monitoring and logging that allows 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 degree of resiliency for our AI Assistant for Help that positively impacted our engineers, prospects, and enterprise.
Fig. 2: The Splunk dashboard gives 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 appropriately and supplies the flexibility 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 enable for fast entry to case histories and workflow logs of our assistant, the TAC engineers overseeing the processes save time on case evaluations 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 exhibit the worth of our answer with real-time metrics.
- Proactive monitoring: Splunk ensures all APIs are totally functioning and screens logs to alert us of potential points that might impression 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 increased caseloads and effectively reprioritize efforts, decreasing burnout whereas optimizing buyer expertise.
- Decreased complexity: The dashboards have a easy interface, making it a lot simpler to coach and onboard new workers. 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 answer that helps us keep compliant, Splunk has enabled us to take care of our dedication to distinctive customer support by our AI Assistant for Help.
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