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 instances and liberate engineers to focus on extra complicated instances, making a 93+% buyer satisfaction score, and guaranteeing the essential help continues working within the face of any disruption.
When you’ve ever opened a help case with Cisco, it’s doubtless that the Technical Help Heart (TAC) got here to your rescue. This around-the-clock, award-winning technical help group companies on-line and over-the-phone help to all of Cisco’s clients, companions, and distributors. In reality, it handles 1.5 million instances all over the world yearly.
Fast, correct, and constant help is essential to guaranteeing the shopper satisfaction that helps us keep our excessive requirements and develop our enterprise. Nonetheless, major occasions like essential vulnerabilities or outages can trigger spikes within the quantity of instances that slow response instances and rapidly swamp our TAC groups, influenceing buyer satisfaction because of this. we’ll dive into the AI-powered help assistant that assists to ease this subject, in addition to how we used our personal Splunk expertise to scale its caseload and enhance our digital resilience.
Constructing an AI Assistant for Help
group of elite TAC engineers with a ardour for innovation set out to construct an answer that might speed up subject decision instances by increaseing an engineers’ capacity 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 instances are analyzed and directed to the AI Assistant for Help or the human engineer based mostly on which is most applicable for decision.
By instantly plugging into the case routing system to research each case that is available in, the AI Assistant for Help evaluates which of them it could possibly simply assist resolve, together with license transactions and procedural issues, and responds on to clients of their most well-liked language.
With such nice success, we set our eyes on much more help for our engineers and clients. Whereas the AI Assistant for Help was initially conceived to assist with the high-volume occasions that create a big inflow of instances, 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 using the AI Assistant grew, so did the complexity and quantity of instances it dealt with. An answer that when dealt with 10-12 instances a day rapidly ballooned into lots of, outgrowing the methodology initially in place for monitoring workflows and sifting by log information.
Initially, we created a strategy often known as “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 may manually return by the workflows to troubleshoot. When our assistant was solely taking a small quantity instances a day, this was all we wanted.
The issue was it couldn’t scale. Because the assistant started taking over lots of of instances a day, we outgrew the size 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 improper had turn out to be a time-consuming problem for the groups working the assistant. We rapidly realized we wanted to:
- Implement a brand new methodology that might scale with our operations
- Discover a resolution 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 may drop log messages into the platform and construct a dashboard with case quantity as an index. As a substitute of manually sifting by our “breadcrumbs,” we may instantaneously find the instances 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 resolution for monitoring and logging that allows the capabilities required for extra environment friendly information administration and troubleshooting. Its capacity to ingest massive volumes of knowledge at excessive charges was essential for our operations. As an business chief in case search indexing and information ingestion, Splunk may simply handle the elevated information 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, clients, 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 accurately and offers 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 instances up to now.
- 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 evaluations 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 reveal 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 influence our AI Assistant’s capacity to function, permitting for fast remediation earlier than buyer expertise is impacted.
- Greater worker and buyer satisfaction: Engineers are outfitted to deal with larger 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 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 our AI Assistant for Help.
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