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 unlock engineers to focus on extra advanced circumstances, making a 93+% buyer satisfaction score, and making certain the essential assist continues operating within the face of any disruption.
If you happen to’ve ever opened a assist case with Cisco, it’s seemingly that the Technical Help Heart (TAC) got here to your rescue. This around-the-clock, award-winning technical assist crew companies on-line and over-the-phone assist to all of Cisco’s clients, companions, and distributors. In truth, it handles 1.5 million circumstances world wide yearly.
Fast, correct, and constant assist is essential to making certain the client 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 circumstances that slow response instances and shortly swamp our TAC groups, impressioning buyer satisfaction because of this. we’ll dive into the AI-powered assist assistant that assists to ease this problem, in addition to how we used our personal Splunk expertise to scale its caseload and enhance our digital resilience.
Constructing an AI Assistant for Assist
crew 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’ 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 Assist or the human engineer based mostly on which is most acceptable for decision.
By straight plugging into the case routing system to investigate each case that is available in, the AI Assistant for Assist evaluates which of them it could simply assist clear up, together with license transactions and procedural issues, and responds on to clients of their most popular language.
With such nice success, we set our eyes on much more assist for our engineers and clients. 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.
Nonetheless, as the usage of 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 shortly ballooned into a whole lot, outgrowing the methodology initially in place for monitoring workflows and sifting by means of log knowledge.
Initially, we created a strategy generally known as “breadcrumbs” that we tracked by means 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 might manually return by means 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 over a whole lot 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 mistaken had grow to be a time-consuming problem for the groups working the assistant. We shortly realized we would have liked to:
- Implement a brand new methodology that would scale with our operations
- Discover a resolution that would supply 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 might drop log messages into the platform and construct a dashboard with case quantity as an index. As an alternative of manually sifting by means 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 might have taken us hours with our unique 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 circulate 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, clients, and enterprise.
Fig. 2: The Splunk dashboard presents 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 offers the flexibility for TAC engineers to watch and troubleshoot workflows, permitting the assistant to effectively scale. The AI Assistant for Assist has efficiently labored on over a million circumstances up to now.
- 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 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 would impression our AI Assistant’s capability 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.
- Diminished 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 take care of our dedication to distinctive customer support by means of our AI Assistant for Assist.
Further Assets:
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