Cisco wanted to scale its digital assist engineer that assists its technical assist groups around the globe. By leveraging its personal Splunk expertise, Cisco was capable of scale the AI assistant to assist greater than 1M instances and unlock engineers to focus on extra advanced instances, making a 93+% buyer satisfaction score, and guaranteeing the vital assist continues operating within the face of any disruption.
In the event you’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 group companies on-line and over-the-phone assist to all of Cisco’s prospects, companions, and distributors. The truth is, it handles 1.5 million instances around the globe 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 instances 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
group of elite TAC engineers with a ardour for innovation set out to construct an answer that might speed up problem decision instances by increaseing an engineers’ capacity 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 instances are analyzed and directed to the AI Assistant for Assist or the human engineer based mostly on which is most acceptable for decision.
By immediately plugging into the case routing system to research each case that is available in, the AI Assistant for Assist evaluates which of them it may simply assist clear up, 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 assist for our engineers and prospects. Whereas the AI Assistant for Assist was initially conceived to assist with the high-volume occasions that create a major inflow of instances, it shortly 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 the usage of 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 shortly ballooned into lots of, outgrowing the methodology initially in place for monitoring workflows and sifting via log knowledge.
Initially, we created a technique often known as “breadcrumbs” that we tracked via a WebEx area. These “breadcrumbs,” or actions taken by the AI Assistant for Assist throughout a case from finish to finish, have been dropped into the area so we might manually return via 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 up lots of of instances 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 flawed 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 resolution that would offer 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 via our “breadcrumbs,” we might instantaneously find the instances and workflows we wanted to hint the actions taken by the assistant. The troubleshooting that will have taken us hours with our unique methodology could possibly be achieved in seconds with Splunk.
The Splunk platform affords a strong and scalable resolution for monitoring and logging that allows the capabilities required for extra environment friendly knowledge administration and troubleshooting. Its capacity to ingest giant 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 movement 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 Assist that positively impacted our engineers, prospects, and enterprise.
Fig. 2: The Splunk dashboard affords clear visibility into capabilities 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 appropriately and supplies 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 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 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 absolutely functioning and displays logs to alert us of potential points that might impression our AI Assistant’s capacity to function, permitting for fast remediation earlier than buyer expertise is impacted.
- Increased worker and buyer satisfaction: Engineers are outfitted to deal with larger 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 staff. 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 via our AI Assistant for Assist.
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