Cisco wanted to scale its digital assist engineer that assists its technical assist groups around the globe. By leveraging its personal Splunk know-how, Cisco was in a position to scale the AI assistant to assist greater than 1M instances and unencumber engineers to focus on extra advanced instances, making a 93+% buyer satisfaction ranking, and making certain the important assist continues operating within the face of any disruption.
For those who’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 group companies on-line and over-the-phone assist to all of Cisco’s clients, companions, and distributors. In reality, it handles 1.5 million instances around the globe yearly.
Fast, correct, and constant assist is important to making certain the client satisfaction that helps us preserve our excessive requirements and develop our enterprise. Nevertheless, major occasions like important vulnerabilities or outages can trigger spikes within the quantity of instances that slow response instances and shortly swamp our TAC groups, influenceing buyer satisfaction consequently. we’ll dive into the AI-powered assist assistant that assists to ease this difficulty, in addition to how we used our personal Splunk know-how 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 would speed up difficulty decision instances by increaseing an engineers’ capability to detect and remedy 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 primarily based on which is most applicable 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 remedy, 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 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 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 constantly sustaining a 93+% buyer satisfaction rating.
Nevertheless, as the usage of the AI Assistant grew, so did the complexity and quantity of instances it dealt with. An answer that after dealt with 10-12 instances a day shortly ballooned into a whole bunch, outgrowing the methodology initially in place for monitoring workflows and sifting by log information.
Initially, we created a strategy often called “breadcrumbs” that we tracked by a WebEx house. These “breadcrumbs,” or actions taken by the AI Assistant for Assist throughout a case from finish to finish, had 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 would have liked.
The issue was it couldn’t scale. Because the assistant started taking over a whole bunch of instances a day, we outgrew the dimensions 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 incorrect had turn into 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 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 may 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 may instantaneously find the instances and workflows we would have liked to hint the actions taken by the assistant. The troubleshooting that may have taken us hours with our authentic methodology might be completed in seconds with Splunk.
The Splunk platform presents a sturdy and scalable resolution for monitoring and logging that allows the capabilities required for extra environment friendly information administration and troubleshooting. Its capability to ingest giant volumes of knowledge at excessive charges was essential for our operations. As an trade chief in case search indexing and information ingestion, Splunk may simply handle the elevated information 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, clients, 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 displays the assistant’s actions to make sure it’s working accurately and gives the power for TAC engineers to observe and troubleshoot workflows, permitting the assistant to effectively scale. The AI Assistant for Assist has efficiently labored on over a million instances to this point.
- 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 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 reveal the worth of our resolution with real-time metrics.
- Proactive monitoring: Splunk ensures all APIs are totally functioning and displays logs to alert us of potential points that would influence 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 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 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 keep up our dedication to distinctive customer support by our AI Assistant for Assist.
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