AI meets technical support: 3 methods to know
Having the right analytics to assess your big data is key
By Militza Bishop | 2 minute read | January 17, 2020
If you’re considering integrating artificial intelligence into your IT support infrastructure, you’re not alone. Perhaps your Net Promoter Score (NPS) for technical support delivery is low. Or your support agents are using a myriad of old tools, which doesn’t translate to a fast, digital support experience.
New research by Morning Consult for IBM says three out of four companies surveyed are exploring or implementing AI, indicating adoption progress. If implemented correctly, the business payoffs include improved efficiency and customer experience.
Transition to a digital infrastructure
Lay the foundation for integrating AI with a digital infrastructure that supports technologies such as AI chatbots, predictive analytics, automation and time optimization to streamline IT support management. If needed, a service provider with AI expertise can help you assess big data through advanced analytics, another key component before you invest in AI.
Consider these use cases and methods of applying AI to technical support to enhance customer satisfaction.
1. Provide an AI-powered chatbot
Often, a customer enters a support request with an incomplete problem description. This triggers a service delay. An AI chatbot interface helps a customer write a better case by researching historical case data and suggesting descriptions. The support agent receives the relevant information they need to resolve the case faster. For more straightforward cases, a self-service chatbot enables customers to use self-service to resolve their support issues.
“By training AI to understand what a complete question looks like in context, we can prompt customers to provide the most relevant information,” said Calvin Xu, Ph.D., a leader on IBM Services’ innovation team.
Predictive analytics and automation are key to customers getting quick responses. “The second the customer accesses support, AI discovery technologies are used to predict questions and present answers for root cause analysis and search optimization. Automation enables instant responses for repeated questions,” Xu said.
2. Use AI for ticket routing
Once an IT support request is submitted, the problem needs to be assigned to a support engineer to resolve it. This is where an AI routing and assignment solution is valuable. The system targets the most efficient problem-handling experience for the customer. It uses massive amounts of analysis to examine workforce skills and case content to match the most appropriate engineer to the case problem.
“With AI chatbots, our IBM Services innovation team is able to improve our first time fix rate, turnaround time and repair rates,” said Xu.
3. Apply a case prioritization solution
Highly skilled agents frequently have complex or lengthy cases and need to prioritize backlogs and caseloads, all while keeping the customer support experience in mind. A case prioritization solution applies AI priority scoring to all cases assigned to an engineer and indicates which case they should work on next, allowing the engineer more time to focus on the resolution.
“When you handle a massive volume of support cases, optimizing efficiency is critical. Customers do not want to wait for effective support, regardless of how complex the scenario may be,” said Xu. “Optimizing the customer’s time and getting the correct support personnel involved immediately leads to a positive client experience.”
AI technology is trained to work through the backlog of cases and present actionable insights to the support agent. The long-term benefit and goal are to help raise NPS scores and lower the risk of missed SLAs.
For more information, reach out to Calvin Xu.