When it comes to insurance, there is no organization more reputable than Generali Italia — particularly in Italy. Founded in 1831, Generali has expanded its customer base to more than 8 million people, and currently serves one in three Italian families as well as one in four Italian businesses.
With insurance offerings including Cars and Mobility, Home and Leisure, Health and Family, as well as Savings and the Future, Generali has customers covered at every turn, and continues to grow worldwide.
There is nothing easy about managing a global operation, especially one that serves as many customers as Generali. With a constant flood of incoming trouble tickets and more than 100 different departments to disperse them to, maintaining an organized service flow had long been a struggle for the organization.
Despite an existing ticket sorting process with automation in place, Generali could not achieve the level of speed and accuracy they desired. Thus, the company sought an AI solution that could better understand and respond automatically to its agents’ requests for assistance. This, in turn, would not only streamline the process, but reduce internal costs.
To combat these issues, Generali turned to expert.ai to help build and maintain AI processes that could automate decision-making across the organization. Generali’s platform leverages several of expert.ai’s core technologies, using them as a foundation to better understand the syntax of extracted text (e.g., names related to their job role/position) and leverage that information to more accurately categorize the message.
As thousands of tickets enter Generali’s system, they are each analyzed to discern which category (or categories) they are to be sorted into. The ticket is then further analyzed by expert.ai technology to ensure accuracy and train the machine learning algorithm.
The automation of this system was not simply limited to the categorization and sorting of tickets though, but Generali’s response to them as well. Tickets the system deemed complex were routed directly to human agents, while all others were replied to via an automated message with resolution to the specific issue.
Generali’s AI initiative delivered an immediate, measurable impact following implementation. Rather than take one-to-two days to sort tickets, the system sorted tickets in a little as five minutes. This allowed Generali to analyze and sort more than 5,000 tickets every day, totaling more than one million tickets every year.
These improvements proved to Generali the enormous potential of automation and prompted them to launch a three-year program to scale automation through artificial intelligence and new technologies across all areas of the company.
This initiative proved that building an accurate automation solution is not simply about acquiring a data set and machine learning model. While both are essential to successful AI, they require experience, time, process redesign and knowledge of algorithms — all of which expert.ai can add to your arsenal.