This is an AArete rithmiqAI Insight
A large service organization was focused on reducing the costs for the services they provided and minimizing SLA penalties paid to their business units. The account was being served by ~1,500 FTEs and SLA penalties were costing ~$500k per month.
A Repetitive Work module was used to leverage natural language processing and clustering to sift through all operational data clusters.
The identification of repetitive work patterns resulted in an opportunity to automate the repetitiveness, finalize solutions to solve the root cause of recurring problems, and suppress meaningless infrastructure alerts. There was also:
• Improved allocation of resources to greater business needs
• 50% decrease in SLA violations