I used to work at IPsoft as well. I think the big difference is between "ML as a tool to solve a 80%+ of relatively standard customer and employee service requests" vs "we've created self-reasoning AI that will solve all your problems".
In my opinion, sales at IPsoft faced some of the same challenges initially (selling the idea of AI vs selling a well working solution for a specific problem), but has become more focused now that they have proven use cases in the field.
As you say, service desks are a great opportunity for ML. They're seen as cost centers, typically plagued by high attrition of employees, provide inconsistent service levels, and spend the majority of their time on requests that are relatively standard. Still, it is not an automatic effort: integrating with back-end systems, creating a dataset to start classifying incoming requests, and defining the processes to handle them are (mostly) still manual work.
In my opinion, sales at IPsoft faced some of the same challenges initially (selling the idea of AI vs selling a well working solution for a specific problem), but has become more focused now that they have proven use cases in the field.
As you say, service desks are a great opportunity for ML. They're seen as cost centers, typically plagued by high attrition of employees, provide inconsistent service levels, and spend the majority of their time on requests that are relatively standard. Still, it is not an automatic effort: integrating with back-end systems, creating a dataset to start classifying incoming requests, and defining the processes to handle them are (mostly) still manual work.