A notable example of disruptive innovation is the open-source database software. In its early days, it was only used by hobbyist developers or small companies who couldn’t afford a proper enterprise-grade database from Oracle or IBM, often sold for 6-7 digits figures to large corporates. As the technology improved, more and more of the larger companies began to adopt open-source databases, at a significantly lower cost than the enterprise-grade software. Now, anybody can spin off a new database server (on-premise or cloud) at no or very little cost.
So what does this have anything to do with AI and customer support? In the past, AI technology
was only accessible to large companies with big budgets who could afford to hire expensive machine learning experts and had access to powerful compute capacity. That changed a lot in the last few years, mostly thanks to open-source software but also to cloud service providers like Amazon or Google. It is very easy now to use cloud services such as Google’s Dialogflow
to create natural language models which could accurately and automatically categorize any piece of text at the fraction of a cent each.
Unfortunately in customer support, AI is often considered a high-end tech, available only to teams with large budgets. The costs, in the range of thousands of dollars per month, are prohibitive to smaller or medium-size companies.
The affordable AI exists though and is already widely available, as mentioned in the example above. In my view, this is an integration problem. That is why I decided to write a step-by-step tutorial on how to implement your own ticket deflection project at the fraction of the cost of using existing customer service AI vendors.
I hope it’s useful.