I’ve been thinking a lot about the most practical and pragmatic uses of Generative AI for Customer Success. Some of the most readily available use cases for Generative AI are for people that work in the following areas:
· Customer Experience
· HR and Talent
· Product Development
This list is, of course, not limited.
Let’s put Customer Success in Customer Experience given adjacencies in outcomes that organizations what to achieve with clients including retention, growth, and advocacy.
IBM Institute for Business Value’s CEO Guide to Generative AI found Customer Service leapfrogged other functions to become CEOs’ #1 priority for Generative AI. CEOs feel demands from customers to accelerate the use of this new technology. They want personalized answers, fast – without always needing a human to intervene.
The most common Generative AI tasks implemented today are in the areas of Summarization, Classification, Generation, Extraction, and Q&A. These tasks are super relevant to CSMs. Here’s how I see it....
Summarization: Generative AI can summarize lengthy client meetings for CSMs, CS Managers, and CS Execs from something like an EBR.
Classification: Generative AI can classify risk profiles of clients taking many features such as support tickets, external news like M&A or quarterly earnings, client satisfaction surveys and more.
Generation: Generative AI can generate new and interesting content for CSMs. Imagine Generative AI generating QBR material by pulling insights from Gainsight and Salesforce. This will save CSMs a ton of time!
Extraction: Generative AI can extract insights from growth plans for next best actions.
Q&A: Generative AI can create product specific Q&A for CSMs, clients, and partners.
This list is not extensive by any means.
We can’t talk about Generative AI without considering the risks. Explainability, ethics, bias, and trust needs to be part of the conversation you are having with your IT partners. Here are some questions for you to consider:
How were the models trained?
Can the platform detect and minimize bias and hallucinations?
Are the models transparent? (Open vs Black Box)
How do you/they audit and explain the models and the answers it generates?
How do foundation models and their usage comply with privacy and government regulations?
Is it safe? Who controls the models, input data, and output data?
Can the models and platform be customized?
I’m excited about the possibilities Generative AI poses for CSMs, clients, and partners, but at the same time trust needs to be at the forefront for all of us. I’d love to hear about some of the Generative AI use cases that you are exploring as you scale Customer Success.
Continued Success,
Janine
A good idea would be o develop a Webex WatsonX plugin that enables us CSMs to do that on the meetings we have with customers and have this logged automatically in Gainsight
Chorus AI does an impressive job of summarizing meetings. Way better than Gong was when we were using it.
Another way we have used chatGPT so far is for summarizing account businesses. I give it a prompt like “tell me about company X. How do they make money, what markets are they in and what are their strategic anchors?”
Then we populate our account records with the output. This info is very handy when you are new to an account.
The next use case I want to try is ingesting previous meetings and analyzing the common topics, challenges and wins across the many meetings we hold with customers. Maybe even some health score analysis. The possibilities are endless!