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Reimagining Rev-Ops with Gen AI: Customer Successby@unnikrishnanroshin

Reimagining Rev-Ops with Gen AI: Customer Success

by Roshin UnnikrishnanOctober 7th, 2024
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Generative AI is rapidly reshaping customer success and adoption by enhancing personalization, enhancing human productivity through automation and support from generative agents, and enabling data-driven decision-making at scale in a variety of use cases and circumstances.
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This is the fourth and final part of a series of articles on how GenAI is revolutionizing Rev-Ops functions in enterprise technology businesses. In the first part, we saw some broad applications of Gen AI in Rev Ops. In the next two parts, we explored how GenAI use cases are being  deployed in Business Strategy and Planning, followed by its applications in Marketing. In this final part, we conclude by taking a look at how Gen AI is shaping the future of Customer Success.


A little bit about myself - I am a Sr. Director of Growth and Rev Ops at Cisco. Here is my LinkedIn profile - prior to Cisco, I was a Manager at McKinsey, with a background in Industrial Robotics and Electrical Engineering.


  • Enhancing Personalization:  Generative AI enables businesses to deliver highly personalized experiences by analyzing vast and different datasets to understand individual customer preferences and behaviors. This capability allows for tailored interactions with products, recommendations, and content curation that resonate with each customer, significantly improving their experience. Curated AI playlists from Spotify is just the tip of the iceberg - this capability is poised to revolutionize enterprise user landscape as well.
  • Improved Customer Support: AI-powered chatbots and virtual assistants can provide instant, 24/7 support, addressing common queries and troubleshooting issues in real-time in a highly personalized manner leveraging input and data from multiple sources. These tools have the potential to enhance the customer support experience by offering quick and accurate responses, reducing wait times, and improving overall satisfaction and adoption. Admittedly, this can also be a double edged sword - with the premium and desire for human interaction going up in parallel- but there is no doubt that a lot of the simple support tasks could be delivered via agents in most situations, helping human agents to be more productive and successful.
  • More accurate churn prediction and prevention: Generative AI can combine disparate vast datasets and identify nuanced insights around customer sentiment, enhancing its predictive analytics capabilities to identify patterns indicative of potential churn. By analyzing customer interactions and behaviors across different channels, AI can predict which customers are at risk of leaving and enable preemptive actions to retain them. This proactive approach helps in maintaining a loyal customer base. This also allows businesses to get better insights into customer’s usage of products, and derive input for further product and service enhancement.
  • Automating Repetitive Tasks: Language capabilities is where Generative AI shines the most - with the ability to handle repetitive tasks such as call notes, meeting minutes, and documentation, freeing up customer success managers (CSMs) to focus on more strategic activities. This automation increases efficiency and productivity of  CSMs — allowing them to focus on higher value activities.
  • Better Fraud Detection: While enhancing customer experience and success is a key business imperative, identifying fraudulent behavior is also equally important. By continuously learning and adapting based on customer interactions, Gen AI can help flag fraudulent behavior early and at scale, helping businesses identify and mitigate fraudulent activities, ensuring a secure and trustworthy environment for customers and partners.


Generative AI is rapidly reshaping customer success and adoption by enhancing personalization, enhancing human productivity through automation and support from generative agents, and enabling data-driven decision-making at scale in a variety of use cases and circumstances. However, it is essential to ensure ethical use and maintain data integrity to fully capitalize on AI's potential in Customer Success as well as Rev Ops more broadly. While this article concludes this series, I look forward to observing and studying the innovations in this space in the coming days - the promises are strong and substantial, with the potential to change the function dramatically.