ChatGPT Work | How NVIDIA Scales GTM with AI

### TL;DR An NVIDIA individual contributor uses ChatGPT to automate workflows, reducing 40% of manual data analysis time to zero. It runs automatically twice a week and can easily scale to global teams. ## Background & Personal Experience I'm currently an individual contributor at NVIDIA. ChatGPT has truly become a force multiplier for me. The key isn't how smart the model is — it's that I can take workflows I've already developed and automate them to run end-to-end with almost no overhead. ### Key to the Automated Workflow Before, about 40% of my time was spent on manual data analysis. Now that work is completely gone, replaced by an automated process. I just have ChatGPT run it twice a week automatically and get the analysis results I need. This change has been a huge quality-of-life improvement, and I especially love it. ## Large-Scale Data Analysis & Team Scaling ### Synthesizing Thousands of Data Points This year we used ChatGPT to synthesize thousands of data points, meeting notes, and customer interview transcripts. What used to require significant manpower and time to summarize now easily yields insights into how we're tracking against our goals. ChatGPT can understand these scattered pieces of information and quickly pull them together into clear conclusions. ### Scalability of the Base Modules What impresses me most is that once you build the base modules, they can easily scale to global teams. In other words, there's no need for each team to start from scratch — just reuse the existing automated workflow templates, and more colleagues can benefit from the efficiency gains. Source: YouTube video (https://www.youtube.com/watch?v=xYVfknDQHU4)