It has been a while since OpenAI published ChatGPT, and other companies followed, creating their own generative AI.
Despite the spreading of these technologies, Engineering and Product Managers often struggle to understand how their teams use generative AI, its potential benefits for them and their companies, its impact on productivity, and the associated privacy concerns.
How can Product and Engineering Managers harness the power of generative AI like ChatGPT to innovate and stay ahead while navigating the complex landscape of productivity and privacy concerns?
In this article, we are going to cover the following:
Our software and data science teams’ usage of ChatGPT and generative AI
Our product team’s usage of ChatGPT and generative AI
The added value and spotted dangers in every use case
Over the last months, I have interviewed dozens of developers and product owners in different departments of my company and some other folks in my network. I can share our main use cases and how we have evolved our usage of ChatGPT over time.
This article does not deal with privacy and security concerns. I don’t recommend sharing your data with AI tools operating on external platforms. Your sensitive or proprietary information should be protected.
On a side and funny note, what I have seen change over time is the ease with which developers speak about their usage of generative AI. Only since the end of last year have I gotten transparent feedback about their usage. Maybe it is linked with the fact that some executives in some companies were wondering if generative AI could effectively replace their developers; perhaps it was some shame or fear of being judged by an engineering manager, I don’t know. Anyway, I now find more and more developers that transparently talk about their usage and how AI helps them. This makes it easier to determine the gain in productivity.