To state the obvious: “AI” is all the rage nowadays, and for good reason. We are probably experiencing the next major technological breakthrough in real-time and this should be an exciting journey. 

As Product Managers and leaders, we obviously have a lot of questions. On the one hand, we worry about how this will affect our jobs. On the other hand, we are left (if not expected) to figure out how to make AI part of our products.

Before attempting to answer these questions, we should first get a few things straight, however.

When we say “AI”, we tend to refer to recent innovations around large language models (LLMs) such as OpenAI’s ChatGPT and Google Bard. This form of AI, which is also referred to as “generative AI” is made available by external providers who have spent years and lots of data to train these models. Those models run on their own (cloud) infrastructure and can be accessed by APIs these providers have made available. The output is largely text- (and recently also image and video-) based. The term “AI” in general is a much larger set of technologies and approaches, not just generative AI.

Then there is “Machine Learning” (ML), which is technically a subset of AI. ML has been around for years and has already found many useful applications which have made inroads into our day-to-day lives, oftentimes without much fanfare (Amazon recommendations, anyone?). ML tends to be more focused in its application compared to generative AI and is great at dealing with numerical and quantitative data. Unlike the big LLMs, companies can create, train, and run their ML models on their own infrastructure with their own proprietary data. With that, there are much fewer external dependencies.

Getting back to how “AI” impacts Product Managers, there are really several parts to this answer:

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