Essential Guide to AI Product Management
In this post when I use the term AI product management (APM), I mean to include both AI and ML (which is technically more accurate). I believe that AI PM is a key role and needs specific skills, judgement and experience that are critical to success of AI products and initiatives.
As a practicing APM and organizer of a successful AI Meetup, I wanted to share useful resources, best practices and tips that I came across and learned from my experience. The principles and tips here are also useful for project managers, software managers and any role where you make decisions for technology teams. I do not spend time talking about the basics of AI here as I assume you already have that background. However there are references at the end if you want to learn about ML or if you want to know more about PM role.
The role / title of a Product Management is relatively recent — I’d say ~25 years. AI Product Management is focused on using AI, Deep Learning and/or Machine Learning to enhance, improve, create and shape products. AI Product Management (APM) is certainly a very recent role.
A recent survey of global business leaders indicated that 70% have started AI initiatives. With the proliferation of AI into business it is easy to see applications to B2C and B2B products and services: Google Search / Photos / Translate, Alexa, Amazon…
