The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems

As a seasoned consultant with a focus on AI and Automation, I've found 'The AI Product Manager's Handbook' by Irene Bratsis to be a critical resource in my professional toolkit. This book adeptly introduces fundamental machine learning concepts tailored specifically for AI product managers. It achieves a commendable balance, offering sufficient technical insights to empower product managers in AI discussions, while remaining accessible to those without a technical background.Bratsis emphasizes the significance of collaborating with stakeholders and acting as a bridge among various departments involved in AI projects. Additionally, she thoughtfully addresses AI ethics, particularly the need for representative datasets, moving beyond superficial treatment of ethical concerns in AI implementation within organizations.A notable strength of the book is Bratsis' structured approach to developing AI products, covering both AI-centric products and the incorporation of AI into existing products. This methodological perspective is particularly valuable, as the field of AI Product Development, although increasingly recognized, lacks a clearly defined framework. Bratsis' contribution in this regard, outlining a comprehensive process for driving AI products from conception to deployment, is commendable.The book is enriched with practical examples that vividly bring AI product concepts to life. These examples are especially beneficial for readers like myself, who are less technically inclined, offering clear insights into the practicalities and significance of AI solutions. It stands as an indispensable guide for industry leaders keen on successfully steering and launching AI initiatives.While Bratsis extensively covers AI ethics, a thought that struck me during my reading was the under-discussed topic of 'truth' in datasets. While issues of bias and representation receive deserved attention, the importance of training machine learning models on reliable and truthful data sources is less frequently addressed. This leads to critical considerations, such as whether legal models are trained on sensational media articles or actual court filings, or if scientific AI models are based on peer-reviewed research or popular science commentary. Recognizing bias as a relative term, it is crucial to prioritize truthfulness as a fundamental standard.The book, largely written pre-chatGPT, possesses an authenticity that is refreshing. Since it predates the surge of generative AI like chatGPT, it feels more grounded in the foundational aspects of AI and ML, steering clear of the latest buzzwords and trends like 'Prompt Engineering.' However, an exploration of how generative AI might reshape the role of AI Product Managers would have been a valuable addition. Nevertheless, this aspect only heightens my anticipation for future updates from Bratsis.In conclusion, 'The AI Product Manager's Handbook' is an essential read for anyone engaged in AI product management. Bratsis' insights, combined with real-world examples, offer a clear and practical guide for leading successful AI product initiatives. The book is particularly recommended for leaders seeking to adeptly manage AI integration in a variety of sectors, maintaining a balanced and ethically responsible approach.

Irene Bratsis's 'The AI Product Manager’s Handbook' is not just a book; it's a reflection of her brilliance and dedication, which I've witnessed firsthand. Her ability to simplify AI concepts, combined with her depth of experience in digital product and data management, makes this book indispensable. As someone who has worked alongside Irene, I can attest to her expertise and foresight in AI product management. This book is a testament to her skill in guiding both novices and professionals through the nuances of AI, making it a must-read for anyone in this field.

Great resource for AI POs. Entire book I received has print quality issues though: entire paragraphs cut off or missing.

The journey from a traditional product management realm to one intertwined with data, machine learning, and other advanced Data Science methodologies has been a roller-coaster of sorts in my career. “The AI Product Managers Handbook” by Irene Bratsis has served as a sagely companion in this voyage. It not only validated the struggles typical to AI/ML teams but also laid down a pragmatic roadmap to navigate through them.I particularly relished the balanced discourse on commencing an AI product journey and infusing AI into existing products. Bratsis’ caution against being carried away by the current AI euphoria, while reiterating the bedrock principles of modern product management, was a timely reminder. I wholeheartedly recommend this book to Product Managers either entrenched in or intrigued by the conjunction of AI and product development.

To preface, I'm a Machine Learning Engineer with over a decade of experience in the field. So this review is mainly catered towards those who have been through the fire and gotten burned a few times.Overall, I don't think the book is bad. I think that it gives comprehensive information in that if you gathered up all of the information on the internet (even in the dark corners and hard to reach places) and put them in one place, this would be that place. It's probably a bit better than that; but on to my major gripe: There really isn't any meat. As someone else said, it would have greatly benefitted from case studies and places in the author's experience where they've come across the concepts they're talking about and referring to. The funny thing is that as I read the book, I began to see areas where I came across the author's concepts and could have expanded upon by them by giving my own stories. Given that several concepts are frequently repeated, it’s frustrating when that space could have been used to show or go into more detail. For example, the idea of data being king is repeated a lot. It's a very important concept; but I think it would have been helpful to show reader's why it's important as well. This would have solidified the author's point of view on data and would have given something more actionable to reader's.I also think that the lack of visuals and diagram's in the book really hurt the overall presentation. Product Manager's are all about diagrams and visuals to show the various aspects of the products (including the roadmap, product release plan, etc.) to the customers and stakeholders. Why not show us how you as the author presented things to stakeholders and management? It would have really helped solidify their expertise.Overall, I think the book just didn't have enough meat for me personally. The meat is what really solidifies the credibility of the writer in instances like this. And it just fell short of that touch.

As an AI PM, I didn't find anything related to real and practical AI product management stuff. All the things in this book are freely available.