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

As a pretty new product manager and as part of a team that is really focused on AI, I was keen to pick this up and get some deeper insights into working with AI as a product manager. This book stands out for its practical approach, demystifying complex machine learning concepts and offering actionable insights that bridge the gap between technical teams and product managers. One of the book's strongest points is its structured roadmap, which walks readers through the entire AI product lifecycle—from ideation and feasibility analysis to development and post-launch iterations. It effectively highlights the importance of collaboration between data scientists, engineers, and product managers, providing tools and frameworks to facilitate seamless communication and project alignment. Moreover, the case studies included are particularly enlightening, showcasing real-world applications of AI and the challenges encountered. These examples not only illustrate successful implementations but also provide valuable lessons from less successful attempts, making it a well-rounded educational resource. While the book is rich in detail, it remains accessible to those who may not have a deep technical background. The author's ability to explain machine learning principles in a clear and concise manner ensures that readers of varying expertise can grasp and apply the concepts.

AI is here to stay and once you get over the fear of being replace by AI, you learn that you need to adapt to it. There will be many more AI products entering our lives and jobs. Building these products comes with its own unique set of challenges. This book covers a large ground on these challenges. Of course a single book is not going to cut it if you aim to work in this space for long. However, this book offers a good entering point that you can build on

The AI Product Manager’s Handbook is really useful for individuals who want to know more about AI. I highly recommend it.

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.

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.

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

Geht nicht auf die Neuerungen durch LLMs wie Chat-GPT ein. Nur Basis Wissen zu allgemeiner KI. Nicht zu empfehlen. Autoren mit mehr Kompetenz wären wünschenswert.

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.