The Art of AI Product Development: Delivering business value

by Janna Lipenkova

A hands-on guide for delivering value with AI-driven products! Learn how AI can improve content creation, accelerate data analysis, and upgrade process automation.

The Art of AI Product Development offers a clear, practical approach to creating products that use AI. It provides real-world guidance on defining your AI strategy, developing useful AI features, and supporting user trust and adoption. Rather than chasing trends, the book focuses on core principles and long-term thinking—foundations that remain relevant as the field evolves.

Inside The Art of AI Product Development, you will learn vital skills for the effective use of AI, market and business opportunities for AIGaining an in-depth understanding of modern AI approaches, incl. predictive AI, LLMs, Retrieval-Augmented Generation, and agent systemsAssembling AI solutions that work, without the hypeEfficiently communicating with data scientists and ML engineersDesigning user-friendly AI interfaces that emphasize trust and transparencyImplementing safe, ethical AI with proper governance processes
The Art of AI Product Development is written for product managers, tech executives, UX designers, and anyone responsible for the success of an AI-driven product. It introduces a broad spectrum of AI opportunities and case studies from different domains such as marketing, supply chain, and logistics. You’ll carefully progress from initial design conversations, through to efficient and secure development, and on to deployment and day-to-day management of AI-driven applications.

About the technology

Integrating AI into your software and processes can create real value for your business and its customers—if you do it right. When you’re on the hook for delivering AI-enabled products, you’ll need to spot high-impact opportunities, work effectively with engineers, design user-centric features, avoid common project failures, and manage real-world launches. This book shows you how.

About the book

The Art of AI Product Development gives you a clear framework, practical tools, and real-world examples to build confidence and succeed with new AI projects—even if you’re tackling AI for the first time. You’ll love the practical use cases and end-to-end scenarios from domains such as marketing, supply chain management, and sustainability.

What's insideIdeate, shape, and prioritize AI opportunitiesDevelop AI systems with techniques such as prompt engineering, RAG, and predictive AICommunicate with different AI stakeholders and promote AI adoption
About the reader

Written for software product managers, business-oriented engineers, UX designers, startup founders, and anyone responsible for developing, designing, or marketing AI products. No experience with AI required.

About the author

Dr. Janna Lipenkova is the founder of an AI and analytics business where she has successfully managed AI projects for world-class companies like BMW, Lufthansa, and Volkswagen.

Table of Contents

Part 1
1 Creating value with AI-driven products
2 Discovering and prioritizing AI opportunities
3 Mapping the AI solution space
Part 2
4 Predictive AI
5 Exploring and evaluating language models
6 Prompt engineering
7 Search and retrieval-augmented generation
8 Fine-tuning language models
9 Automating workflows with agentic AI
Part 3
10 AI user Designing for uncertainty
11 AI governance
12 Working with your stakeholders
Appendix A AI development toolbox

Genres: Artificial Intelligence

368 pages, Paperback

Published July 15, 2025

Reviews

[Ali]

A guide on product development lifecycle (SDLC/PLM) for incorporating AI into your business whether in greenfield, greyfield or brownfield. Lipenkova defines the AI SDLC in three phases (covered in 3 parts of the book): Discovery, Development, and Integration/Adoption. Discovery phase is about identifying the opportunities to utilize AI and prioritize methods or models based on business value they could actually deliver. Development part goes over challenges of LMMs (size/cost), RAG pipelines (data issues), and agents when not organically built into business. The Integration/Adoption part demonstrates various approaches in UX, governance or stakeholder alignment eventually determining success or failure of your AI products. Lipenkova recommends an agile approach with smaller models and prototypes/pilots gradually exposing AI into your business. Overall recap of SDLC in the context of AI.

[Johnny]

A nice, accessible book for people who are new to product management, and a helpful refresher for anyone who now needs to incorporate AI into their products. It provides a good overview of the key steps involved in building AI-powered products, while also highlighting the things that can go wrong along the way.

The checklists in the appendix are particularly useful, providing practical reminders of the important questions and considerations to keep in mind for the task at hand. They make it easy to revisit the book when working on an actual product rather than simply reading it once and putting it aside.

When it comes to technical implementation, however, you will need a different book. The technical material here is too shallow to be particularly useful in practice. It points you in the right direction and helps you understand what you need to consider, but leaves you to figure out the actual implementation on your own.

Overall, this is a useful introduction and reference for the product-management side of building with AI, but it should be complemented with more technical resources if you are responsible for implementation.

[Ali Mortezapour]

I really enjoyed this book. It beautifully combines traditional product management thinking with the practical use of AI to solve real, often overlooked customer problems. One of the aspects I appreciated most was the lean product development approach, especially the clear distinction between the problem space and the solution space.

Dr. Janna Lipenkova brings together years of experience and turns them into practical, easy-to-follow frameworks that feel both thoughtful and actionable. This is a valuable read for anyone interested in product thinking, innovation, and applying AI in a meaningful way.

I hope other readers enjoy this book as much as I did. Happy reading!

[Kenneth]

This is a good book for anyone with out a deep product background, or if you're a product person and trying to see how to analyze and understand how AI changes the developer, user, and product experience.

A great intro for those trying to determine how to define your products in the age of AI, and a good refresher for those in the field.

Especially helpful are the sections on mapping the solution space, identifying where AI might be problematic (e.g. effects of bias) and defining how it changes your user experience.

[Reem Zekry]

This book offers a practical, no-hype perspective on AI product development. Instead of focusing only on models, it explains the full system behind successful AI products: opportunity, value, data, intelligence, user experience, and governance. One of the most important takeaways is that many AI projects fail not because of the model, but because the data and product goals are misaligned. A highly recommended read for AI engineers, product managers, and anyone interested in building impactful AI systems.

[Guang Lu]

Dr. Janna Lipenkova’s book offers an excellent exploration of the intersection between AI technologies and business innovation. It provides a wealth of valuable insights and practical knowledge, serving as a highly useful guide for AI product development. Moreover, it can serve as an effective blueprint for teaching AI and innovation at the university level. I highly recommend this book to both AI practitioners and business professionals.

[Andreas Lange]

The book is quite dense, but very well-researched and grounded. I was struggling with looking past all the buzz and hype around AI, and this book really helped me understand what actually works with the present capabilities of the technology. It manages to organically put AI into the larger context of a business.

[Liliia Zinchenko]

I have read part of this book and I should appreciate it as it's written quite good and should be very helpful for those PM who are just learning the AI possibilities for their work. It can also be helpful for everyone from the related fields like UX / UI design, FE and BE if you want to switch from your field to PM or simply understand AI that is used in your company on all levels.

[Luiz]

The book Art of AI Product Development is a great resource for any person working in product development who wants to understand more about how AI-based applications work. The book covers how this new technology is transforming multiple industries and how LLMs can be applied in our own work contexts. I really recommend this reading for my peer product people.

[Anna Lipanovic]

I really enjoyed the scenarios in the book. Almost every chapter is based on an end-to-end story, and it is not just made up, you can feel the practical side of it. That was very useful for me to develop a more differentiated judgement when it comes to AI!

[Ninoslav Cerkez]

This book emphasizes the business and product perspective of AI solutions. At the same time, the author provides enough technical details for each topic. So, you can't go wrong with this book. It will broaden your perspective on deploying AI solutions in production.

[Özgür]

DNF...
No real unique insights of any value for AI products.

[Ming Wen]

Very practical, well-structured book. Really gave me more confidence with AI - it is not super technical, but everything is backed by high-quality sources.

[Prashant]

I liked chapter 6 a lot

[Sujatha]

The book is exceptionally well-structured, comprehensive, and practical, offering a full-spectrum view of AI products.It successfully combines strategic thinking with tactical implementation, making it valuable for all roles such as PMs, data scientists, and cross-functional teams.

The book has balanced coverage of both business and technical aspects focussed on sections about language models, RAG, fine-tuning, agentic AI, and prompt engineering but also AI governance, UX for uncertainty

Some sections can be re-ordered for flow and easier understanding and adding more case studies or interviews with AI product teams would make the read more interesting.