[AI for Business #12] - 🎉 Launching The Art of AI Product Development

Your comprehensive guide for designing, building, and scaling AI in B2B.

Dear readers,

After 15 years of building, advising, and fixing AI products, I wrote the book I wish every team had at the start of their AI journey. It’s called The Art of AI Product Development, and it’s finally available!

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📘 👉 Use the code PBLipenkova here for 50% off (valid until June 30th)!

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This isn’t another book about the magic of AI models or agents. It’s a guide to the messy, high-stakes, and deeply human work of building AI products that actually work—products that generate real value, and survive outside of demo land. If you've ever felt overwhelmed by AI jargon, frustrated by another flaky PoC, or uncertain about how to move from experimentation to production, this book is for you.

But let me take a step back and tell you how it started. As a student, I worked as a translator, and I quickly grew bored with the repetitive nature of the job. Around 2009, I started to look for ways to automate them. Quickly, this curiosity turned into a PhD project and a deep, enduring interest in AI. At the time, AI wasn’t exactly hot stuff. It sat at the margins of mainstream science, built on rigid rules, limited data, and minimal compute. My colleagues and I believed in its potential, but the path ahead was unclear. Academia gave us a safe space to explore, but it also revealed just how far there was to go.

Fast forward to today: AI hype is everywhere. From translation to medical imaging, tasks that once took days can now be done in seconds. AI models and tools are no longer confined to labs but are publicly available. You can pick, choose, and integrate them into your workflows in almost every industry.

However, accessibility doesn’t mean success. Over the past few years, I’ve worked with dozens of product teams across industries like mobility, finance, energy, and beyond. Often, I came in after the damage was already done, and a project that had started with great expectations was about to join the 80% of AI projects that fail to deliver. In one way or another, most of these teams had overestimated their capabilities while underestimating the complexity of their AI endeavours.

Seeing this pattern again and again is what finally pushed me to write The Art of AI Product Development. The book summarizes the knowledge needed to build successful AI products in an enterprise context. It stays true to my zero-hype policy, distilling scientific facts and practical experience into a clear, accessible guide for non-technical readers.

Now, let’s look at some key insights and snapshots from the book.

What you’ll find in the book

This book is a practical guide for building AI products and applications. It’s written for:

• Product managers and business leaders making strategic bets on AI

• Designers crafting experiences that can live with uncertainty

• Engineers integrating models, pipelines, prompts, and agents

• Knowledge workers who wish to optimize their work with AI

Based on 15 years of hands-on experience about what works, what doesn’t, and why, I structured it around the three big themes for AI in the enterprise:

• Discovery: How to identify and prioritize viable AI opportunities and navigate the AI solution space

• Development: Predictive AI, LLMs, prompt engineering, RAG pipelines, agent systems

• Adoption: AI UX design, governance, bias mitigation, and stakeholder alignment

Each of the twelve chapters presents an end-to-end case study from industries incl. finance, logistics, marketing, and enterprise software.

Some highlights from the book:

• Discovering and structuring AI use cases with the AI Opportunity Tree: “Outside of the bubble of technological fascination, your users and customers have real-life problems and expect you to provide solutions. Most of them don’t care about the AI in your product - and if they do, they might rather associate it with risks such as job replacement, privacy, and hallucinations. How, then, can you identify exactly those customer problems that are worth solving with AI? In my experience, it’s helpful to start with a bias toward the specific benefits of AI. As an example, imagine that you’re managing a music streaming app. As you explore AI opportunities along six types of benefits— automation and productivity, improvement and augmentation, personalization, inspiration and innovation, convenience, and emotional benefits—you end up with the following AI Opportunity Tree…”

  

  The AI Opportunity Tree helps you discover and structure your AI opportunities

• Gaining a shared and holistic understanding with the AI System Blueprint: “Each team member sees the AI system through their own lens—technical feasibility, user experience, data quality, and regulatory risk. The AI System Blueprint captures these different dimensions. You can use it to plan any AI project, align your team and other stakeholders, and update your setup with new insights over time.”

  

  The AI System Blueprint creates alignment between different stakeholders

• People often ask why the cover of my book features a dervish. It reflects a fundamental shift in how we need to think about AI product development.

  Traditional software development often aims for a clear delivery point. With AI, the real work begins after the initial launch. The quality of your iteration loop—how quickly you learn, adapt, and improve—defines your long-term success. The faster your team can evaluate outputs, optimize performance, and learn from each cycle, the more valuable your system becomes. Over time, this learning compounds into a powerful form of IP.

  The dervish represents this mindset: continuous movement, deliberate refinement, never standing still.

  

• If you asked me about the single biggest barrier to AI adoption in the enterprise, I’d say trust - or, rather, its lack or excess. Mistrust can come from users, but also from management, compliance teams, or other stakeholders. On the flip side, too much trust can lead users to accept AI outputs uncritically, overlooking potential mistakes or biases. Whatever the root cause, transparency and trust calibration are essential. Teams need to understand what AI can do, what it can’t, and where human judgment remains irreplaceable. In the book, I provide a toolbox for designing user experiences and educating users for proper trust calibration.

  

  On the trust scale, you can plan how you will build and calibrate the trust of your users

👉 Get the book here with 50% off using the code PBLipenkova.

Why this book matters now

With AI, we need to move fast:

• As individuals, we need to understand AI to stay relevant in the workplace.

• As businesses, we must implement it wisely to remain competitive in a shifting market.

• As societies, we are in a mess of our own making. We face climate breakdown, social fragmentation, and global instability. We need to come together and explore how AI can help us fix what we’ve broken..

The window for trial and error is narrow, and learning through failure is becoming a luxury. That’s the deeper reason for writing this book: to give you the foundations so you can move forward with AI in a competent, confident, and deliberate way.

That’s it for today. In the next couple of episodes, I will be posting deep-dives on specific topics from the book, such as RAG, AI agents, and AI monetization. If you have any questions or suggestions for topics you would like to see covered, please hit reply and let me know!

Thank you for reading, learning, and building with me!

Warmly,

Janna

Author of The Art of AI Product Development