فهرست مطالب

• Preface
• Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well
• Understanding the Infrastructure and Tools for Building AI Products
• Definitions – what AI is and is not
• Introducing ML and DL
• ML versus DL – understanding the difference
• Learning paradigms in ML
• LLMs, NLP, GANs, and generative AI
• Succeeding in AI – how well-managed AI companies do infrastructure right
• The order – what is the optimal flow and where does every part of the process live?
• Storing and managing data
• Managing projects – IaaS
• Deployment strategies – what do we do with these outputs?
• The promise of AI – where is AI taking us?
• Summary
• Additional resources
• References
• Model Development and Maintenance for AI Products
• Understanding the stages of NPD
• Model types – from linear regression to neural networks
• OKRs
• Training – when is a model ready for market?
• Deployment – what happens after training?
• Testing and troubleshooting
• Ethical retraining – the ethics of how often we update our models
• Summary
• Additional resources
• References
• Deep Learning Deep Dive
• Types of neural networks
• Case study
• Exploring generative AI models
• Emerging technologies – ancillary and related tech
• Explainability – optimizing for ethics, caveats, and responsibility
• Guidelines for success
• Summary
• References
• Leave a Review!
• Commercializing AI Products
• The professionals – examples of B2B products done right
• The artists – examples of B2C products done right
• The pioneers – examples of blue ocean products
• The rebels – examples of red ocean products
• The GOATs – examples of differentiated disruptive and dominant strategy products
• Summary
• References
• AI Transformation and Its Impact on Product Management
• Money and value – how AI could revolutionize our economic systems
• Sickness and health – the benefits of AI and nanotech across healthcare
• Goods and services – growth in commercial applications
• Government and autonomy – how AI will shape our borders and freedom
• Basic needs – AI for Good
• Summary
• Additional resources
• References
• Building an AI-Native Product
• Understanding the AI-Native Product
• Stages of AI product development
• AI/ML product dream team
• Investing in your tech stack
• Productizing AI-powered outputs – how AI product management is different
• AI customization
• Selling AI – product management as a higher octave of sales
• Case study
• Summary
• References
• Productizing the ML Service
• Basics of productizing
• AI versus traditional software product management
• B2B versus B2C – productizing business models
• Using AIOps/MLOps
• Case study
• Summary
• References
• Customization for Verticals, Customers, and Peer Groups
• Domains – orienting AI toward specific areas
• Verticals – examination of some key domains
• Thought leadership – learning from peer groups
• Case Study
• Summary
• References
• Product Design for the AI-Native Product
• Product design elements 101
• What makes the AI-native product design process special?
• Choosing your priorities wisely
• What’s the story you’re telling?
• Case study
• Summary
• References
• Benchmarking Performance, Growth Hacking, and Cost
• Value metrics – a guide to north star metrics, KPIs and OKRs
• Hacking – product-led growth
• The tech stack – early signals
• Managing costs and pricing – AI is expensive
• Case study
• Summary
• References
• Managing the AI-Native Product
• The head – Managing alignment
• The heart – Managing people and values
• The guts – Managing the rest
• Case study
• Summary
• References
• Integrating AI into Existing Traditional Software Products
• The Rising Tide of AI
• Evolve or die – when change is the only constant
• Changes in the Fourth Industrial Revolution
• Fear is not the answer – there is more to gain than lose (or spend)
• Anticipating potential risks
• How LLMs are evolving and the rise of open source LLM capabilities
• Case study
• Summary
• References
• Join us on Discord
• Trends and Insights Across Industry
• Highest growth areas for AI integration
• Low-hanging fruit – quickest wins for AI enablement
• Riding the GenAI wave
• Summary
• References
• Evolving Products into AI Products
• Ideation – what’s possible, what’s desirable, and what’s probable
• Case study
• Data management – the bloodstream of the company
• Competition – love your enemies
• Product strategy – building a blueprint that works for everyone
• Red flags and green flags – what to look for and watch out for
• Summary
• Additional resources
• The Role of AI Product Design
• The evolution of product design
• Expansion: What makes the evolved AI product special?
• Choosing your words carefully
• Building with trust and security
• Case study
• Summary
• References
• Managing the Evolving AI Product
• The head – managing alignment
• The heart – managing the people and values
• The guts – managing data, infrastructure, and ongoing maintenance
• Case study
• Summary
• Managing the AI PM Career
• Starting a Career as an AI PM
• Bolstering your knowledge in theory and practice
• What an AI PM looks like today
• The importance of communities
• Choosing your AI PM specialization
• Case study
• Summary
• References
• What Does It Mean to Be a Good AI PM?
• A job family of many hats
• The AI whisperer and the role of communicating accessibly
• Common challenges and opportunities as you’re leveling up in your career
• The importance of self-care
• Case study
• Summary
• Maturing and Growing as an AI PM
• Projecting – what’s your ideal AI PM roadmap?
• Learning – staying informed and inspired
• Networking – deepening your involvement with the professional community
• Growing – the student becomes the teacher
• What’s next? The world is our oyster
• Case study
• Summary
• Other Books You May Enjoy
• Index