Название: Successful AI Product Creation: A 9-Step FrameworkАвтор: Shub Agarwal
Издательство: Wiley
Год: 2025
Страниц: 304
Язык: английский
Формат: pdf, epub (true)
Размер: 12.2 MB
The Essential Guide to AI and Generative AI Product Creation from a Veteran AI Leader and Educator.
In Successful AI Product Creation: A 9-Step Framework, AI product leader, professor of product management and AI, and industry expert, Prof. Shub Agarwal delivers the ultimate playbook—a comprehensive, step-by-step guide to Building, Scaling, and Integrating AI and Generative AI into real-world products. Drawing from over two decades of experience, this comprehensive guide bridges the gap between AI technology and business impact, ensuring you can navigate the AI revolution with confidence.
The cutting-edge technology known as Generative AI is a specific Artificial Intelligence (AI) that produces new content that closely mimics training data. It operates across various formats, including text, images, audio, and video, each contributing to diverse business applications. At its core, Generative AI leverages advanced algorithms to learn patterns and structures from existing data, enabling it to generate innovative outputs that have the potential to revolutionize industries.
Generative AI serves as a transformative force in product development—from rapid prototyping to design iteration—while augmenting human decision-making through data analysis and scenario generation. It amplifies creative capabilities by suggesting novel approaches and variations while streamlining content production through automated generation of text, code, and media assets. However, its role remains collaborative, enhancing rather than replacing human expertise and judgment.
LLMs, such as GPT (generative pretrained transformer), are leading the way in generative AI technologies. These models are capable of producing language that closely resembles human speech due to their training on large datasets, which helps them identify patterns. The training process involves two main stages: pretraining and fine-tuning. During pretraining, the model learns from a broad corpus of text, developing an understanding of language structures and semantics. Fine-tuning involves adjusting the model's parameters on specific tasks to enhance performance. Large datasets are paramount in this context. These datasets provide the diverse examples the model needs to learn a wide range of linguistic patterns and contexts. Consequently, LLMs are helpful tools for content creation, customer service automation, and personalized communication because they can produce coherent and contextually relevant language.
What You'll Learn:
• Complete 9-Step AI Product Creation Framework: Master the entire AI product lifecycle from discovery and experimentation to scaling, governance, and AI model lifecycle management.
• 20+ Real-World Case Studies: Learn from successful AI implementations across healthcare, finance, e-commerce, retail, manufacturing, and big tech companies like Google, Meta, Amazon, and Apple.
• Traditional AI vs. Generative AI: Understand when to use each approach, how to leverage models like GPT and transformers, and key differences in adoption strategies.
• AI Model Performance and Ethics: Address challenges like bias, fairness, model drift, and regulatory compliance.
• Practical Tools and Templates: Access decision-making frameworks, checklists, and internal diagrams that guide seamless execution.
Who Should Read This Book?
• AI Product Managers and Tech Leaders: A strategic and tactical guide for AI integration.
• Entrepreneurs and Founders: Leverage AI for competitive advantage and scalability.
• Business Executives and Decision-Makers: Understand AI's potential for growth and optimization.
• Students and Aspiring AI PMs: Develop industry-ready skills through real-world case studies.
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