AI起業戦略<br>AI Startup Strategy : A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit (1st)

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AI起業戦略
AI Startup Strategy : A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit (1st)

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  • 製本 Paperback:紙装版/ペーパーバック版
  • 言語 ENG
  • 商品コード 9781484295014

Full Description

Gain exclusive access to the secrets to building an enterprise AI start-up. AI innovation helps with every aspect of the business, from the supply chain, marketing, and advertising, customer service, risk management, operations to security. Industries from different verticals have been adopting AI and get real business values out of it.  
This book guides you through each step, from defining the business need and business model, all the way to registering IP and calculating your AI start-up valuation. You see how to perform market and technology validation, perform lean AI R&D, design AI architecture, AI product development and operationalization. The book also cover building and managing an AI team, along with attracting and keeping business and developer users,
Building an Enterprise AI start-up is hard because Enterprise AI is an effort to build applications to mimic human intelligence to solve business problems. Hence it has a different challenge from building traditional non-AI applications, such as scouting, recruiting and managing AI talents; designing the most cost-efficient and scalable Enterprise AI; or establishing the best practice to operationalize AI in production
As we are in the dawn of the AI-first product wave, AI-powered products for enterprises will be created for many years to come and AI Startup Strategy is the one-stop guide for it.

What You'll Learn

Match customer's expectation VS technical feasibility
Justify business values and ROI for customers
Review the best business models for high valuation enterprise AI start-ups
Design an AI product that gives a satisfactory experience for the user

Register and value AI IP 

Who This Book is For 
Startup Founders, Product Managers, Software Architects/Lead Engineers, Executives

Contents

​·         Chapter 1:  Introduction of AI Product Management:

Chapter Goal :

o   To understand the foundation of enterprise AI.

o   To understand AI start-up's landscape, including taxonomy, business value and ROI, business models, and valuation.

o   Case Study.

 

·         Chapter 2: Product Market Validation for B2B AI Start-ups:  

Chapter Goal:

o   To understand why we need to do AI product-market validation for B2B.

o   To understand when to do AI product-market validation for B2B.

o   To understand how to do AI product-market validation for B2B.

o   Case Study.

 

·         Chapter 3: Product Market Validation for B2D AI Start-ups:  

Chapter Goal:

o   To understand what is a developer-centric product.

o   To understand why selling to the developer is one of the best strategies for AI products.

o   To understand how to do AI product-market validation for B2D.

o   Case Study.

 

·         Chapter 4: AI Product Strategy:  

Chapter Goal:

o   To understand the foundation of product strategy.

o   To understand how to do discovery for AI-related products.

o   To understand how to do AI product requirement analysis.

o   To understand how to do AI product prioritization.

o   Case Study.

 

·         Chapter 5: AI Product Development in practice:  

Chapter Goal:

o   To understand the foundation of the product lifecycle.

o   To understand how to do User Research for AI products.

o   To understand how to do AI product development.

o   Case Study.

 

 

·         Chapter 6:  Software Development Lifecycle for AI products :  

Chapter Goal:

o   To understand the foundation of the software development lifecycle (SDLC).

o   To understand how the SDLC for AI is different from traditional SDLC.

o   To understand DevOps and MLOps concepts, the difference, and practices. 

o   Case Study.

 

·         Chapter 7:  Software Architecture and Team design for AI products :  

Chapter Goal:

o   To understand the importance of Conway law for AI start-ups.

o   To understand why data engineering and operations are the keys to successful AI start-ups.

o   To understand how to design scalable data-intensive software architecture.

o   To understand how to define a highly effective technical team 

o   Case Study.

 

·         Chapter 8:  Building effective AI Product Go-To-Market strategy :  

Chapter Goal:

o   To understand the foundation of AI start-ups' growth strategy.

o   To understand the B2B and B2D sales funnels, the difference, and strategies.

o   Understanding AIaaS and AI-powered SaaS marketing and growth metrics.  

o   Case Study.

 

·         Chapter 9:  Building effective AI Product Go-To-Market strategy :  

Chapter Goal:

o   To understand the foundation of AI start-ups' growth strategy.

o   To understand the B2B and B2D sales funnels, the difference, and strategies.

o   Understanding AIaaS and AI-powered SaaS marketing and growth metrics.  

o   Case Study.

 

·         Chapter 10:  Building effective AI Product Go-To-Market strategy :  

Chapter Goal:

o   To understand the foundation of AI start-ups' growth strategy.

o   To understand the B2B and B2D sales funnels, the difference, and strategies.

o   Understanding AIaaS and AI-powered SaaS marketing and growth metrics.  

o   Case Study.

 

·         Chapter 11: Recruiting and Managing AI talents:

Chapter Goal:

o   To understand that production AI is different from academia Ph.D.

o   To understand how to scout and recruit AI talents.

o   To understand how to outsource AI development.

o   To understand how to manage the AI team and minimize turn-over.

o   Case Study.

 

 

 

 

 

·         Chapter 12: Strategizing Exit Plan:

Chapter Goal:

o   To understand how to drive strategic value in AI start-ups.

o   To understand how to targeting acquisitors.

o   To understand the M&A process and how to select M&A advisors.

o   The future of Enterprise AI landscapes.

o   Wrapping Up.

o   Case Study.