AI Startup Strategy: A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit

AI Startup Strategy: A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit

by Adhiguna Mahendra
AI Startup Strategy: A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit

AI Startup Strategy: A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit

by Adhiguna Mahendra

Paperback(1st ed.)

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Overview

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

Product Details

ISBN-13: 9781484295014
Publisher: Apress
Publication date: 08/05/2023
Edition description: 1st ed.
Pages: 428
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Adhiguna Mahendra, with his extensive academic and industry experience, brings a wealth of knowledge to this book. He obtained his M.S. degree in Computer Vision and Robotics from the University of Heriot-Watt, U.K. 2008, and his Ph.D. in Machine Learning and Computer Vision from the Universite de Dijon, France, in 2012.

Currently, he serves as Chief of AI, Business, and Products at Nodeflux, Indonesia's leading AI Vision company. At Nodeflux, he has spearheaded the development and commercialization of AI products, overseeing everything from product design and algorithm development to operationalization through MLOps.

His 20+ years of experience span across global and national companies, developing intelligent systems across various sectors. Alongside, he serves as a lecturer at Swiss German University and Central Queensland University, honored with the Best Lecturer Award in 2018. Adhiguna is also the founder of the AI Business Institute, an online platform offering courses in Deep Learning and AI Product Management. Adhiguna is also a key contributor to AI policy-making in Indonesia and serves as an AI trainer and technology advisor.

Adhiguna has a robust academic footprint with publications in esteemed platforms like SPIE, ICME, ICODSE, and IEEE. He also balances his work with his love for Muay Thai, jazz piano, and soccer, which he enjoys with his three boys. In this book, he shares valuable insights on building AI startups, drawing from his extensive experience and knowledge.

Table of Contents

· Chapter 1: Introduction of AI Product Management:

Chapter Goal :

• 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.

• Case Study.

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

Chapter Goal:

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

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

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

• Case Study.

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

Chapter Goal:

• To understand what is a developer-centric product.

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

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

• Case Study.

· Chapter 4: AI Product Strategy:

Chapter Goal:

• To understand the foundation of product strategy.

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

• To understand how to do AI product requirement analysis.

• To understand how to do AI product prioritization.

• Case Study.

· Chapter 5: AI Product Development in practice:

Chapter Goal:

• To understand the foundation of the product lifecycle.

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

• To understand how to do AI product development.

• Case Study.

· Chapter 6: Software Development Lifecycle for AI products :

Chapter Goal:

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

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

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

• Case Study.

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

Chapter Goal:

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

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

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

• To understand how to define a highly effective technical team

• Case Study.

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

Chapter Goal:

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

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

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

• Case Study.

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

Chapter Goal:

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

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

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

• Case Study.

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

Chapter Goal:

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

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

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

• Case Study.

· Chapter 11: Recruiting and Managing AI talents:

Chapter Goal:

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

• To understand how to scout and recruit AI talents.

• To understand how to outsource AI development.

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

• Case Study.

· Chapter 12: Strategizing Exit Plan:

Chapter Goal:

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

• To understand how to targeting acquisitors.

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

• The future of Enterprise AI landscapes.

• Wrapping Up.

• Case Study.

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