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
Author(s): Adhiguna Mahendra
Publisher: Apress
Year: 2023
Language: English
Pages: 434
Contents
About the Author
The Praise for AI Startup Strategy
Introduction
Chapter 1: Fundamental of AI Startups
Historical Perspective: The Fourth Revolution
AI Startups vs. AI-First Companies
Understanding Enterprise AI
Fundamental of AI Technologies
Large Language Models (LLMs) and AGI
Enterprise AI, Analytics, and Automated Decision
When to Deploy AI in Decision-Making
Automated Decision and the SETDA Loop
Conclusion
Key Takeaways
Chapter 2: AI Startup Landscape
What Problems Do AI Startups Solve?
The Role of an AI Product Manager
AI Startup Business Model
The Business Value Within the Value Chain
The Business Value of an AI Product
Understanding the Valuation of AI Startups
AI Startup Acquisitions
Challenges of Building AI Startups
The Key to Building Successful AI Startups
The Successful AI Startup Patterns
Conclusion
Key Takeaways
Chapter 3: Product-Market Validation for AI-First SaaS
SaaS and Its Evolution
What Is SaaS
From SaaS to AI-Powered SaaS to AI-First SaaS
AI as a Service (AIaaS)
Understanding the Fundamental Principles of SaaS
Product-Market-Technology (P-M-T) and Validation Framework
Product-Market Validation Fundamental
Product-Technology Validation Fundamental
Product-Market-Technology Validation
Five-Step AI-First SaaS Validation Framework
Step 1: Choosing a Target Industry
Step 2: Brainstorming Ideas
Step 3: Measuring Idea Feasibility
Business Feasibility: Market Size
Business Feasibility: Usage Frequency
Business Feasibility: Market Need
Business Feasibility: Use Case Scalability
Business Feasibility: Competitiveness
Technical Feasibility: Expected Level of Autonomy
Technical Feasibility: Risk of Error
Technical Feasibility: Algorithmic Complexity
Technical Feasibility: Infrastructure Complexity
Technical Feasibility: Data Feasibility
Step 4: Recruiting Early Adopters
Step 5: Validating Product-Market-Technology Fit
Conclusion
Key Takeaways
Chapter 4: Product-Market Validation for AI as a Service (AIaaS)
What Is AIaaS and a Developer-Centric Product
Definition of AIaaS and B2D
Difference Between B2B and B2D
Understanding a Developer-Oriented AI Product: API
AIaaS Business Models
Why Selling to Developers
The Developer Market Is Lucrative
The Characteristics and Challenges of the Developer Market
Key to a Successful Developer-Centric Product
The Mistakes of Developer-Centric Product Strategy
Five-Step AIaaS Validation
Step 1: Breaking Down the AI Solution
Step 2: Defining the Vertical Market
Step 3: Mapping the Developer Buying Journey
Step 4: Testing the Market
Step 5: Validating Product-Market-Technology Fit
Conclusion
Key Takeaways
Chapter 5: AI Product Strategy
Product Strategy Fundamental
Product Roadmap
Define Product Vision, Strategy, and Roadmap
Product Discovery
Understanding Customer Needs
Discovery of Needs
Translating Needs to Requirements
Product Requirements Analysis
Define Product Requirements
Product Prioritization
Identification of Product Purpose and Product Objectives
From Product Objectives to Customer Values to Roadmap
Collaborative Weighted Scorecard Prioritization Method
Measuring the Efficacy of the Product Roadmap
Ten Sins of AI Product Roadmapping
Conclusion
Key Takeaways
Chapter 6: Human-Centered AI Experience Design
The Principles of Human Factors in AI
Embrace Customer Needs
Amplify Human Capability
Embrace Trustworthiness
Be Ethical
User Experience Design of an AI Product
Principles of AI UX Design
Design Thinking
AI UX Design Principles
AI UX Design Process Framework
1. Empathize
Steps:
Output:
2. Define
Steps:
Output:
3. Ideate
Steps:
Output:
4. Prototype
Steps:
Output:
5. Test
Steps:
Conclusion
Key Takeaways
Chapter 7: Human- Centered AI Developer Experience Design
AI Products for Developers
Principles of AI DX Design
AI Developer Experience Process Framework
1. Empathize
2. Define
3. Ideate
4. Prototype
5. Test
Conclusion
Key Takeaways
Chapter 8: Building an AI Platform
Introduction
Key Components and Layers of an AI Software Platform
AI Platform Design
Six Layers of the AI Platform
MLOps/ModelOps
LLM, VLP, and LLMOps
Team Topologies
Unifying All
Challenges in Building an AI Platform
Ideal AI Platform Design
Architecting an AI Platform
AI Product Archetypes and Their Architectural Complexity
Measuring the Maturity Level of Your AI System
Best Practices of Architecting an AI Software Platform
Designing the AI Platform Architecture
Evaluating Technology Choices
Developing an AI Platform
Why AI Software Development Is Different from Traditional Software Development
The Principles of Software Development for an AI Software Platform
Understanding AI Software Development Stages
Measuring the Maturity Level of an AI Software Development Process
Measuring AI Software Development Process Maturity
Applying the Measurement Framework to Your Process
AI Software Development Process
Operationalizing an AI Platform
Team and Task
Coordinating the Different Teams with Team Topologies
Registering IP of an AI Product
How to Scout Top AI Talents and Compete with Big Tech
Conclusion
Key Takeaways
Chapter 9: Go-to-Market Strategy for an AI Startup
Background
Introduction to the Go-to-Market Strategy for AI Startups
The Importance of a Go-to-Market Strategy for AI Startups
Description of Different Types of AI Products
AI (as a) Solution
AI as a Service
AI (as a) Toolkit
Go-to-Market Strategy for AI (as a) Solution
Identifying the Target Market
Developing a Unique Value Proposition
Designing a Customer Journey Map
Developing a Marketing Strategy to Reach the Target Market
Go-to-Market Strategy for AI as a Service
Understanding the Needs and Pain Points of Developers
Developing a User-Friendly and Flexible API and SDK
1. Clear Documentation
2. Integration
3. Flexibility
4. Consistency
5. Excellent Assistance
6. Compatibility
What Makes a Great Documentation?
Determining the Pricing Model and Packaging That Appeals to Developers
Packaging Strategies for AIaaS
Pricing Models for AIaaS
Customer Journey Mapping
Find
Assess
Absorb
Develop
Scale
Developing a Marketing Strategy
Go-to-Market Strategy for AI (as a) Toolkit
Understanding the Needs and Pain Points of Developers and Data Scientists
Developing a User-Friendly and Comprehensive AI Toolkit
Determining the Pricing Model and Packaging That Appeals to Developers
Designing a Customer Journey Map
Building a Partnership Strategy
Partnership with Distributors
Partnership with a System Integrator
Developing a Marketing Strategy
Conclusion
Key Takeaways
Chapter 10: AI Startup Exit Strategy
Introduction
The Gold Rush of AI
Exit Strategies of AI Startups
Why Companies Acquire
The Importance of an Exit Plan
Factors Impacting AI Startup Acquisition
Identifying Potential Acquirers
Understanding Your Strategic Value
Searching and Assessing a Potential Acquirer
Approaching Potential Acquirers and Initiating Conversations
Preparing the Company for Sale
Maximizing AI Startup Value
Valuation Methods for AI Startups
Creating a Compelling Story
Negotiating the Sale
Negotiating the Terms of the Sale
Handle Objections and Counteroffers
Key Legal and Financial Considerations During the Negotiation Process
Due Diligence
Technical Due Diligence
Financial, Legal, and Commercial Due Diligence
Labor Due Diligence
Closing the Deal
Finalize the Sale and Transfer Ownership of the Company
Communication Strategy
The Transition from Seller to Acquirer
Conclusion
Key Takeaways
Final Thoughts
Index
