AI Startup Strategy: A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress L. P.
2023
|
| Ausgabe: | 1st ed |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (434 Seiten) |
| ISBN: | 9781484295021 |
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| 245 | 1 | 0 | |a AI Startup Strategy |b A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit |
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| 505 | 8 | |a Intro -- 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
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Datensatz im Suchindex
| _version_ | 1818760560385196032 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Mahendra, Adhiguna |
| author_facet | Mahendra, Adhiguna |
| author_role | aut |
| author_sort | Mahendra, Adhiguna |
| author_variant | a m am |
| building | Verbundindex |
| bvnumber | BV050100530 |
| collection | ZDB-30-PQE |
| contents | Intro -- 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 |
| ctrlnum | (ZDB-30-PQE)EBC30676490 (ZDB-30-PAD)EBC30676490 (ZDB-89-EBL)EBL30676490 (OCoLC)1394117134 (DE-599)BVBBV050100530 |
| dewey-full | 006.30681 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.30681 |
| dewey-search | 006.30681 |
| dewey-sort | 16.30681 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV050100530 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-18T07:00:34Z |
| institution | BVB |
| isbn | 9781484295021 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035437692 |
| oclc_num | 1394117134 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (434 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Apress L. P. |
| record_format | marc |
| spelling | Mahendra, Adhiguna Verfasser aut AI Startup Strategy A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit 1st ed Berkeley, CA Apress L. P. 2023 ©2023 1 Online-Ressource (434 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- 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 Erscheint auch als Druck-Ausgabe Mahendra, Adhiguna AI Startup Strategy Berkeley, CA : Apress L. P.,c2023 9781484295014 |
| spellingShingle | Mahendra, Adhiguna AI Startup Strategy A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit Intro -- 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 |
| title | AI Startup Strategy A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit |
| title_auth | AI Startup Strategy A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit |
| title_exact_search | AI Startup Strategy A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit |
| title_full | AI Startup Strategy A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit |
| title_fullStr | AI Startup Strategy A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit |
| title_full_unstemmed | AI Startup Strategy A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit |
| title_short | AI Startup Strategy |
| title_sort | ai startup strategy a blueprint to building successful artificial intelligence products from inception to exit |
| title_sub | A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit |
| work_keys_str_mv | AT mahendraadhiguna aistartupstrategyablueprinttobuildingsuccessfulartificialintelligenceproductsfrominceptiontoexit |