Call for Chapters: Strategies for Commercializing AI Startups

Editors

Cristina Raluca Gh. Popescu, University of Bucharest, & The Bucharest University of Economic Studies, Romania
Poshan Yu, European Business Institute of Luxembourg, Luxembourg & The Australian Studies Centre of Shanghai University, China

Call for Chapters

Proposals Submission Deadline: June 11, 2026
 Full Chapters Due: September 3, 2026
 Submission Date: September 3, 2026

Introduction

Artificial Intelligence (AI) has evolved from a niche technological innovation to a transformative force reshaping global industries, with startups at the forefront of translating cutting-edge AI research into marketable products and services. The rapid advancement of generative AI, large language models (LLMs), and open-source AI infrastructure has lowered technical entry barriers, spurring an unprecedented wave of AI startups seeking to capitalize on emerging opportunities across sectors such as healthcare, finance, education, and enterprise services. However, the path from AI prototype to sustainable commercial success remains fraught with unique challenges that distinguish AI startups from traditional tech ventures—challenges that have led to a "two-tiered" scenario in the AI industry, where investment enthusiasm coexists with a high failure rate, with some entrepreneurs noting that "one company collapses every three months".

Industry realities highlight a critical "commercialization gap": while AI technology continues to advance at a breakneck pace, only approximately 12% of enterprises—and even fewer startups—successfully scale AI projects to generate consistent, measurable business value. Many AI startups fall prey to the "technology-centric trap," prioritizing technical sophistication over alignment with market needs, leading to products that fail to address real-world pain points—a phenomenon exacerbated by the "fruit fly-like iteration" of large models, where a model’s "shelf life" averages only 35 days, often rendering AI startup products obsolete before they even launch. Additionally, AI startups grapple with distinct hurdles, including high computational costs, data accessibility and privacy constraints (a critical "data dilemma" that has derailed many projects, particularly in healthcare and industrial sectors), talent shortages, unclear monetization models, and the need to navigate complex regulatory landscapes—all while competing with established tech giants and navigating a shifting funding environment where capital has become increasingly prudent, shifting focus from technical demos to real business value loops.

In an era where AI innovation is democratized by open-source models and cloud-based AI tools, the critical differentiator for AI startups is no longer just technical capability, but the ability to execute effective commercialization strategies. This edited collection aims to fill a critical void in existing literature by focusing explicitly on the practical, actionable strategies that enable AI startups to bridge the gap between technological potential and commercial viability. Unlike generic startup guides or technical AI handbooks, this book will center on the unique intersections of AI technology, market dynamics, and business strategy—offering a comprehensive resource for researchers, practitioners, and entrepreneurs seeking to navigate the complex journey of AI startup commercialization. It will address the core paradox of the AI industry: while technology has become more accessible than ever, commercial success remains elusive for most startups, often due to misalignment with real business scenarios, data barriers, or unsustainable monetization models.

Objective

This book intends to accomplish four core objectives, each designed to address gaps in current research and provide tangible value to its audience, while responding to the most pressing challenges facing AI startups today:

1. Synthesize Current Knowledge and Identify Critical Gaps: The book will consolidate existing research on AI startup commercialization, critically analyzing successful and failed case studies to identify recurring patterns, common pitfalls (such as the "technology-centric trap" and "scenario mismatch"), and understudied areas. It will address the limitations of current literature, which often focuses either on AI technology in isolation or on general startup strategies, without integrating the unique challenges of AI commercialization—including non-zero marginal costs, data-driven barriers, rapid model iteration, and the pressure from tech giants and open-source dynamics.

2. Develop a Comprehensive, Practical Framework for Commercialization: Drawing on interdisciplinary insights from business strategy, technology management, entrepreneurship, and AI research, the book will propose a structured framework that guides AI startups through key commercialization phases—from idea validation and product-market fit to scaling, monetization, and global expansion. This framework will integrate the "technology-business dual-drive model", emphasizing the need for both leveraging accessible AI tools (technology inclusiveness) and deep alignment with specific business scenarios (business positioning), while addressing the unique constraints of rapid model iteration and data scarcity.

3. Provide Actionable Strategies and Evidence-Based Insights: Beyond theoretical frameworks, the book will offer actionable strategies tailored to the unique needs of AI startups, including approaches to reducing computational costs, designing value-aligned pricing models (addressing the "more usage, more losses" dilemma faced by many C-end AI products), building data-driven competitive advantages (overcoming data barriers in vertical sectors), and navigating regulatory compliance. It will also address emerging trends such as vertical scenario deepening and AI-native business models, which are reshaping the commercialization landscape and enabling startups to avoid direct competition with tech giants.

4. Advance Scholarly Discourse and Practical Application: The book will contribute to academic research by extending theories of technology commercialization, entrepreneurship, and innovation management to the AI context—particularly by re-evaluating traditional SaaS metrics and business models that are no longer applicable to AI startups, given their unique cost structures and iteration cycles. For practitioners, it will serve as a hands-on guide, translating academic research into actionable steps that can be implemented to improve commercialization outcomes for AI startups, helping them avoid common pitfalls such as "demo grandstanding" and "false demand" products.

By achieving these objectives, the book will add significant value to current research by focusing on the "how" of AI startup commercialization—moving beyond descriptive analyses to provide prescriptive, evidence-based strategies. It will also further existing research by highlighting emerging trends (e.g., vertical scenario breakthroughs, cross-border commercialization of validated AI products) and addressing understudied topics (e.g., ethical commercialization, startup-enterprise partnerships in AI), thereby filling critical gaps in both academic and practical knowledge.

Target Audience

This book is strategically geared toward a diverse, interdisciplinary audience of researchers, practitioners, and stakeholders with a direct interest in AI startup commercialization. The primary audience segments, and their specific benefits, are as follows:

1. AI Startup Founders and Entrepreneurs,

2. Academic Researchers and Students,

3. Investors and Venture Capitalists,

4. Corporate Innovation Teams and Technology Leaders,

5. Policy Makers and Regulators,

6. AI Practitioners and Technical Leaders.

Overall, the book will be accessible to both academic and non-academic audiences, balancing theoretical rigor with practical relevance to ensure it serves as a valuable resource for anyone involved in or interested in the commercialization of AI startups.

Recommended Topics

We invite chapter proposals that address the following 16 recommended topics, which align with the book’s objectives and cover the full spectrum of AI startup commercialization. Proposals focusing on interdisciplinary approaches, real-world case studies, and actionable strategies are particularly encouraged. Topics include (but are not limited to):

1. Navigating the AI Commercialization Gap: Strategies for bridging the divide between AI technical feasibility and market adoption, including addressing the "12% phenomenon" and avoiding the technology-centric trap, while adapting to the rapid iteration of large models that often renders products obsolete.

2. Product-Market Fit for AI Startups: Methods for validating AI product-market fit, including user research, iterative prototyping, and aligning AI capabilities with unmet market needs—with a focus on vertical niche targeting to avoid direct competition with tech giants and address scenario mismatch issues.

3. Monetization Models for AI Startups: Innovative pricing strategies (e.g., usage-based, outcome-based, hybrid models) and revenue streams tailored to AI products, including addressing the challenge of non-zero marginal costs and the "more usage, more losses" dilemma faced by many C-end and B-end AI startups.

4. Cost Optimization for AI Startups: Strategies for managing computational costs, leveraging open-source models and cloud infrastructure to reduce entry barriers, and improving unit economics in AI product development and delivery—critical for surviving the resource-intensive AI commercialization journey.

5. Data Strategy for Commercialization: Approaches to acquiring, managing, and leveraging private domain data to build competitive advantages, while navigating data privacy regulations (e.g., GDPR, CCPA) and overcoming data barriers in vertical sectors such as healthcare, education, and industry.

6. AI Startup Funding and Investment Strategies: Navigating the venture capital landscape, securing seed/series funding, and demonstrating commercial viability to investors—including alternative funding models for early-stage AI startups, in an era of increasingly prudent capital allocation focused on business value loops.

7. Scaling AI Startups: From Prototype to Mass Adoption: Operational strategies for scaling AI products, including team building, technology infrastructure, and customer acquisition—with a focus on adaptive go-to-market (GTM) strategies and integrating AI into users’ existing workflows to avoid "one-time tool" pitfalls.

8. Ethical Commercialization of AI: Balancing innovation with ethical considerations (e.g., bias mitigation, transparency, accountability) in AI product commercialization, and addressing stakeholder expectations—critical for building trust and long-term sustainability in the AI ecosystem.

9. Regulatory Compliance for AI Startups: Navigating complex global AI regulations, industry-specific compliance requirements (e.g., healthcare, finance), and building regulatory resilience into commercialization strategies—particularly as data privacy and AI governance become increasingly stringent across regions.

10. Partnerships and Ecosystems in AI Commercialization: Strategies for building strategic partnerships (e.g., with enterprises, research institutions, cloud providers) to accelerate commercialization, access data resources, and mitigate the competitive pressure from tech giants.

11. AI-Native Business Models: Commercialization strategies for AI-native applications and AI agents, including defining value propositions and monetizing autonomous task execution capabilities—aligning with the shift from "demo grandstanding" to "process integration" in AI commercialization.

12. Cross-Border Commercialization of AI Startups: Approaches to expanding AI products into global markets, including localization strategies, cultural adaptation, and navigating international regulatory differences—leveraging the "service capability overseas expansion" model for validated AI products.

13. Case Studies in AI Startup Commercialization: In-depth analyses of successful and failed AI startup commercialization efforts, extracting actionable lessons and best practices—including vertical scenario breakthroughs (e.g., healthcare, finance) and failures due to data barriers or scenario mismatch.

14. Talent Acquisition and Retention for AI Startups: Strategies for attracting and retaining AI talent (e.g., data scientists, AI engineers) and building cross-functional teams that bridge technical and business expertise—critical for aligning technical development with commercial goals and avoiding the technology-centric trap.

15. Competitive Strategy for AI Startups: Differentiating AI startups from incumbents and competitors, leveraging niche markets, and building sustainable competitive advantages (e.g., domain expertise, data barriers) to avoid being outcompeted by tech giants or disrupted by open-source models.

16. Future Trends in AI Startup Commercialization: Emerging technologies (e.g., edge AI, multimodal AI) and market shifts (e.g., AI democratization, vertical scenario deepening) that will shape the future of AI startup commercialization, including the growing importance of "product intelligence micro-innovation" and "business process integration".

Submission Procedure

Researchers and practitioners are invited to submit on or before June 11, 2026, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by June 25, 2026 about the status of their proposals and sent chapter guidelines.Full chapters of a minimum of 10,000 words (word count includes references and related readings) are expected to be submitted by September 3, 2026, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Strategies for Commercializing AI Startups. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.

Publisher

This book is scheduled to be published by IGI Global Scientific Publishing, an international academic publisher of the "Information Science Reference", "Medical Information Science Reference", "Business Science Reference", and "Engineering Science Reference" imprints. IGI Global Scientific Publishing specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2027.

Indexing Information for Prospective Authors

IGI Global Scientific Publishing meets the criteria for inclusion in major indexing services such as Scopus; however, it is important to note that all indexing decisions are made independently by these services. IGI Global Scientific Publishing books are selectively indexed by the indexing organization after publication. Indexing cannot be guaranteed for any book prior to publication, and the indexing organization has complete control over the final selection and timeline.

Important Dates

June 11, 2026: Proposal Submission Deadline
June 25, 2026: Notification of Acceptance
September 3, 2026: Full Chapter Submission
October 15, 2026: Review Results Returned
November 12, 2026: Final Acceptance Notification
November 19, 2026: Final Chapter Submission

Inquiries

Cristina Raluca Gh. Popescu
University of Bucharest, & The Bucharest University of Economic Studies, Romania
popescu_cr@yahoo.com

Poshan Yu
European Business Institute of Luxembourg, Luxembourg & The Australian Studies Centre of Shanghai University, China
yuposhan@outlook.com
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