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Disruptive Machine Learning Entrepreneurship for Micro, Small, Medium, and Large-Sized Enterprises (MSMEs)

A Volume in Information Systems Engineering & Management (ISEM) book series, Springer-Verlag

Assoc. Prof.  Adeyemi Abel Ajibesin
Chair, Software Engineering
School of IT & Computing,
American University of Nigeria
Assoc. Prof. Narasimha Rao Vajjhala
Dean, Faculty of Engineering and Architecture,
University of New York Tirana
Professor Tiko Iyamu
Professor - Information Technology
Cape Peninsula University of Technology, Cape Town, South Africa

About the Book

This book will provide a comprehensive guide for enterprises of all sizes to leverage machine learning for groundbreaking entrepreneurial ventures. As our world undergoes rapid digital transformation, micro, small, medium, and large enterprises (MSMEs) must navigate the challenges and opportunities brought about by machine learning advancements. This book demystifies the intersection of machine learning and entrepreneurship, offering actionable insights, real-world case studies, and proven strategies to catalyze business growth and innovation. From nascent start-ups looking to disrupt local markets to established conglomerates aiming to reinvent their global footprint, this book will cater to entrepreneurs at every stage to discover how to harness data-driven insights, foster a culture of continuous learning, and pivot effectively in response to the ever-evolving digital landscape. This book will venture beyond traditional business models and dive deep into the transformative potential of machine learning. This book will equip the readers with the tools, knowledge, and foresight to spearhead change, capitalize on emerging opportunities, and drive enterprises toward a future of unparalleled success.

Unlike other books that target only start-ups or large corporations, this proposed book will address the unique needs and challenges of micro, small, medium, and large enterprises. This book will explore real-world examples of MSMEs successfully integrating machine learning, offering tangible lessons and insights. This book will include a dedicated section on the ethics of machine learning in business, addressing potential pitfalls and responsible innovation. This book offers actionable strategies beyond theoretical knowledge and is suitable for practitioners and policymakers looking into actionable insights. This book will include a unique section dedicated to predicting future trends, equipping readers with foresight in a rapidly evolving field. While many books in this segment focus on Western markets, this book provides a global perspective, addressing opportunities and challenges in diverse regions.

Note: There are NO submission or acceptance fees for manuscripts submitted to this book publication. This book will be submitted for possible indexation to Scopus, Web of Science, and major indices.

Topics (Suggestive but not limited to)

The proposed tentative content for this book is given below:

  1. Introduction

    • The Evolution of Entrepreneurship in the Digital Age

    • Understanding MSMEs and Their Unique Position

    • Challenges and Opportunities

  2. The Machine Learning Revolution

    • What is Machine Learning?

    • Machine Learning vs. Traditional Computing

    • Understanding Machine Learning in Entrepreneurship

  3. Entrepreneurship in the Age of Data

    • The Data-driven Business Model

    • Identifying Opportunities in Big Data

  4. Strategies for Micro and Small Enterprises

    • Embracing Localized Machine Learning Solutions

    • Pivoting with Agile Methodologies

  5. Navigating Medium-sized Enterprises

    • Scaling with Machine Learning at the Core

    • Addressing the Talent and Technology Gap

  6. Reinventing Large Enterprises with Machine Learning

    • Digital Transformation and Enterprise Overhaul

    • Leveraging Machine Learning for Innovation

    • Case Studies: Big Corporations That Pivoted Successfully

  7. Tools and Techniques for Machine Learning Implementation

    • Practical Implementation Strategies

    • Tools for MSMEs

    • Overcoming Implementation Challenges

  8. Ethical Challenges and Risks

    • Ethical Considerations in Machine Learning Entrepreneurship

    • Avoiding Over-reliance on Data

  9. The Future of Machine Learning and Entrepreneurship

    • Beyond Automation: The Rise of AI-first Businesses

    • Global Trends and Predictions for the Next Decade

    • Strategies for Future Success

  10. Concluding Thoughts and Call for Action.

  11. Preparing for a Disruptive Future: Action Steps for MSMEs

Submission Guidelines

All papers must be original and not simultaneously submitted to another book, journal, or conference.

Kindly note the following when submitting your book chapter:

  • Submit an initial proposal through the EquinOCS system, including the chapter title and the problem/purpose statement explaining the proposed chapter: 



  • The length of the chapter should be 6,000 - 8,000 words.

  • Paper should be formatted as per the template provided. 

  • Ensure that the paper adheres to Springer’s book chapter formatting guidelines. All submissions should be made in Word format only (.DOCX):

  • All papers will be checked for plagiarism.

  • The chapter template is given below:

       Download Word Template


       Sample Book Chapter


Feel free to email us if you have questions or want feedback on your proposed research question (,, You may also contact us via professional social media at:

Important Dates

Expression of Interest                -    April 30, 2024

Draft Chapter Due Date             -    June 30, 2024 

Peer Review Period                   -    July 1 - August 30, 2024

Finalized Chapter Due               -    September 30, 2024

Final Acceptance Notification    -    October 30, 2024

Book Publication Date               -    December 2024

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