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Generative AI: Innovations and Applications Across Industries [Kumar Karna] - mklabgr - 08-30-2026
Generative AI: Innovations and Applications Across Industries
Subtitle:Exploring the Influence on Healthcare, Education, Finance, and Beyond
Authors: Brijesh Kumar Karna, Suryabhan Singh, Pethuru Raj Chelliah, Akshitta R. Chaudhary
Publication date: 26 August 2026 (eBook)
Publisher: Apress, Berkeley, CA
Edition: 1st
Length: 527 pages
ISBN: 979-8-8688-2912-3
Topics: Artificial Intelligence, Machine Learning, Generative AI, Large Language Models, Python
Summary
Generative AI: Innovations and Applications Across Industries is a broad, application-oriented survey of generative artificial intelligence and the ways it is reshaping professional and industrial activity. The book begins with the foundations of generative AI, introducing technologies such as deep neural networks, variational autoencoders (VAEs), generative adversarial networks (GANs), transformers, and large language models. Rather than concentrating only on how the models work mathematically, the authors emphasize how these technologies can be incorporated into real systems and organizations.
The central part of the book examines generative AI sector by sector. Creative applications include automated writing, visual art, and music; business and marketing applications include content generation, personalization, and new forms of customer engagement. Healthcare chapters discuss applications such as drug discovery, personalized medicine, and medical imaging, while the education chapter examines personalized learning and AI-generated interactive educational material. Other chapters extend the discussion to entertainment, law, finance, industrial maintenance, agriculture, environmental sustainability, recruitment, and talent acquisition.
A particularly substantial part of the book is devoted to the broader consequences of deploying these systems. The authors examine ethical, legal, governance, privacy, bias, and societal questions alongside the technical possibilities of generative AI. The concluding section uses case studies and expert perspectives to connect the earlier examples and consider future developments. The result is less a specialist textbook on building foundation models and more a wide-ranging guide to where generative AI can be used, what benefits it may provide, and what problems organizations must manage when adopting it. It is aimed primarily at AI professionals, machine-learning engineers, software developers, and readers interested in applying generative AI across business domains.
Key takeaways
- Generative AI is becoming a general-purpose technology: its uses extend well beyond chatbots into medicine, education, finance, manufacturing, agriculture, recruitment, media, and creative work.
- LLMs are only one part of the field: the book places them alongside GANs, VAEs, deep learning, transformers, and other generative techniques.
- The main challenge is increasingly deployment rather than mere model capability: organizations need integration strategies, governance, reliability, privacy safeguards, and appropriate human oversight.
- Ethical and legal questions are treated as central rather than secondary: bias, accountability, privacy, regulation, intellectual property, and responsible deployment become increasingly important as generative systems move into consequential domains.
Springer/Apress book page