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AI business model: an integrative business approach

Author

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  • Shrutika Mishra

    (Banaras Hindu University)

  • A. R. Tripathi

    (Banaras Hindu University)

Abstract

Artificial intelligence is the ecosphere’s prevalent and most comprehensive general acquaintance common-sense cognitive engine. The artificial intelligence (AI) business platform model is virtually at affluence with cloud SaaS model. It concerns AI solutions that can work together on the top layer of the other digital systems, like a Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) business system. AI admittances in the digital data fluid through the coordination, fueling business enhancements over phases. In this business model, the business will safekeep a recurrent subscription. This paper endeavors to emphasize on the preventative side of the use of AI and machine learning (ML) technology to enterprise digital platform business model innovation and business dynamics. We acme the strategic implications and innovations with analytics. We explore the derivations of data-driven insights, models, and visualizations.

Suggested Citation

  • Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
    • Handle: RePEc:spr:joiaen:v:10:y:2021:i:1:d:10.1186_s13731-021-00157-5
    DOI: 10.1186/s13731-021-00157-5
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    References listed on IDEAS

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    2. Nam, Jinyoung & Jung, Yoonhyuk & Kim, Junghwan, 2024. "Understandings of the AI business ecosystem in South Korea: AI startups’ perspective," Telecommunications Policy, Elsevier, vol. 48(6).
    3. Asmat Ara Shaikh & K. Santhana Lakshmi & Korakod Tongkachok & Joel Alanya-Beltran & Edwin Ramirez-Asis & Julian Perez-Falcon, 2022. "Empirical analysis in analysing the major factors of machine learning in enhancing the e-business through structural equation modelling (SEM) approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 681-689, March.
    4. Monirah Ali Aleisa & Natalia Beloff & Martin White, 2023. "Implementing AIRM: a new AI recruiting model for the Saudi Arabia labour market," Journal of Innovation and Entrepreneurship, Springer, vol. 12(1), pages 1-41, December.
    5. Ha, Seungyeon & Park, Yujun & Kim, Jongpyo & Kim, Seongcheol, 2023. "Research trends of digital platforms: A survey of the literature from 2018 to 2021," Telecommunications Policy, Elsevier, vol. 47(8).
    6. Jorzik, Philip & Klein, Sascha P. & Kanbach, Dominik K. & Kraus, Sascha, 2024. "AI-driven business model innovation: A systematic review and research agenda," Journal of Business Research, Elsevier, vol. 182(C).
    7. Yasheng Chen & Mohammad Islam Biswas, 2021. "Turning Crisis into Opportunities: How a Firm Can Enrich Its Business Operations Using Artificial Intelligence and Big Data during COVID-19," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    8. Zhaoxi Wei, 2024. "Navigating Digital Learning Landscapes: Unveiling the Interplay Between Learning Behaviors, Digital Literacy, and Educational Outcomes," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 10516-10546, September.
    9. Mariani, Marcello M. & Machado, Isa & Nambisan, Satish, 2023. "Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda," Journal of Business Research, Elsevier, vol. 155(PB).

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