IDEAS home Printed from https://ideas.repec.org/a/spr/joiaen/v10y2021i1d10.1186_s13731-021-00157-5.html
   My bibliography  Save this article

AI business model: an integrative business approach

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s13731-021-00157-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1186/s13731-021-00157-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rajendra Akerkar, 2019. "Artificial Intelligence for Business," SpringerBriefs in Business, Springer, number 978-3-319-97436-1, March.
    2. Jack Clark & Gillian K. Hadfield, 2019. "Regulatory Markets for AI Safety," Papers 2001.00078, arXiv.org.
    3. Davenport, Thomas H., 2018. "The AI Advantage: How to Put the Artificial Intelligence Revolution to Work," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262039176, December.
    4. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    5. Shrutika Mishra & A. R. Triptahi, 2019. "Platforms oriented business and data analytics in digital ecosystem," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 1-16, December.
    6. Constantin Zopounidis & Michael Doumpos, 1999. "Business failure prediction using the UTADIS multicriteria analysis method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(11), pages 1138-1148, November.
    7. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    8. Rajendran Muthuveloo & Teoh Ai Ping, 2013. "Achieving Business Sustainability Via I-TOP Model," American Journal of Economics and Business Administration, Science Publications, vol. 5(1), pages 15-21, September.
    9. Jason Furman & Robert Seamans, 2019. "AI and the Economy," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 161-191.
    10. Shrutika Mishra, 2018. "Financial management and forecasting using business intelligence and big data analytic tools," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(02), pages 1-16, June.
    11. Alexander Maedche & Christine Legner & Alexander Benlian & Benedikt Berger & Henner Gimpel & Thomas Hess & Oliver Hinz & Stefan Morana & Matthias Söllner, 2019. "AI-Based Digital Assistants," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 535-544, August.
    12. Shrutika Mishra & A. R. Tripathi, 2020. "IoT Platform Business Model for Innovative Management Systems," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 1-31, September.
    13. Shrutika Mishra & A. R. Tripathi, 2020. "Platform business model on state-of-the-art business learning use case," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 1-12, June.
    14. Luo, Suyuan & Lin, Xudong & Zheng, Zunxin, 2019. "A novel CNN-DDPG based AI-trader: Performance and roles in business operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 68-79.
    15. Shrutika Mishra & A. R. Tripathi, 2020. "Literature review on business prototypes for digital platform," Journal of Innovation and Entrepreneurship, Springer, vol. 9(1), pages 1-19, December.
    16. Andrew Burgess, 2018. "The Executive Guide to Artificial Intelligence," Springer Books, Springer, number 978-3-319-63820-1, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhiqiang Zhou & Wenyan Liu & Pengfei Cheng & Zhenjin Li, 2022. "The Impact of the Digital Economy on Enterprise Sustainable Development and Its Spatial-Temporal Evolution: An Empirical Analysis Based on Urban Panel Data in China," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    2. Angeliki Papana & Anastasia Spyridou, 2020. "Bankruptcy Prediction: The Case of the Greek Market," Forecasting, MDPI, vol. 2(4), pages 1-21, December.
    3. Thomas E. Mckee, 2000. "Developing a bankruptcy prediction model via rough sets theory," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(3), pages 159-173, September.
    4. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    5. Michael Doumpos & Constantin Zopounidis, 1999. "A Multicriteria Discrimination Method for the Prediction of Financial Distress: The Case of Greece," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 71-101, June.
    6. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    7. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
    8. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    9. Zopounidis, Constantin & Doumpos, Michael, 2001. "A preference disaggregation decision support system for financial classification problems," European Journal of Operational Research, Elsevier, vol. 130(2), pages 402-413, April.
    10. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    11. Fejér-Király Gergely, 2015. "Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 3(1), pages 93-108, December.
    12. Zopounidis, C., 1999. "Multicriteria decision aid in financial management," European Journal of Operational Research, Elsevier, vol. 119(2), pages 404-415, December.
    13. García-Gallego, Ana & Mures-Quintana, María-Jesús, 2013. "La muestra de empresas en los modelos de predicción del fracaso: influencia en los resultados de clasificación || The Sample of Firms in Business Failure Prediction Models: Influence on Classification," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 133-150, June.
    14. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    15. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    16. Apostolos G. Christopoulos & Ioannis G. Dokas & Iraklis Kollias & John Leventides, 2019. "An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms," Bulletin of Applied Economics, Risk Market Journals, vol. 6(1), pages 1-20.
    17. McKee, Thomas E. & Lensberg, Terje, 2002. "Genetic programming and rough sets: A hybrid approach to bankruptcy classification," European Journal of Operational Research, Elsevier, vol. 138(2), pages 436-451, April.
    18. Wagner, Dirk Nicolas, 2020. "The nature of the Artificially Intelligent Firm - An economic investigation into changes that AI brings to the firm," Telecommunications Policy, Elsevier, vol. 44(6).
    19. Petr Jakubík & Petr Teplý, 2011. "The JT Index as an Indicator of Financial Stability of Corporate Sector," Prague Economic Papers, Prague University of Economics and Business, vol. 2011(2), pages 157-176.
    20. Chung-Ho Su, 2017. "A Novel Hybrid Learning Achievement Prediction Model: A Case Study in Gamification Education Applications (APPs)," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 515-543, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joiaen:v:10:y:2021:i:1:d:10.1186_s13731-021-00157-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.