IDEAS home Printed from https://ideas.repec.org/a/das/njaigs/v7y2024i01p304-319id361.html
   My bibliography  Save this article

A Decision Intelligence Framework: Integrating Human Intuition with Ai Models

Author

Listed:
  • Nazia Tasleem
  • Raghvendra Singh Raghav
  • Mohammed Nadeem Ansari
  • Anuradha Jaggi Sharma

Abstract

Decision intelligence (DI) has emerged as a transformative interdisciplinary field that integrates data science, social science, and managerial expertise to improve complex decision-making processes. As artificial intelligence (AI) continues to advance, its role in supporting strategic and operational decisions has expanded significantly, offering predictive insights, automated reasoning, and data-driven guidance across various sectors, including healthcare, finance, logistics, and governance. However, despite the impressive computational capabilities of AI, effective decision-making in real-world environments continues to depend on human judgment, which incorporates tacit knowledge, professional experience, contextual awareness, and ethical reasoning. The interplay between algorithmic intelligence and human intuition is especially critical when decisions involve ambiguity, moral considerations, or stakeholder implications that transcend quantifiable outcomes. research introduces a decision intelligence framework emphasizing a hybrid approach, merging AI-driven analytics with human-centric inputs to guide strategic decision pathways. The proposed model outlines a cyclical process where machine learning algorithms generate predictions, humans interpret these outputs within a contextual and ethical framework, and decisions are continuously refined through feedback loops. The implications of this model are significant, suggesting that organizations can enhance decision accuracy and agility by fostering a collaborative dynamic between intelligent systems and human experts. Ultimately, this framework advances the conversation about how decision intelligence can shape the future of high-stakes decision-making in an increasingly automated and data-intensive world.

Suggested Citation

  • Nazia Tasleem & Raghvendra Singh Raghav & Mohammed Nadeem Ansari & Anuradha Jaggi Sharma, 2024. "A Decision Intelligence Framework: Integrating Human Intuition with Ai Models," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 7(01), pages 304-319.
    • Handle: RePEc:das:njaigs:v:7:y:2024:i:01:p:304-319:id:361
    as

    Download full text from publisher

    File URL: https://newjaigs.com/index.php/JAIGS/article/view/361
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dominik Dellermann & Nikolaus Lipusch & Philipp Ebel & Jan Marco Leimeister, 2019. "Design principles for a hybrid intelligence decision support system for business model validation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(3), pages 423-441, September.
    2. Jiaxin Zhang & Yan Luximon & Yao Song, 2019. "The Role of Consumers’ Perceived Security, Perceived Control, Interface Design Features, and Conscientiousness in Continuous Use of Mobile Payment Services," Sustainability, MDPI, vol. 11(23), pages 1-16, December.
    Full references (including those not matched with items on IDEAS)

    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. Sonia Singh & Subhra Mondal & Lata Bajpai Singh & Kalyan Kumar Sahoo & Subhankar Das, 2020. "An Empirical Evidence Study of Consumer Perception and Socioeconomic Profiles for Digital Stores in Vietnam," Sustainability, MDPI, vol. 12(5), pages 1-30, February.
    2. Frank M. Fossen & Trevor McLemore & Alina Sorgner, 2024. "Artificial Intelligence and Entrepreneurship," Foundations and Trends(R) in Entrepreneurship, now publishers, vol. 20(8), pages 781-904, December.
    3. Basma Hamrouni & Abdelhabib Bourouis & Ahmed Korichi & Mohsen Brahmi, 2021. "Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability," Sustainability, MDPI, vol. 13(17), pages 1-28, September.
    4. Laddawan Kaewkitipong & Charlie Chen & Jiangxue Han & Peter Ractham, 2022. "Human–Computer Interaction (HCI) and Trust Factors for the Continuance Intention of Mobile Payment Services," Sustainability, MDPI, vol. 14(21), pages 1-15, November.
    5. Jung-Yi (Capacity) Lin & Chien-Cheng Chen, 2025. "Factors Influencing Consumer Buying Behavior for Smart Home Technologies," Sustainability, MDPI, vol. 17(7), pages 1-30, March.
    6. Stefan Stieglitz & Milad Mirbabaie & Nicholas R. J. Möllmann & Jannik Rzyski, 2022. "Collaborating with Virtual Assistants in Organizations: Analyzing Social Loafing Tendencies and Responsibility Attribution," Information Systems Frontiers, Springer, vol. 24(3), pages 745-770, June.
    7. Oussama Tounekti & Antonio Ruiz-Martínez & Antonio F. Skarmeta Gomez, 2022. "Research in Electronic and Mobile Payment Systems: A Bibliometric Analysis," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    8. Philipp Ebel & Matthias Söllner & Jan Marco Leimeister & Kevin Crowston & Gert-Jan Vreede, 2021. "Hybrid intelligence in business networks," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 313-318, June.
    9. Li, Yisheng & Zadehnoori, Iman & Jowhar, Ahmad & Wise, Sean & Laplume, Andre & Zihayat, Morteza, 2024. "Learning from Yesterday: Predicting early-stage startup success for accelerators through content and cohort dynamics," Journal of Business Venturing Insights, Elsevier, vol. 22(C).
    10. Montijn Ven & Paola Lara Machado & Alexia Athanasopoulou & Banu Aysolmaz & Oktay Turetken, 2023. "Key performance indicators for business models: a systematic review and catalog," Information Systems and e-Business Management, Springer, vol. 21(3), pages 753-794, September.
    11. Thorsten Schoormann & Maren Stadtländer & Ralf Knackstedt, 2022. "Designing business model development tools for sustainability—a design science study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 645-667, June.
    12. Siyu Long & Abdullah Al Mamun & Qing Yang & Jingzu Gao & Wan Mohd Hirwani Wan Hussain & Sayed Samer Ali Al Shami, 2023. "Modelling the mass adoption of mobile payment for e-hailing services using SEM-MGA," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-20, October.
    13. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    14. Jing Liu & Khairul Manami Kamarudin & Yuqi Liu & Jinzhi Zou & Jiaqi Zhang, 2022. "Developing a Behavior Change Framework for Pandemic Prevention and Control in Public Spaces in China," Sustainability, MDPI, vol. 14(4), pages 1-24, February.
    15. Jan Brocke & Alexander Maedche, 2019. "The DSR grid: six core dimensions for effectively planning and communicating design science research projects," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(3), pages 379-385, September.
    16. Sabakun Naher Shetu & Md. Muzahidul Islam & Sadia Islam Promi, 2022. "An Empirical Investigation of the Continued Usage Intention of Digital Wallets: The Moderating Role of Perceived Technological Innovativeness," Future Business Journal, Springer, vol. 8(1), pages 1-17, December.
    17. Milad Mirbabaie & Felix Brünker & Nicholas R. J. Möllmann Frick & Stefan Stieglitz, 2022. "The rise of artificial intelligence – understanding the AI identity threat at the workplace," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 73-99, March.
    18. 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).
    19. Abbasi, Ghazanfar Ali & Sandran, Thiviya & Ganesan, Yuvaraj & Iranmanesh, Mohammad, 2022. "Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA," Technology in Society, Elsevier, vol. 68(C).
    20. Bunhov Chov & Phichhang Ou, 2022. "Determinants of the consumer’s adoption of the next-generation mobile payments and banking: a case study of the Bakong system," SN Business & Economics, Springer, vol. 2(10), pages 1-38, October.

    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:das:njaigs:v:7:y:2024:i:01:p:304-319:id:361. 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: Open Knowledge (email available below). General contact details of provider: https://newjaigs.com/index.php/JAIGS/ .

    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.