Implementing Artificial Intelligence in Data-Driven Enterprises through a Ten-Phase Framework Based on Multiple Case Studies
Abstract
Purpose. To develop and empirically validate a ten-phase framework for the implementation of artificial intelligence (AI) in data-driven enterprises, conceptualizing AI adoption as a socio-technical transformation integrating strategic, technological, and human dimensions.
Method. Multiple case study design combined with Design Science Research. Data were collected from twelve enterprises across manufacturing, logistics, and healthcare sectors through interviews, workshops, process datasets, and surveys. Cross-case and within-case analyses were used to validate and refine the framework.
Findings. Organizations that completed all ten phases recorded measurable gains: +14% operational efficiency (OEE); –32% decision latency; +11% first-pass yield (FPY); >80% user adoption of AI-supported systems. Success depended less on algorithms and more on data integrity, strategic alignment, human participation, and embedded governance.
Theoretical implications: Integrates fragmented models (CRISP-DM, AI Maturity Models, TOE) into a unified, process-oriented framework; redefines AI maturity as a dynamic capability-building process; extends socio-technical systems theory by proving that human participation accelerates AI adoption.
Practical implications. Provides a replicable roadmap for scaling trustworthy and regulation-compliant AI aligned with the EU AI Act and ISO/IEC 42001. Demonstrates that data governance, competence development, and early compliance reduce failure risk and long-term cost.
Value. The first empirically validated end-to-end managerial framework linking strategy, data governance, human competence, and regulatory accountability into a single model for AI transformation. Bridges the gap between AI research and enterprise-level implementation.
Downloads
References
Baker, J. (2011). The technology–organization–environment framework. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information systems theory: Explaining and predicting our digital society (Vol. 1, pp. 231–245). Springer. https://doi.org/10.1007/978-1-4419-6108-2_12
Chen, Z. (2024). Responsible AI in organizational training: Applications, implications, and recommendations for future development. Human Resource Development Review, 23(4), 498–521.
Dudley, C. (2024). The rise of AI governance: Unpacking ISO/IEC 42001. Quality, 63(8), 27.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385
European Commission. (2024). Regulation (EU) 2024/1689 on Artificial Intelligence (AI Act). Official Journal of the European Union, L172, 1–84.
Gartner. (2024). AI Maturity Model & Roadmap Toolkit. Gartner Research Report.
George, B., & Wooden, O. S. (2025). Ethical AI and responsible innovation. In AI Empowered: Pioneering African American Entrepreneurship in the Digital Age (pp. 105–111). Emerald Publishing Limited. ISBN 978-1-83662-817-0 Barnes & Noble+1
Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337–355. https://doi.org/10.25300/MISQ/2013/37:2.3.
Halkias, D., Neubert, M., Thurman, P. W., & Harkiolakis, N. (2022). The multiple case study design: Methodology and application for management education. Routledge.
Herrera-Poyatos, A., Del Ser, J., de Prado, M. L., Wang, F. Y., Herrera-Viedma, E., & Herrera, F. (2025). Responsible artificial intelligence systems: A roadmap to society’s trust through trustworthy AI, auditability, accountability, and governance. arXiv preprint. arXiv:2503.04739. https://arxiv.org/abs/2503.04739
ISO/IEC. (2017). ISO/IEC 38505-1:2017 – Governance of data – Part 1: Application of ISO/IEC 38500 to the governance of data. International Organization for Standardization.
ISO/IEC. (2018). ISO/IEC 20889:2018 – Privacy enhancing data de-identification terminology and classification of techniques. International Organization for Standardization.
ISO/IEC. (2023). ISO/IEC 42001:2023 – Artificial intelligence management system – Requirements. International Organization for Standardization.
Mecca, A. (2025). The influence of artificial intelligence implementation on firm scaling: An exploratory approach.
Morley, J., Kinsey, L., Elhalal, A., Garcia, F., Ziosi, M., & Floridi, L. (2023). Operationalising AI ethics: Barriers, enablers and next steps. AI & Society, 38(1), 411–423. https://doi.org/10.1007/s00146-022-01485-5.
OECD. (2024). OECD Framework for the Classification of AI Systems. Organisation for Economic Co-operation and Development. Available at https://www.oecd.org/
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2019.0574.
Sadeghi Moghadam, M. R., Ghasemnia Arabi, N., & Khoshsima, G. (2021). A review of case study method in operations management research. International Journal of Qualitative Methods, 20, Article 16094069211010088. https://doi.org/10.1177/16094069211010088.
Sajadieh, S. M. M., & Noh, S. D. (2025). From simulation to autonomy: Reviews of the integration of artificial intelligence and digital twins. International Journal of Precision Engineering and Manufacturing-Green Technology, 1–32.
van der Aalst, W. (2024). How object-centric process mining helps to unleash predictive and generative AI. In Process Intelligence in Action: Taking Process Mining to the Next Level (pp. 219–232).
Wilhelm, J., Petzoldt, C., Beinke, T., & Freitag, M. (2021). Review of digital twin-based interaction in smart manufacturing: Enabling cyber-physical systems for human-machine interaction. International Journal of Computer Integrated Manufacturing, 34(10), 1031–1048. https://doi.org/10.1080/0951192X.2020.1864976.
Abstract views: 232 PDF Downloads: 148
Copyright (c) 2025 Karol Jędrasiak, Piotr Gawliczek
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors agree with the following conditions:
1. Authors retain copyright and grant the journal right of first publication (Download agreement) with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors have the right to complete individual additional agreements for the non-exclusive spreading of the journal’s published version of the work (for example, to post work in the electronic repository of the institution or to publish it as part of a monograph), with the reference to the first publication of the work in this journal.
3. Journal’s politics allows and encourages the placement on the Internet (for example, in the repositories of institutions, personal websites, SSRN, ResearchGate, MPRA, SSOAR, etc.) manuscript of the work by the authors, before and during the process of viewing it by this journal, because it can lead to a productive research discussion and positively affect the efficiency and dynamics of citing the published work (see The Effect of Open Access).
