AI Capability Maturity Assessment Model for ServiceNow Enabled Digital Enterprise Transformation
Abstract
Artificial Intelligence (AI) has emerged as a strategic enabler to further speed up transformation in digital enterprises, especially when combined with enterprise workflow platforms, like ServiceNow. Nevertheless, there is no standard template that could be used in evaluating the AI readiness of these organisations, governance, operational maturity and realisation of business values. The paper presents an AI Capability Maturity Assessment Model (AI-CMAM) of ServiceNow-aware digital enterprise transformation, which has a systematic evaluation of the organisational AI capabilities in six maturity dimensions such as Strategy and Leadership, Data and Knowledge Management, AI Governance and Risk, Intelligent Automation, ServiceNow Platform Integration, and Workforce Readiness and Innovation. The provided framework proposes five maturity levels, such as Initial, Emerging, Defined, Managed and Optimized AI-driven digital services adoption and effectiveness. An existing set maturity scoring scheme and capability-assessment matrix are also presented to help determine organizational strengths and capabilities gaps and priorities of transformation. The framework co-shares a better approach through providing real-world advice on how to enhance AI control, workflow automation, predictive analytics, virtual agents and choice cleverness in ServiceNow ecosystem. By fuelling their digital processes with AI, the suggested model will allow businesses to map AI maturity, to streamline business processes, enhance compliance, and to extract maximum business value. The study provides a practical and scalable assessment model to assist businesses map the path maps of analytics applications and get an extended digital transformation with the assistance of smart ServiceNow platform choices
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
100-110 |
Published |
January 9, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Deeksha Sivakumer (%2026). AI Capability Maturity Assessment Model for ServiceNow Enabled Digital Enterprise Transformation. International Journal of Science, Research and Technology , Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 100-110. https://doi.org/10.15662/IJSRAT.2026.0901011 |
References
[2] J. Holmström and N. Carroll, “How organisations can innovate with generative AI,” Business Horizons, 2024, doi: 10.1016/j.bushor.2024.02.010.
[3] H. Takeuchi, J. H. Husen, H. T. Tun, H. Washizaki, and N. Yoshioka, “Enterprise architecture-based metamodel for machine learning projects and its management,” Future Generation Computer Systems, vol. 161, pp. 135–145, 2024, doi: 10.1016/j.future.2024.06.062.
[4] S. Bankins, A. C. Ocampo, M. Marrone, S. L. D. Restubog, and S. E. Woo, “A multilevel review of artificial intelligence in organisations: Implications for organisational behavior research and practice,” Journal of Organizational Behavior, vol. 45, no. 2, pp. 159–182, 2024, doi: 10.1002/job.2735.
[5] K. Singh, S. Chatterjee, and M. Mariani, “Applications of generative AI and future organisational performance: The mediating role of explorative and exploitative innovation and the moderating role of ethical dilemmas and environmental dynamism,” Technovation, vol. 133, Art. no. 103021, 2024, doi: 10.1016/j.technovation.2024.103021.
[6] K. Riemer and S. Peter, “Conceptualizing generative AI as style engines: Application archetypes and implications,” International Journal of Information Management, vol. 79, Art. no. 102824, 2024, doi: 10.1016/j.ijinfomgt.2024.102824.
[7] D. Denni-Fiberesima, “Navigating the generative AI-enabled enterprise architecture landscape: Critical success factors for AI adoption and strategic integration,” in Lecture Notes in Networks and Systems, vol. 1082, Cham, Switzerland: Springer, 2024, doi: 10.1007/978-3-031-67434-1_20.
[8] J. Schneider, R. Abraham, and C. Meske, “Governance of Generative Artificial Intelligence for Companies,” arXiv, 2024, doi: 10.48550/arXiv.2403.08802.
[9] J. Bughin, “Doing versus saying: Responsible AI among large firms,” AI & Society, 2024, doi: 10.1007/s00146-024-02014-x.
[10] R. Gupta and B. Rathore, “Exploring the generative AI adoption in service industry: A mixed-method analysis,” Journal of Retailing and Consumer Services, vol. 81, Art. no. 103997, 2024, doi: 10.1016/j.jretconser.2024.103997.
[11] N. E. Kamalabai, I. Donoghue, and L. Hannola, “Sustainable enterprise architecture: A critical imperative for substantiating artificial intelligence,” in Proc. Portland Int. Conf. Manage. Eng. Technol. (PICMET), Portland, OR, USA, 2024, pp. 1–12, doi: 10.23919/PICMET64035.2024.10653061.
[12] D. Sjödin, V. Parida, M. Palmié, and J. Wincent, “How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops,” Journal of Business Research, vol. 134, pp. 574–587, 2021, doi: 10.1016/j.jbusres.2021.05.009.
[13] R. van de Wetering, “The role of enterprise architecture-driven dynamic capabilities and operational digital ambidexterity in driving business value under the COVID-19 shock,” Heliyon, vol. 8, no. 11, Art. no. e11484, 2022, doi: 10.1016/j.heliyon.2022.e11484.
[14] M. Pattij, R. van de Wetering, and R. Kusters, “Enhanced digital transformation supporting capabilities through enterprise architecture management: A fsQCA perspective,” Digital Business, vol. 2, Art. no. 100036, 2022, doi: 10.1016/j.digbus.2022.100036.
[15] J. D. Rittelmeyer and K. Sandkuhl, “Effects of artificial intelligence on enterprise architectures: A structured literature review,” in Proc. IEEE 25th Int. Enterprise Distributed Object Computing Workshop (EDOCW), 2021, pp. 130–137, doi: 10.1109/EDOCW52865.2021.00042