Methodological Foundations of AI Readiness in Enterprise Operations and Analytics Functions

Sushrutha Sreevathsa

Sushrutha Sreevathsa

Keywords: AI readiness, enterprise operations, analytics functions, data governance, AI capability, operational intelligence, decision governance, data quality, organizational maturity, model lifecycle

Abstract

The article examines AI readiness as a methodological issue for enterprise operations and analytics functions. Its relevance follows from the gap between AI adoption ambitions and the organizational conditions required for reliable decision support, operational intelligence, and governed automation. The aim is to define the foundations that make enterprise AI usable beyond isolated pilots. The study applies comparative analysis, source analysis, conceptual synthesis, typologization, and analytical generalization to ten recent academic sources on AI readiness, AI capability, governance, data governance, and business value. The analytical part identifies three connected foundations: data and infrastructure reliability, organizational and process maturity, and decision-oriented governance. The article proposes an implementation logic that treats readiness as a managed operating condition measured through data quality, model lifecycle control, adoption behavior, accountability, and feedback loops. The results are applicable to enterprises preparing AI-enabled analytics, anomaly detection, forecasting, and operational decision support.

References

Ali, W., & Khan, A. Z. (2025). Factors influencing readiness for artificial intelligence: A systematic literature review. Data Science and Management, 8(2), 224–236. https://doi.org/10.1016/j.dsm.2024.09.005
Anomah, S. (2025). Assessing the institutional readiness and capacity for AI adoption in public audit institutions in developing countries: Evidence from Ghana. Telematics and Informatics Reports, 20, 100260. https://doi.org/10.1016/j.teler.2025.100260
Bernardo, B. M. V., Mamede, H. S., Barroso, J. M. P., & Dos Santos, V. M. P. D. (2024). Data governance & quality management—Innovation and breakthroughs across different fields. Journal of Innovation & Knowledge, 9(4), 100598. https://doi.org/10.1016/j.jik.2024.100598
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885
Schneider, J., Abraham, R., Meske, C., & Vom Brocke, J. (2023). Artificial intelligence governance for businesses. Information Systems Management, 40(3), 229–249. https://doi.org/10.1080/10580530.2022.2085825
Tursunbayeva, A., & Chalutz-Ben Gal, H. (2024). Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders. Business Horizons, 67(4), 357–368. https://doi.org/10.1016/j.bushor.2024.04.006
Van Noordt, C., & Tangi, L. (2023). The dynamics of AI capability and its influence on public value creation of AI within public administration. Government Information Quarterly, 40(4), 101860. https://doi.org/10.1016/j.giq.2023.101860
Weber, M., Engert, M., Schaffer, N., Weking, J., & Krcmar, H. (2023). Organizational capabilities for AI implementation—Coping with inscrutability and data dependency in AI. Information Systems Frontiers, 25(4), 1549–1569. https://doi.org/10.1007/s10796-022-10297-y