Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

The Business Analyst as an AI-Transformation Broker: A Framework for Stakeholder Alignment, Responsible Automation, And Value Realization in Small and Medium-Sized Enterprises

Joseph Idanesi Alieme, Aumbur Sule, Valentina Ochuko Obukadata

Abstract

Small and medium-sized enterprises adopting artificial intelligence rarely fail because a model does not work. They fail at the seams: a process is automated that nobody agreed on, a model is built on data never fit for the decision it informs, a governance checklist is satisfied without any design decision changing, and a benefit is declared that nobody measured. This paper argues that these are boundary failures that is, knowledge that did not survive transfer between groups who do not share a frame, and that they are best understood through Carlile's syntactic, semantic and pragmatic boundary hierarchy. Large firms answer boundary failure structurally, through data offices, governance functions, transformation offices and model-risk teams; a smaller firm has none of these, yet the brokering work does not disappear, it is either performed by a role or not at all. The paper argues, from role positioning rather than from empirical demonstration, that the business analyst is the role structurally placed to perform it, and develops a framework of four brokering functions consisting of translation, legitimation, constraint injection and benefit tracing, specifying for each what is moved, across which boundary, which boundary objects are produced, what competence is required, and what fails without it. Six tables set out the functions, a boundary- by-phase matrix, the substitutions smaller firms must make, a benefits dependency structure, the pathologies of brokering, and the traditions grounding each function. An applied-literature survey shows the functions surfacing across domains, and eight propositions are offered for testing.

Keywords

business analysis; boundary spanning; intelligent process automation; small and medium-sized enterprises; AI governance; benefits realization; stakeholder salience

References

Adebayo, A., Adegbite, M. P., & Ahmed, M. O. (2022). Adversarial machine learning in critical infrastructure: A conceptual framework for threat modeling AI enabled OT systems. World Journal of Innovation and Modern Technology, 6(1), 184–234. https://doi.org/10.56201/wjimt.v6.no1.2022.pg184.234 Adegbite, M. P., Adebayo, A., & Ahmed, M. O. (2022). A security architecture model for IT and OT convergence in regulated energy networks: Design principles and governance alignment. International Journal of Computer Science and Mathematical Theory, 8(2), 81– 132. https://doi.org/10.56201/ijcsmt.v8.no2.2022.pg81.132 Adegbite, M. P., Adebayo, A., & Ahmed, M. O. (2025). A cyber risk quantification and governance architecture for critical infrastructure: From posture measurement to executive reporting. International Journal of Computer Science and Mathematical Theory, 11(12), 232–293. https://doi.org/10.56201/ijcsmt..pg232.293 Adelanwa, A., Basnet, A., & Anene, U. N. (2023b). Predictive analytics models for financial risk detection and fraud prevention in public systems. International Journal of Advanced Multidisciplinary Research and Studies, 3(6). https://doi.org/10.62225/2583049X.2023.3.6.5969 Adelanwa, A., Basnet, A., & Anene, U. N. (2024). Performance intelligence models for optimization and outcome measurement in large scale public services. Shodhshauryam, International Scientific Refereed Research Journal, 6(1). Adeyelu, O. O., & Dagodzo, D. (2024). A maturity model for predicting airport safety audit outcomes in resource-constrained regulatory environments. International Journal of Scientific Research in Civil Engineering, 8(4), 132–170. https://doi.org/10.32628/IJSRCE248423 Afrihyia, E., Akinse, S. G., & Ojukwu, P. U. (2025). Organizational readiness for generative AI integration in healthcare operations: Comparative management capabilities between the U.S. and low- and middle-income countries. Iconic Research and Engineering Journals, 8(10), 1673–1697. https://doi.org/10.64388/IREV8I10-1714670 Agu, M. U., Akomolafe, O., & Bello, A. (2023a). A comparative review of SOX compliance frameworks in cross-border financial auditing. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2297–2306. https://doi.org/10.62225/2583049X.2023.3.6.5362 Ahmad, K., Abdelrazek, M., Arora, C., Bano, M., & Grundy, J. (2023). Requirements engineering for artificial intelligence systems: A systematic mapping study. Information and Software Technology, 158, Article 107176. https://doi.org/10.1016/j.infsof.2023.107176 Akin-Oluyomi, O. T., Atima, M. E., & Akinleye, O. K. (2023b). Regulatory compliance and supplier risk assessment frameworks in international pharmaceutical procurement. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2194– 2204. Akinleye, O. K., & Adeyoyin, O. (2021). Process automation framework for enhancing procurement efficiency and transparency. Shodhshauryam, International Scientific Refereed Research Journal, 4(4), 356–387. Akinleye, O. K., Okoruwa, P. O., Babatope, O. M., & Akokodaripon, D. A. (2023). Leveraging big data and business intelligence for optimization of manufacturing sector procurement. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2164– 2172. P-ISSN 2695-186X Akintola, A. S., Fawehinmi, Y. O., Aiyenitaju, O., Chinemerem, B., & Emmanuella, O. (2025). Digital transformation in telehealth: A systematic review of user trust, privacy, and regulatory governance in AI-powered remote monitoring systems. Journal of Scientific Research and Reports, 31(11), 163–182. https://doi.org/10.9734/jsrr/2025/v31i113658 Akomolafe, O., & Agu, M. U. (2018). A conceptual model for enhancing internal audit quality through technology-enabled risk assessment frameworks. Iconic Research and Engineering Journals, 1(9), 458–475. Akomolafe, O., & Agu, M. U. (2019c). Advances in financial resilience through integrated governance and compliance strategies. Iconic Research and Engineering Journals, 2(10), 607–620. Akomolafe, O., Agu, M. U., & Bello, A. (2023a). A conceptual model for implementing risk-based auditing in strategic financial management. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2274–2286. https://doi.org/10.62225/2583049X.2023.3.6.5359 Akomolafe, O., Olaogun, B. O., Adesuyi, M. O., Ndukwe, V. U., & Sakyi, J. K. (2025a). Collaborative governance framework for secure cross-border payment data sharing. International Journal of Advanced Multidisciplinary Research and Studies, 5(6), 849–865. https://doi.org/10.62225/2583049X.2025.5.6.5283 Aldrich, H., & Herker, D. (1977). Boundary spanning roles and organization structure. The Academy of Management Review, 2(2), 217. https://doi.org/10.2307/257905 Aliliele, C., Mbonu, I. S., & Iwuanyanwu, U. (2023a). A conceptual framework for continuous cloud misconfiguration monitoring and enterprise risk mitigation strategies. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(10), 373–394. https://doi.org/10.32628/CSEIT2361071 Aliliele, C., Mbonu, I. S., & Iwuanyanwu, U. (2023b). A review of API governance and risk prioritization frameworks in modern financial institutions. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(10), 395–433. https://doi.org/10.32628/CSEIT2361072 Aliliele, C., Mbonu, I. S., & Iwuanyanwu, U. (2024a). A conceptual framework for enterprise data sensitivity classification and regulatory traceability mechanisms. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 3103–3124. https://doi.org/10.62225/2583049X.2024.4.6.5991 Aliliele, C., Mbonu, I. S., & Iwuanyanwu, U. (2024b). Advances in HIPAA compliant data architecture and secure analytics frameworks for community healthcare organizations. Shodhshauryam, International Scientific Refereed Research Journal, 7(2), 277–324. https://doi.org/10.32628/SHISRRJ2472163 Aliliele, C., Mbonu, I. S., Uzoka, E., & Iwuanyanwu, U. (2025a). A review of AI assisted continuous auditing systems in technology risk and cybersecurity oversight. Gyanshauryam, International Scientific Refereed Research Journal, 8(4), 210–250. https://doi.org/10.32628/GISRRJ258369 Alsheibani, S., Cheung, Y., & Messom, C. (2018). Artificial intelligence adoption: AI-readiness at firm-level. In Proceedings of the 22nd Pacific Asia Conference on Information Systems (PACIS 2018), Paper 37. Amayo, E. B., Owulade, O. A., & Isi, L. R. (2023a). Optimizing project governance in multinational infrastructure projects: Insights from General Electric's global operations. P-ISSN 2695-186X International Journal of Multidisciplinary Research and Growth Evaluation, 4(1), 975– 983. https://doi.org/10.54660/.IJMRGE.2023.4.1.975-983 Amayo, E. B., Owulade, O. A., & Isi, L. R. (2024b). Effective project governance in multinational infrastructure projects: A case study from General Electric. Iconic Research and Engineering Journals, 8(5), 1305–1324. Ambali, K. B., Eyetsemitan, R. A., Oyeleye, A. O., & Fadayomi, O. (2021). Lean Six Sigma for small enterprises: A systematic review and Lite-DMAIC adaptation framework for resource-constrained organizations. Iconic Research and Engineering Journals, 5(5), 562– 583. https://doi.org/10.64388/IREV5I5-1716957 Aminu-Ibrahim, A. Y., Ogbete, J. C., & Ambali, K. B. (2024). Governance and accountability models for public private partnerships in healthcare infrastructure development. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 2943– 2960. https://doi.org/10.62225/2583049X.2024.4.6.5699 Aminu-Ibrahim, A. Y., Ogbete, J. C., & Iwuanyanwu, O. C. (2025c). Sustainable healthcare infrastructure performance metrics for long-term asset management and value creation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(4), 566–601. https://doi.org/10.32628/CSEIT251116277 Annan, A. O. (2023). Intellectual property ownership and authorship in AI-generated works. Gyanshauryam, International Scientific Refereed Research Journal, 6(3), 425–450. Annan, A. O. (2024). Algorithmic accountability and trade secret protection in artificial intelligence. Shodhshauryam, International Scientific Refereed Research Journal, 7(5), 315–347. Annan, A. O. (2025a). Automated decision-making and anti-discrimination compliance under U.S. law. International Journal of Advanced Multidisciplinary Research and Studies, 5(2), 2522–2540. Aranda, J., Easterbrook, S., & Wilson, G. (2007). Requirements in the wild: How small companies do it. In 15th IEEE International Requirements Engineering Conference (RE 2007) (pp. 39–48). IEEE. https://doi.org/10.1109/RE.2007.54 Argote, L., & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational Behavior and Human Decision Processes, 82(1), 150–169. https://doi.org/10.1006/obhd.2000.2893 Asatiani, A., Penttinen, E., Ruissalo, J., & Salovaara, A. (2020). Knowledge workers' reactions to a planned introduction of robotic process automation: Empirical evidence from an accounting firm. In Information systems outsourcing (Progress in IS, pp. 413–452). Springer. https://doi.org/10.1007/978-3-030-45819-5_17 Ashmore, R., Calinescu, R., & Paterson, C. (2021). Assuring the machine learning lifecycle: Desiderata, methods, and challenges. ACM Computing Surveys, 54(5), 1–39. https://doi.org/10.1145/3453444 Ashurst, C., Doherty, N. F., & Peppard, J. (2008). Improving the impact of IT development projects: The benefits realization capability model. European Journal of Information Systems, 17(4), 352–370. https://doi.org/10.1057/ejis.2008.33 Atakpa, M. I., & Abolaji, T. O. (2022). A privacy-preserving data architecture model for regulated industry analytics under GDPR and HIPAA compliance. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(3), 781–808. https://doi.org/10.32628/CSEIT22558 P-ISSN 2695-186X Atima, M. E., Sanni, J. O., & Attah, A. (2022). Predictive audience segmentation models resolving targeting inefficiencies in regulated professional service enterprises. Shodhshauryam, International Scientific Refereed Research Journal, 5(1), 271–303. https://doi.org/10.32628/SHISRRJ247134 Ayinaddis, S. G. (2025). Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis. Journal of Innovation & Knowledge, 10(3), 100682. https://doi.org/10.1016/j.jik.2025.100682 Badewi, A. (2016). The impact of project management (PM) and benefits management (BM) practices on project success: Towards developing a project benefits governance framework. International Journal of Project Management, 34(4), 761–778. https://doi.org/10.1016/j.ijproman.2015.05.005 Badmus, O., Dosunmu, A. A., & Anunagba, C. O. (2025). A governance framework for AI- assisted CRM workflows: Agentforce deployment, risk management, and organizational readiness in enterprise Salesforce environments. International Journal of Scientific Research in Humanities and Social Sciences, 2(1), 59–80. Badmus, O., Dosunmu, A. A., & Ozowara, D. E. (2018). A systematic review of CI/CD pipeline strategies in Salesforce DevOps: Tools, practices, and deployment outcomes. Iconic Research and Engineering Journals, 2(6). Badmus, O., Dosunmu, A. A., & Ozowara, D. E. (2019a). A conceptual model for ETL design and data integration in Salesforce-centric enterprise architectures. Iconic Research and Engineering Journals, 3(5). Badmus, O., Dosunmu, A. A., Ozowara, D. E., & Anunagba, C. O. (2020). A comparative framework for Salesforce DevOps tooling: Evaluating Copado, Flosum, and Salesforce DX across enterprise deployment contexts. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 1021–1031. https://doi.org/10.54660/.IJMRGE.2020.1.5.1021-1031 Badmus, O., Jooda, D., & Anunagba, C. O. (2024). A privacy-by-design framework for role-based security architecture in Salesforce environments handling sensitive personal data. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 3244– 3254. https://doi.org/10.62225/2583049X.2024.4.6.6243 Badmus, O., Jooda, D., Ozowara, D. E., & Anunagba, C. O. (2022b). A systematic review of MuleSoft ESB integration patterns in multi-cloud Salesforce architectures. Gyanshauryam, International Scientific Refereed Research Journal, 5(3), 462–481. Baier, L., Jöhren, F., & Seebacher, S. (2019). Challenges in the deployment and operation of machine learning in practice. In Proceedings of the 27th European Conference on Information Systems (ECIS 2019), Research Paper 163. Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779. https://doi.org/10.1016/0005-1098(83)90046-8 Bano, M., & Zowghi, D. (2015). A systematic review on the relationship between user involvement and system success. Information and Software Technology, 58, 148–169. https://doi.org/10.1016/j.infsof.2014.06.011 Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31 Barrett, M., & Oborn, E. (2010). Boundary object use in cross-cultural software development teams. Human Relations, 63(8), 1199–1221. https://doi.org/10.1177/0018726709355657 P-ISSN 2695-186X Bassellier, G., & Benbasat, I. (2004). Business competence of information technology professionals: Conceptual development and influence on IT–business partnerships. MIS Quarterly, 28(4), 673–694. https://doi.org/10.2307/25148659 Batoulis, K., Meyer, A., Bazhenova, E., Decker, G., & Weske, M. (2015). Extracting decision logic from process models. In Advanced Information Systems Engineering (CAiSE 2015) (LNCS Vol. 9097, pp. 349–366). Cham: Springer. https://doi.org/10.1007/978-3-319- 19069-3_22 Bechky, B. A. (2003). Sharing meaning across occupational communities: The transformation of understanding on a production floor. Organization Science, 14(3), 312–330. https://doi.org/10.1287/orsc.14.3.312.15162 Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., & Eckersley, P. (2020). Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 648–657). Association for Computing Machinery. https://doi.org/10.1145/3351095.3375624 Board of Governors of the Federal Reserve System & Office of the Comptroller of the Currency. (2011). Supervisory guidance on model risk management (SR Letter 11-7). Board of Governors of the Federal Reserve System. https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm Bonney, K., Breaux, C., Buffington, C., Dinlersoz, E., Foster, L., Goldschlag, N., Haltiwanger, J., Kroff, Z., & Savage, K. (2024). Tracking firm use of AI in real time: A snapshot from the Business Trends and Outlook Survey (CES Working Paper No. CES-24-16). US Census Bureau, Center for Economic Studies. https://www.census.gov/library/working- papers/2024/adrm/CES-WP-24-16.html Bourne, M., Mills, J., Wilcox, M., Neely, A., & Platts, K. (2000). Designing, implementing and updating performance measurement systems. International Journal of Operations & Production Management, 20(7), 754–771. https://doi.org/10.1108/01443570010330739 Brown, J. S., & Duguid, P. (1991). Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organization Science, 2(1), 40–57. https://doi.org/10.1287/orsc.2.1.40 Brynjolfsson, E. (1993). The productivity paradox of information technology. Communications of the ACM, 36(12), 66–77. https://doi.org/10.1145/163298.163309 Brynjolfsson, E., & Hitt, L. M. (2000). Beyond computation: Information technology, organizational transformation and business performance. Journal of Economic Perspectives, 14(4), 23–48. https://doi.org/10.1257/jep.14.4.23 Brynjolfsson, E., Rock, D., & Syverson, C. (2019). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In A. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 23–60). Chicago: University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.003.0001 Burton, J. W., Stein, M.-K., & Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220–239. https://doi.org/10.1002/bdm.2155 By, R. T. (2005). Organisational change management: A critical review. Journal of Change Management, 5(4), 369–380. https://doi.org/10.1080/14697010500359250 Carlile, P. R. (2002). A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organization Science, 13(4), 442–455. https://doi.org/10.1287/orsc.13.4.442.2953 P-ISSN 2695-186X Carlile, P. R. (2004). Transferring, translating, and transforming: An integrative framework for managing knowledge across boundaries. Organization Science, 15(5), 555–568. https://doi.org/10.1287/orsc.1040.0094 Cecez-Kecmanovic, D., Kautz, K., & Abrahall, R. (2014). Reframing success and failure of information systems: A performative perspective. MIS Quarterly, 38(2), 561–588. https://doi.org/10.25300/misq/2014/38.2.11 Chakraborti, T., Isahagian, V., Khalaf, R., Khazaeni, Y., Muthusamy, V., Rizk, Y., & Unuvar, M. (2020). From robotic process automation to intelligent process automation: Emerging trends. In Business process management: Blockchain and robotic process automation forum (Lecture Notes in Business Information Processing, pp. 215–228). Springer. https://doi.org/10.1007/978-3-030-58779-6_15 Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553 Cooper, L. A., Holderness, D. K., Jr., Sorensen, T. L., & Wood, D. A. (2019). Robotic process automation in public accounting. Accounting Horizons, 33(4), 15–35. https://doi.org/10.2308/acch-52466 Costanza-Chock, S., Raji, I. D., & Buolamwini, J. (2022). Who audits the auditors? Recommendations from a field scan of the algorithmic auditing ecosystem. In 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1571–1583). Association for Computing Machinery. https://doi.org/10.1145/3531146.3533213 Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 Deloitte. (2018). The robots are ready. Are you? Untapped advantage in your digital workforce (Deloitte Global RPA Survey). Deloitte LLP. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60 DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 Diakopoulos, N. (2015). Algorithmic accountability: Journalistic investigation of computational power structures. Digital Journalism, 3(3), 398–415. https://doi.org/10.1080/21670811.2014.976411 Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033 Doherty, N. F., & King, M. (2001). An investigation of the factors affecting the successful treatment of organisational issues in systems development projects. European Journal of Information Systems, 10(3), 147–160. https://doi.org/10.1057/palgrave.ejis.3000401 Doherty, N. F., Ashurst, C., & Peppard, J. (2012). Factors affecting the successful realisation of benefits from systems development projects: Findings from three case studies. Journal of Information Technology, 27(1), 1–16. https://doi.org/10.1057/jit.2011.8 Donaldson, T., & Preston, L. E. (1995). The stakeholder theory of the corporation: Concepts, evidence, and implications. Academy of Management Review, 20(1), 65–91. https://doi.org/10.5465/amr.1995.9503271992 P-ISSN 2695-186X Dosunmu, A. A., & Ogundele, P. O. (2024a). Breach and attack simulation frameworks for continuous validation of enterprise security controls. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(3), 1100– 1119. Dosunmu, A. A., & Ogundele, P. O. (2024b). Cyber risk quantification models for prioritizing enterprise security investment decisions. International Journal of Multidisciplinary Research and Growth Evaluation, 5(6), 1777–1785. https://doi.org/10.54660/.IJMRGE.2024.5.6.1777-1785 Dosunmu, A. A., & Ogundele, P. O. (2025b). Cyber defense performance measurement frameworks for executive and board level governance. Computer Science and IT Research Journal, 6(11), 895–913. https://doi.org/10.51594/csitrj.v6i11.2164 Ebhojie, O., Dogbatsey, E. A., & Oyeleye, A. O. (2023a). Audit liaison, corrective action planning, and control compliance in multinational organisations: A systematic literature review. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2839– 2850. https://doi.org/10.62225/2583049X.2023.3.6.6200 Edivri, J., & Oteri, O. (2024). A conceptual KPI-driven decision and optimization framework for IT service delivery, portfolio performance, and adoption. Gyanshauryam, International Scientific Refereed Research Journal, 7(1), 167–187. https://doi.org/10.32628/GISRRJ246643 Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135–153. https://doi.org/10.1016/j.accinf.2008.03.001 Eller, R., Alford, P., Kallmünzer, A., & Peters, M. (2020). Antecedents, consequences, and challenges of small and medium-sized enterprise digitalization. Journal of Business Research, 112, 119–127. https://doi.org/10.1016/j.jbusres.2020.03.004 Enríquez, J. G., Jiménez-Ramírez, A., Domínguez-Mayo, F. J., & García-García, J. A. (2020). Robotic process automation: A scientific and industrial systematic mapping study. IEEE Access, 8, 39113–39129. https://doi.org/10.1109/access.2020.2974934 European Parliament and Council. (2024). Regulation (EU) 2024/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L, 12 July 2024. http://data.europa.eu/eli/reg/2024/1689/oj Eurostat. (2025a). 20% of EU enterprises use AI technologies. Eurostat News, 11 December 2025. https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251211-2 Eurostat. (2025b). Use of artificial intelligence in enterprises. Statistics Explained. https://ec.europa.eu/eurostat/statistics- explained/index.php?title=Useofartificialintelligencein_enterprises EY. (2016). Get ready for robots: Why planning makes the difference between success and disappointment. EYGM Limited. Eyetsemitan, R. A., Ambali, K. B., Oyeleye, A. O., & Fadayomi, O. (2020). Multi-stakeholder governance alignment in joint venture operations: A conceptual framework for coordinating business processes in highly regulated environments. Iconic Research and Engineering Journals, 4(4), 418–441. https://doi.org/10.64388/IREV4I4-1716955 Eyetsemitan, R. A., Ambali, K. B., Oyeleye, A. O., & Fadayomi, O. (2021). Translating tax and regulatory requirements into SME compliance workflows: A conceptual framework for P-ISSN 2695-186X implementing IAS 12, VAT, PAYE, and withholding tax. Iconic Research and Engineering Journals, 4(11), 621–641. https://doi.org/10.64388/IREV4I11-1716956 Eyetsemitan, R. A., Ambali, K. B., Oyeleye, A. O., & Fadayomi, O. (2023b). User acceptance testing in small business technology deployment: A structured validation framework for lean operational environments. International Journal of Multidisciplinary Research and Growth Evaluation, 4(6), 1512–1531. https://doi.org/10.54660/IJMRGE.2023.4.6.1512- 1531 Eyetsemitan, R. A., Ambali, K. B., Oyeleye, A. O., & Fadayomi, O. (2025). An integrated lean- digital framework for scaling small business operations: Synthesizing SOP design, automation, compliance, and change management. International Journal of Multidisciplinary Research and Growth Evaluation, 6(6), 1341–1360. https://doi.org/10.54660/IJMRGE.2025.6.6.1341-1360 Eyetsemitan, R. A., Oyeleye, A. O., Ambali, K. B., & Fadayomi, O. (2022). Standard operating procedures as strategic assets in small business operations: A systematic review and implementation framework. Gyanshauryam, International Scientific Refereed Research Journal, 5(2), 438–465. https://doi.org/10.32628/GISRRJ225356 Eyetsemitan, R. A., Oyeleye, A. O., Ambali, K. B., & Fadayomi, O. (2024a). CRM and workflow automation in small healthcare practices: A process efficiency framework for scalable patient engagement. International Journal of Multidisciplinary Research and Growth Evaluation, 5(6), 1931–1949. https://doi.org/10.54660/IJMRGE.2024.5.6.1931-1949 Femmer, H., Méndez Fernández, D., Wagner, S., & Eder, S. (2017). Rapid quality assurance with requirements smells. Journal of Systems and Software, 123, 190–213. https://doi.org/10.1016/j.jss.2016.02.047 Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... Vayena, E. (2018). AI4People: An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5 Fobellah, A. N. (2025b). Navigating digital transformation: Auditing artificial intelligence- powered financial systems: A conceptual review. International Journal of Science and Research Archive, 16(2), 23–28. https://doi.org/10.30574/ijsra.2025.16.2.2274 Freeman, R. E. (1984). Strategic management: A stakeholder approach. Boston: Pitman. Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2021). Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. MIS Quarterly, 45(3), 1527–1556. https://doi.org/10.25300/misq/2021/16553 Gebru, T., Morgenstern, J., Vecchione, B., Wortman Vaughan, J., Wallach, H., Daumé, H., III, & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86– 92. https://doi.org/10.1145/3458723 Gotel, O. C. Z., & Finkelstein, A. C. W. (1994). An analysis of the requirements traceability problem. In Proceedings of the IEEE International Conference on Requirements Engineering (ICRE '94) (pp. 94–101). IEEE. https://doi.org/10.1109/ICRE.1994.292398 Green, B. (2022). The flaws of policies requiring human oversight of government algorithms. Computer Law & Security Review, 45, 105681. https://doi.org/10.1016/j.clsr.2022.105681 Grønsund, T., & Aanestad, M. (2020). Augmenting the algorithm: Emerging human-in-the-loop work configurations. The Journal of Strategic Information Systems, 29(2), 101614. https://doi.org/10.1016/j.jsis.2020.101614 P-ISSN 2695-186X Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8 Hargadon, A., & Sutton, R. I. (1997). Technology brokering and innovation in a product development firm. Administrative Science Quarterly, 42(4), 716–749. https://doi.org/10.2307/2393655 Hasić, F., De Smedt, J., & Vanthienen, J. (2018). Augmenting processes with decision intelligence: Principles for integrated modelling. Decision Support Systems, 107, 1–12. https://doi.org/10.1016/j.dss.2017.12.008 Herm, L.-V., Janiesch, C., Helm, A., Imgrund, F., Hofmann, A., & Winkelmann, A. (2023). A framework for implementing robotic process automation projects. Information Systems and e-Business Management, 21(1), 1–35. https://doi.org/10.1007/s10257-022-00553-8 High-Level Expert Group on Artificial Intelligence. (2019). Ethics Guidelines for Trustworthy AI. Brussels: European Commission. https://digital-strategy.ec.europa.eu/en/library/ethics- guidelines-trustworthy-ai Iacovou, C. L., Benbasat, I., & Dexter, A. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19(4), 465–485. https://doi.org/10.2307/249629 IBM. (2023). IBM Global AI Adoption Index 2023: Enterprise report. Armonk, NY: IBM. Ike, P. N., Aifuwa, S. E., Nnabueze, S. B., Olatunde-Thorpe, J., Ogbuefi, E., Oshoba, T. O., & Akokodaripon, D. (2024a). Quantitative risk architecture for public-private partnerships: A multi-layered model for allocating public and private risk. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 2669–2682. https://doi.org/10.62225/2583049X.2024.4.6.5021 Ike, P. N., Ogbuefi, E., Nnabueze, S. B., Olatunde-Thorpe, J., Aifuwa, S. E., Oshoba, T. O., & Akokodaripon, D. (2021). Supplier relationship management strategies fostering innovation, collaboration, and resilience in global supply chain ecosystems. International Journal of Multidisciplinary Evolutionary Research, 2(2), 52–62. https://doi.org/10.54660/IJMER.2021.2.2.52-62 Ilodigwe, L., & Adesemoye, A. C. (2021). The data backbone of health system transformation: A governance and architecture framework for interoperability, data quality, and advanced analytics at national scale. International Journal of Health and Pharmaceutical Research, 6(2), 52–76. https://doi.org/10.56201/ijhpr..pg52.76 Ilodigwe, L., & Adesemoye, A. C. (2025a). Advances, risks, and implementation challenges of artificial intelligence as a force multiplier for clinical decision making and health system efficiency. International Journal of Medical Evaluation and Physical Report, 9(7), 165– 185. https://doi.org/10.56201/ijmepr.v9.no7.2025.pg165.185 Ilodigwe, L., & Adesemoye, A. C. (2025b). Executing healthcare transformation at scale: A strategic change model derived from large programs across payers, providers, and integrated health systems. International Journal of Health and Pharmaceutical Research, 10(12), 253–272. https://doi.org/10.56201/ijhpr..pg253.272 Institute of Electrical and Electronics Engineers. (2021). IEEE standard model process for addressing ethical concerns during system design (IEEE Std 7000-2021). IEEE. https://standards.ieee.org/ieee/7000/6781/ International Institute of Business Analysis. (2015). A Guide to the Business Analysis Body of Knowledge (BABOK Guide) (Version 3). Toronto: International Institute of Business Analysis. P-ISSN 2695-186X International Organization for Standardization. (2018). ISO/IEC/IEEE 29148:2018 Systems and software engineering Life cycle processes: Requirements engineering (2nd ed.). Geneva: ISO. https://www.iso.org/standard/72089.html International Organization for Standardization. (2023a). ISO/IEC 23894:2023 Information technology Artificial intelligence Guidance on risk management. Geneva: ISO. https://www.iso.org/standard/77304.html International Organization for Standardization. (2023b). ISO/IEC 42001:2023 Information technology Artificial intelligence Management system. Geneva: ISO. https://www.iso.org/standard/42001 Isiekwu, C. P., Oluwo, K., & Dada, T. (2021). A conceptual model for CFO-led strategic finance in joint venture and cross-border partnerships. Iconic Research and Engineering Journals, 4(7), 383–400. https://doi.org/10.64388/IREV4I7-1714350 Isiekwu, C. P., Oluwo, K., & Dada, T. (2025). A post-pandemic strategic collaboration model for banks and capital markets: Addressing resilience, recovery, and growth challenges. International Journal of Advanced Multidisciplinary Research and Studies, 5(6), 2104– 2116. Jimoh, H. O., Ahmed, M. O., & Fagbade, M. O. (2023). The role of frameworks in cybersecurity governance. Journal of Behavioural Informatics, Digital Humanities and Development Research, 9(4), 7–16. https://doi.org/10.22624/AIMS/BHI/V9N4P2 Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2 Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes: An interview study of organizational AI readiness factors. Business & Information Systems Engineering, 63(1), 5–20. https://doi.org/10.1007/s12599-020-00676-7 Jones, B. C., & Ominyi, M. (2025a). Conceptualizing AI-driven advisory services for expanding trade compliance access to small and medium enterprises. IIARD International Journal of Economics and Business Management, 11(12), 441–474. https://doi.org/10.56201/ijebm..pg441.474 Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, January–February 1992. Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard: Translating strategy into action. Boston, MA: Harvard Business School Press. Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2022). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), 1–38. https://doi.org/10.1145/3491209 Kinkel, S., Baumgartner, M., & Cherubini, E. (2022). Prerequisites for the adoption of AI technologies in manufacturing: Evidence from a worldwide sample of manufacturing companies. Technovation, 110, 102375. https://doi.org/10.1016/j.technovation.2021.102375 Kohli, R., & Grover, V. (2008). Business value of IT: An essay on expanding research directions to keep up with the times. Journal of the Association for Information Systems, 9(1), Article 1. https://doi.org/10.17705/1jais.00147 Komi, N. M. (2023). When accurate forecasts are not fair forecasts: Deep learning load prediction and equity across districts. Shodhshauryam, International Scientific Refereed Research Journal, 6(6), 476–546. https://doi.org/10.32628/SHISRRJ247102 P-ISSN 2695-186X Komi, N. M. (2024). Turning social license into a measurable variable: Predicting community trust in renewable energy development. International Journal of Engineering and Modern Technology, 10(11), 239–296. https://doi.org/10.56201/ijemt.v10.no11.2024.pg239.296 Kotter, J. P. (1995). Leading change: Why transformation efforts fail. Harvard Business Review, March–April 1995. Kotter, J. P. (1996). Leading change. Boston, MA: Harvard Business School Press. Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine learning operations : Overview, definition, and architecture. IEEE Access, 11, 31866–31879. https://doi.org/10.1109/ACCESS.2023.3262138 Lacity, M. C., & Willcocks, L. P. (2016a). A new approach to automating services. MIT Sloan Management Review, 58(1), 41–49. Lacity, M., & Willcocks, L. (2016b). Robotic process automation at Telefónica O2. MIS Quarterly Executive, 15(1), 21–37. Lacity, M., Willcocks, L., & Gozman, D. (2021). Influencing information systems practice: The action principles approach applied to robotic process and cognitive automation. Journal of Information Technology, 36(3), 216–240. https://doi.org/10.1177/0268396221990778 Ladapo, O. O., Dosunmu, A. A., Jooda, D., & Abolaji, T. O. (2022). Human-in-the-loop machine learning: A state of the art. Journal of Frontiers in Multidisciplinary Research, 3(1), 656– 669. https://doi.org/10.54660/.JFMR.2022.3.1.656-669 Ladapo, O. O., Dosunmu, A. A., Jooda, D., & Abolaji, T. O. (2025). Migration of applications and information systems to cloud computing infrastructure: Lessons from a South African retail bank. International Journal of Multidisciplinary Research and Growth Evaluation, 6(6), 1361–1375. https://doi.org/10.54660/.IJMRGE.2025.6.6.1361-1375 Ladapo, O. O., Jooda, D., Dosunmu, A. A., & Abolaji, T. O. (2023a). A comprehensive survey on ServiceNow for IT service management. International Journal of Multidisciplinary Research and Growth Evaluation, 4(6), 1532–1545. https://doi.org/10.54660/.IJMRGE.2023.4.6.1532-1545 Ladapo, O. O., Jooda, D., Dosunmu, A. A., & Abolaji, T. O. (2023b). Systematic literature review on security access control policies and techniques based on privacy requirements in a BYOD environment. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2890–2904. https://doi.org/10.62225/2583049X.2023.3.6.6206 Ladapo, O. O., Jooda, D., Dosunmu, A. A., & Abolaji, T. O. (2024). Keeping humans in the loop: Human-centered automated annotation with generative AI. International Journal of Multidisciplinary Futuristic Development, 5(1), 81–95. https://doi.org/10.54660/IJMFD.2024.5.1.81-95 Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392 Levina, N., & Vaast, E. (2005). The emergence of boundary spanning competence in practice: Implications for implementation and use of information systems. MIS Quarterly, 29(2), 335–363. Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005 Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022). Defining organizational AI governance. AI and Ethics, 2(4), 603–609. https://doi.org/10.1007/s43681-022-00143-x P-ISSN 2695-186X Mbonu, I. S., Aliliele, C., Iwuanyanwu, U., & Oluoha, O. M. (2018). A conceptual framework for legal and ethical risk modeling in enterprise data protection governance systems. Iconic Research and Engineering Journals, 2(2), 207–226. https://doi.org/10.64388/IREV2I2- 1714911 Mbonu, I. S., Aliliele, C., Uzoka, E., & Oluoha, O. M. (2019). A review of comparative data protection regulations and secure cloud implementation strategies across jurisdictions. Iconic Research and Engineering Journals, 2(9), 482–501. https://doi.org/10.64388/IREV2I9-1714912 Mbonu, I. S., Iwuanyanwu, U., Aliliele, C., & Uzoka, E. (2022a). A review of data protection impact assessment models in multi cloud financial infrastructure systems. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(1), 589–623. https://doi.org/10.32628/CSEIT25442 Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607 Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Review: Information technology and organizational performance: An integrative model of IT business value. MIS Quarterly, 28(2), 283–322. Meyer, M. (2010). The rise of the knowledge broker. Science Communication, 32(1), 118–127. https://doi.org/10.1177/1075547009359797 Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287596 Mitchell, R. K., Agle, B. R., & Wood, D. J. (1997). Toward a theory of stakeholder identification and salience: Defining the principle of who and what really counts. Academy of Management Review, 22(4), 853–886. https://doi.org/10.5465/amr.1997.9711022105 Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4 Mökander, J., Morley, J., Taddeo, M., & Floridi, L. (2021). Ethics-based auditing of automated decision-making systems: Nature, scope, and limitations. Science and Engineering Ethics, 27(4), Article 44. https://doi.org/10.1007/s11948-021-00319-4 Nahar, N., Zhou, S., Lewis, G. A., & Kästner, C. (2022). Collaboration challenges in building ML- enabled systems: Communication, documentation, engineering, and process. In Proceedings of the 44th International Conference on Software Engineering (ICSE '22) (pp. 413–425). ACM. https://doi.org/10.1145/3510003.3510209 National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). Gaithersburg, MD: NIST. https://doi.org/10.6028/NIST.AI.100-1 Neely, A., Gregory, M., & Platts, K. (1995). Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management, 15(4), 80–116. https://doi.org/10.1108/01443579510083622 Nicolini, D., Mengis, J., & Swan, J. (2012). Understanding the role of objects in cross-disciplinary collaboration. Organization Science, 23(3), 612–629. https://doi.org/10.1287/orsc.1110.0664 P-ISSN 2695-186X Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37. https://doi.org/10.1287/orsc.5.1.14 Nwakamma, S., Ojukwu, J., & Oyesiji, S. O. (2024b). Securing agentic AI enterprise workflows against prompt injection, tool poisoning, memory manipulation, and excessive agency threats. World Journal of Innovation and Modern Technology, 8(6), 217–248. https://doi.org/10.56201/wjimt.v8.no6.2024.pg217.248 Object Management Group. (2019). Semantics of Business Vocabulary and Business Rules , Version 1.5 (OMG Document formal/19-10-02). https://www.omg.org/spec/SBVR/ Object Management Group. (2024). Decision Model and Notation , Version 1.5 (OMG Document formal/24-01-01). https://www.omg.org/spec/DMN/1.5/ Oborn, E., & Dawson, S. (2010). Knowledge and practice in multidisciplinary teams: Struggle, accommodation and privilege. Human Relations, 63(12), 1835–1857. https://doi.org/10.1177/0018726710371237 Oduleye, T. E., & Medon, J. J. (2023b). A quantitative model for measuring the strategic impact of financial analysis on enterprise growth. International Journal of Advanced Multidisciplinary Research and Studies, 3(1), 1663–1672. https://doi.org/10.62225/2583049X.2023.3.1.5334 OECD. (2019). Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449). Paris: OECD. https://legalinstruments.oecd.org/en/instruments/oecd-legal-0449 OECD. (2025). The adoption of artificial intelligence in firms: New evidence for policymaking. Paris: OECD Publishing. https://doi.org/10.1787/f9ef33c3-en Ogbete, J. C., & Aminu-Ibrahim, A. Y. (2024). Translating healthcare infrastructure investment into measurable population health and diagnostic outcomes. International Journal of Scientific Research in Humanities and Social Sciences, 1(2), 955–985. https://doi.org/10.32628/IJSRSSH242775 Ogbole, J. I., Okoruwa, P. O., Fadayomi, O., Abolaji, T. O., Edivri, J., & Akeju, B. (2021). Conceptual model for identity-centric zero trust architecture in enterprise security governance. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(5), 393–415. https://doi.org/10.32628/IJSRCSEIT217562 Ogundairo, K. M. (2025). Hardening the autonomous value chain: Lean Six Sigma guardrails and multi-enterprise ETL architecture for agentic supply chain orchestration. IIARD International Journal of Economics and Business Management, 11(12), 441–478. https://doi.org/10.56201/ijebm..pg441.478 Ogundairo, K. M., & Ayivi-Donkor, S. S. (2023b). Telemetry-driven value realization: Infusing economic evaluation algorithms directly into enterprise data pipelines. International Journal of Economics and Financial Management, 8(8), 195–232. https://doi.org/10.56201/ijefm.v8.no8.2023.pg195.232 Ogundairo, K. M., & Ayivi-Donkor, S. S. (2024b). The vanishing interface: Why the coming era of autonomous agents demands a paradigm shift in product marketing architecture. IIARD International Journal of Economics and Business Management, 10(11), 350–385. https://doi.org/10.56201/ijebm.v10.no11.2024.pg350.385 Okojie, J. S., Filani, O. M., Ike, P. N., Okojokwu-Idu, J. O., Nnabueze, S. B., & Ihwughwavwe, S. I. (2023). Integrating AI with ESG metrics in smart infrastructure auditing for high-impact P-ISSN 2695-186X urban development projects. International Journal of Multidisciplinary Futuristic Development, 4(1), 32–44. https://doi.org/10.54660/IJMFD.2023.4.1.32-44 Okonkwo, C. S., Agbabiaka, J., Mayo, W., & Okeke, O. T. (2024a). Conceptual framework for digital supply chain governance in energy and infrastructure sectors. Gyanshauryam, International Scientific Refereed Research Journal, 7(4), 335–356. https://doi.org/10.32628/GISRRJ247423 Okonkwo, C. S., Agbabiaka, J., Mayo, W., & Okeke, O. T. (2024f). Supply chain automation framework using service management platforms. Shodhshauryam, International Scientific Refereed Research Journal, 7(2), 157–177. https://doi.org/10.32628/SHISRRJ2472157 Okonkwo, C. S., Ahiaeke Patrick, M. C., Okeke, O. T., & Mayo, W. (2023). Framework for integrating IT systems engineering with supply chain operations. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2580–2589. https://doi.org/10.62225/2583049X.2023.3.6.5500 Okonkwo, C. S., Ogunwole, O., Mayo, W., & Okeke, O. T. (2021). Framework for regulatory- compliant procurement in high-risk energy environments. International Journal of Multidisciplinary Research and Growth Evaluation, 2(6), 595–605. https://doi.org/10.54660/IJMRGE.2021.2.6.595-605 Okoruwa, P. O., Babatope, O. M., Akokodaripon, D. A., & Akinleye, O. K. (2024). Developing integrated digital platforms for enhancing transparency in procurement and supply chain management. International Journal of Multidisciplinary Research and Growth Evaluation, 5(6), 1719–1729. Oloto, A. M., & Yeboah, T. J. (2024). A critical review of procedural safeguards and regulatory compliance in special education programs. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 3125–3137. Oloto, A. M., & Yeboah, T. J. (2025). Developing conceptual risk assessment models for special education documentation and accountability systems. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(4), 691–734. https://doi.org/10.32628/CSEIT251116283 Ominyi, M., & Anichukwueze, C. C. (2023). A review of algorithmic accountability and model risk management approaches in financial services. Journal of Accounting and Financial Management, 9(12), 218–238. https://doi.org/10.56201/jafm.v9.no12.2023.pg218.238 Ominyi, M., & Anichukwueze, C. C. (2024). Conceptualizing responsible AI governance structures for automated decision systems in regulated industries. International Journal of Social Sciences and Management Research, 10(11), 403–430. https://doi.org/10.56201/ijssmr.v10.no11.2024.pg.403.430 Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2024a). Developing a conceptual framework for AI-assisted compliance monitoring and anomaly detection in high-volume transaction environments. World Journal of Innovation and Modern Technology, 8(6), 204–228. https://doi.org/10.56201/wjimt.v8.no6.2024.pg204.228 Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2024b). A systematic review of organizational readiness for the EU Artificial Intelligence Act in multinational enterprises. World Journal of Innovation and Modern Technology, 8(6), 185–203. https://doi.org/10.56201/wjimt.v8.no6.2024.pg185.203 Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2025a). A conceptual framework for multi- agent AI quality control in the review of regulated documents. International Journal of P-ISSN 2695-186X Social Sciences and Management Research, 11(8), 534–557. https://doi.org/10.56201/ijssmr..pg534.557 Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2025c). Reviewing legal privilege and confidentiality challenges in enterprise deployment of large language models. Journal of Law and Global Policy, 10(3), 150–176. https://doi.org/10.56201/jlgp..pg150.176 Omo Enabulele, A. B., Oyebamiji, I., Ikubanni, O., Okwah, M. N., Babalola, W. S., Azeez, O. M., & Olaniyi, A. O. (2025). A coordinated project-management approach to multisite implementation of motor-rehabilitation programs for children with autism spectrum disorder in United States healthcare systems: A narrative review. Cureus, 17(12), e100513. https://doi.org/10.7759/cureus.100513 Oreg, S., Vakola, M., & Armenakis, A. (2011). Change recipients' reactions to organizational change: A 60-year review of quantitative studies. The Journal of Applied Behavioral Science, 47(4), 461–524. https://doi.org/10.1177/0021886310396550 Orlikowski, W. J. (2002). Knowing in practice: Enacting a collective capability in distributed organizing. Organization Science, 13(3), 249–273. https://doi.org/10.1287/orsc.13.3.249.2776 Ozowara, D. E., Adebayo, A., & Anunagba, C. O. (2025). Advanced conceptual model for strengthening audit quality using data analytics across financial institutions. International Journal of Advanced Multidisciplinary Research and Studies, 5(6), 2246–2268. https://doi.org/10.62225/2583049X.2025.5.6.6050 Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253. https://doi.org/10.1518/001872097778543886 Paul, D., Cadle, J., Eva, M., Rollason, C., & Hunsley, J. (2020). Business Analysis (4th ed.). Swindon: BCS, The Chartered Institute for IT. Peppard, J., Ward, J., & Daniel, E. (2007). Managing the realization of business benefits from IT investments. MIS Quarterly Executive, 6(1), Article 3. Plattfaut, R., Borghoff, V., Godefroid, M., Koch, J., Trampler, M., & Coners, A. (2022). The critical success factors for robotic process automation. Computers in Industry, 138, 103646. https://doi.org/10.1016/j.compind.2022.103646 Plattfaut, R., Rehse, J.-R., Jans, C., Schulte, M., & van Wendel de Joode, J. (2024). Robotic process automation: Research impulses from the BPM 2023 panel discussion. Process Science, 1, Article 5. https://doi.org/10.1007/s44311-024-00005-1 Pohl, K. (2010). Requirements Engineering: Fundamentals, Principles, and Techniques. Berlin: Springer. Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729–739. https://doi.org/10.1016/j.dss.2012.08.017 Rafferty, A. E., Jimmieson, N. L., & Armenakis, A. A. (2013). Change readiness: A multilevel review. Journal of Management, 39(1), 110–135. https://doi.org/10.1177/0149206312457417 Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., ... Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). New York: ACM. https://doi.org/10.1145/3351095.3372873 P-ISSN 2695-186X Rakova, B., Yang, J., Cramer, H., & Chowdhury, R. (2021). Where responsible AI meets reality: Practitioner perspectives on enablers for shifting organizational practices. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–23. https://doi.org/10.1145/3449081 Robertson, J., Robertson, S., & Reed, A. (2024). Mastering the Requirements Process (4th ed.). Boston: Addison-Wesley Professional. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press. Rosemann, M., & vom Brocke, J. (2014). The six core elements of business process management. In J. vom Brocke & M. Rosemann (Eds.), Handbook on business process management 1 (2nd ed., pp. 105–122). Berlin: Springer. https://doi.org/10.1007/978-3-642-45100-3_5 Rowley, T. J. (1997). Moving beyond dyadic ties: A network theory of stakeholder influences. The Academy of Management Review, 22(4), 887. https://doi.org/10.2307/259248 Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x Sadiq, R. B., Safie, N., Abd Rahman, A. H., & Goudarzi, S. (2021). Artificial intelligence maturity model: A systematic literature review. PeerJ Computer Science, 7, e661. https://doi.org/10.7717/peerj-cs.661 Sagala, G. H., & Őri, D. (2024). Toward SMEs digital transformation success: A systematic literature review. Information Systems and e-Business Management, 22(4), 667–719. https://doi.org/10.1007/s10257-024-00682-2 Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. (2021). 'Everyone wants to do the model work, not the data work': Data cascades in high-stakes AI. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–15). Association for Computing Machinery. https://doi.org/10.1145/3411764.3445518 Sánchez, E., Calderón, R., & Herrera, F. (2025). Artificial intelligence adoption in SMEs: Survey based on TOE–DOI framework, primary methodology and challenges. Applied Sciences, 15(12), 6465. https://doi.org/10.3390/app15126465 Sanderson, C., Douglas, D., Lu, Q., Schleiger, E., Whittle, J., Lacey, J., Newnham, G., Hajkowicz, S., Robinson, C., & Hansen, D. (2023). AI ethics principles in practice: Perspectives of designers and developers. IEEE Transactions on Technology and Society, 4(2), 171–187. https://doi.org/10.1109/TTS.2023.3257303 Sanni, J. O., & Atima, M. E. (2021b). Business intelligence dashboard frameworks resolving executive visibility gaps in strategic marketing governance. International Journal of Multidisciplinary Research and Growth Evaluation, 2(6), 633–646. https://doi.org/10.54660/IJMRGE.2021.2.6.633-646 Sanni, J. O., & Attah, A. (2023a). A comprehensive framework for digital transformation in capital markets: Solving operational challenges and enhancing stakeholder engagement. Gyanshauryam, International Scientific Refereed Research Journal, 6(6), 275–302. https://doi.org/10.32628/GISRRJ236640 Sanni, J. O., Ajiga, D., & Atima, M. E. (2020a). Analytical models addressing measurement challenges of marketing return on investment in regulated services. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 636–648. https://doi.org/10.54660/IJMRGE.2020.1.5.636-648 Sanni, J. O., Iwuanyanwu, U. A., & Essien, M. A. (2025). Problem-oriented process mining for auditable marketing automation lifecycle control. International Journal of Advanced P-ISSN 2695-186X Multidisciplinary Research and Studies, 5(6), 1933–1947. https://doi.org/10.62225/2583049X.2025.5.6.5650 Sanni, J. O., Iwuanyanwu, U. A., Essien, M. A., Atima, M. E., & Attah, A. (2022). Adaptive control models for AI-driven marketing automation in financial compliance environments. Shodhshauryam, International Scientific Refereed Research Journal, 5(1), 243–270. https://doi.org/10.32628/SHISRRJ247133 Sarker, S., Chatterjee, S., Xiao, X., & Elbanna, A. (2019). The sociotechnical axis of cohesion for the IS discipline: Its historical legacy and its continued relevance. MIS Quarterly, 43(3), 695–719. https://doi.org/10.25300/MISQ/2019/13747 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 Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. (2024). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management. https://doi.org/10.1080/00472778.2024.2379999 Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems 28 (NIPS 2015). Curran Associates. Selbst, A. D., boyd, d., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287598 Serra, C. E. M., & Kunc, M. (2015). Benefits realisation management and its influence on project success and on the execution of business strategies. International Journal of Project Management, 33(1), 53–66. https://doi.org/10.1016/j.ijproman.2014.03.011 Shneiderman, B. (2020). Bridging the gap between ethics and practice: Guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems, 10(4), 1–31. https://doi.org/10.1145/3419764 Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision-making? International Journal of Human-Computer Studies, 51(5), 991–1006. https://doi.org/10.1006/ijhc.1999.0252 Sonteya, T., & Seymour, L. F. (2012). Towards an understanding of the business process analyst: An analysis of competencies. Journal of Information Technology Education: Research, 11, 43–63. https://doi.org/10.28945/1568 Star, S. L. (2010). This is not a boundary object: Reflections on the origin of a concept. Science, Technology, & Human Values, 35(5), 601–617. https://doi.org/10.1177/0162243910377624 Star, S. L., & Griesemer, J. R. (1989). Institutional ecology, 'translations' and boundary objects: Amateurs and professionals in Berkeley's Museum of Vertebrate Zoology, 1907–39. Social Studies of Science, 19(3), 387–420. https://doi.org/10.1177/030631289019003001 Sutcliffe, A., & Sawyer, P. (2013). Requirements elicitation: Towards the unknown unknowns. In 2013 21st IEEE International Requirements Engineering Conference (RE) (pp. 92–104). IEEE. https://doi.org/10.1109/RE.2013.6636709 P-ISSN 2695-186X Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S. J. J., Ouyang, C., ... Reijers, H. A. (2020). Robotic process automation: Contemporary themes and challenges. Computers in Industry, 115, 103162. https://doi.org/10.1016/j.compind.2019.103162 Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer of best practice within the firm. Strategic Management Journal, 17(S2), 27–43. https://doi.org/10.1002/smj.4250171105 Telford, T., Thopalli, K., Schaefer, G., Polner, A., Urbaniak, T., & Wright, D. (2022). Automation with intelligence (2022 survey). Deloitte Insights. Thong, J. Y. L. (2001). Resource constraints and information systems implementation in Singaporean small businesses. Omega, 29(2), 143–156. https://doi.org/10.1016/S0305- 0483(00)00035-9 Tonoyan, A., Dada, O., & Ayivi-Donkor, S. S. (2022b). Algorithmic process optimization and cost reduction: A review of simulation modeling and financial impact assessment. Journal of Accounting and Financial Management, 8(8), 139–169. https://doi.org/10.56201/jafm..p139.169 Tonoyan, A., Dada, O., & Ayivi-Donkor, S. S. (2024b). Real-time KPI tracking systems: A review of automated performance monitoring and data-driven decision making. World Journal of Innovation and Modern Technology, 8(6), 247–281. https://doi.org/10.56201/wjimt.v8.no6.2024.pg247.281 Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). The processes of technological innovation. Lexington, MA: Lexington Books. Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101 Trkman, P. (2010). The critical success factors of business process management. International Journal of Information Management, 30(2), 125–134. https://doi.org/10.1016/j.ijinfomgt.2009.07.003 Tushman, M. L. (1977). Special boundary roles in the innovation process. Administrative Science Quarterly, 22(4), 587–605. https://doi.org/10.2307/2392402 Uren, V., & Edwards, J. S. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management, 68, 102588. https://doi.org/10.1016/j.ijinfomgt.2022.102588 van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3), 1557–1580. https://doi.org/10.25300/misq/2021/16559 van der Aalst, W. M. P., Bichler, M., & Heinzl, A. (2018). Robotic process automation. Business & Information Systems Engineering, 60(4), 269–272. https://doi.org/10.1007/s12599-018- 0542-4 van Lamsweerde, A. (2009). Requirements Engineering: From System Goals to UML Models to Software Specifications. Chichester: John Wiley & Sons. Van Looy, A., & Shafagatova, A. (2016). Business process performance measurement: A structured literature review of indicators, measures and metrics. SpringerPlus, 5, Article 1797. https://doi.org/10.1186/s40064-016-3498-1 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 P-ISSN 2695-186X Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412 Vogelsang, A., & Borg, M. (2019). Requirements engineering for machine learning: Perspectives from data scientists. In 2019 IEEE 27th International Requirements Engineering Conference Workshops (pp. 245–251). IEEE. https://doi.org/10.1109/REW.2019.00050 Waizenegger, L., & Techatassanasoontorn, A. A. (2022). When robots join our team: A configuration theory of employees' perceptions of and reactions to robotic process automation. Australasian Journal of Information Systems, 26. https://doi.org/10.3127/ajis.v26i0.3833 Walawalkar, G., Adesuyi, M. O., Kalu, A., & Oduleye, T. E. (2025). Executive financial dashboards for real-time strategic oversight. International Journal of Advanced Multidisciplinary Research and Studies, 5(6), 2042–2054. Ward, J., & Daniel, E. (2012). Benefits management: How to increase the business value of your IT projects (2nd ed.). Chichester: John Wiley & Sons. Ward, J., De Hertogh, S., & Viaene, S. (2007). Managing benefits from IS/IT investments: An empirical investigation into current practice. In Proceedings of the 40th Annual Hawaii International Conference on System Sciences (HICSS 2007) (p. 206a). IEEE. https://doi.org/10.1109/HICSS.2007.330 Ward, J., Taylor, P., & Bond, P. (1996). Evaluation and realisation of IS/IT benefits: An empirical study of current practice. European Journal of Information Systems, 4(4), 214–225. https://doi.org/10.1057/ejis.1996.3 Weiner, B. J. (2009). A theory of organizational readiness for change. Implementation Science, 4(1), 67. https://doi.org/10.1186/1748-5908-4-67 Weiner, B. J., Amick, H., & Lee, S.-Y. D. (2008). Review: Conceptualization and measurement of organizational readiness for change. Medical Care Research and Review, 65(4), 379– 436. https://doi.org/10.1177/1077558708317802 Wenger, E. (1998). Communities of practice. Cambridge University Press. https://doi.org/10.1017/CBO9780511803932 Wenger, E. (2000). Communities of practice and social learning systems. Organization, 7(2), 225– 246. https://doi.org/10.1177/135050840072002 Wiegers, K., & Beatty, J. (2013). Software Requirements (3rd ed.). Redmond, WA: Microsoft Press. Wieringa, M. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 1–18). New York: ACM. https://doi.org/10.1145/3351095.3372833 Willcocks, L. P., Lacity, M., & Craig, A. (2015). Robotic process automation at Xchanging (The Outsourcing Unit Working Research Paper Series, Paper 15/03). London: London School of Economics and Political Science. Willcocks, L., Lacity, M., & Craig, A. (2017). Robotic process automation: Strategic transformation lever for global business services? Journal of Information Technology Teaching Cases, 7(1), 17–28. https://doi.org/10.1057/s41266-016-0016-9 Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25(1), 17–41. P-ISSN 2695-186X Wright, D., Telford, T., Schaefer, G., Watson, J., Polner, A., & Witherick, D. (2020). Automation with intelligence: Pursuing organisation-wide reimagination. Deloitte Insights. Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. The Academy of Management Review, 27(2), 185–203. https://doi.org/10.2307/4134351 Zhao, L., Alhoshan, W., Ferrari, A., Letsholo, K. J., Ajagbe, M. A., Chioasca, E.-V., & Batista- Navarro, R. T. (2021). Natural language processing for requirements engineering: A systematic mapping study. ACM Computing Surveys, 54(3), 1–41. https://doi.org/10.1145/3444689

More Articles from IIARD INTERNATIONAL JOURNAL OF ECONOMICS AND BUSINESS MANAGEMENT