Architectural Synthesis and Cyber-Physical Integration: A Comprehensive Literature Review
DOI:
https://doi.org/10.59097/jasae.v5i1.93Keywords:
agentic AI, digital transformation, technology convergence, organizational readiness, autonomous systemsAbstract
The integration of autonomous, goal-driven AI agents into enterprise digital transformation initiatives necessitates a comprehensive reassessment of organizational readiness that extends beyond conventional technical implementation to encompass strategic alignment, governance architectures, and socio-technical adaptability. This systematic literature review synthesizes extant research published between 2015 and 2026 across five major academic databases (IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and arXiv) using a rigorous multi-stage screening methodology. We construct a seven-dimensional readiness framework examining: (1) strategic roadmap alignment, (2) technology stack interoperability, (3) autonomous decision-making governance, (4) data accessibility and quality, (5) organizational adaptability and AI literacy, (6) technology convergence velocity, and (7) transformation performance outcomes. Moving beyond descriptive maturity models, this review makes a distinct theoretical contribution by grounding readiness in established frameworks—including the Strategic Alignment Model, Dynamic Capabilities Theory, and multi-layered interoperability ontologies—while resolving conceptual ambiguities between readiness and implementation outcomes. Practical deliverables include an enterprise self-assessment framework, maturity progression matrices, and industry-specific deployment pathways.
References
S. Alvarez-Telena and M. Diez-Fernandez, “Advances in agentic AI: Back to the future,” arXiv:2512.24856, 2025.
J. Holmström, “From AI to digital transformation: The AI readiness framework,” Business Horizons, vol. 65, no. 3, pp. 329–339, 2022, doi: 10.1016/j.bushor.2021.03.006.
D. Dey and D. Ghose, “Integrating AI adoption frameworks with digital maturity models: Strategic pathways in a rapidly evolving technological landscape,” Journal of Contemporary Business Research, vol. 1, no. 2, pp. 156–170, 2025.
M. Kumar, M. G. Taj, A. Zevalov, R. Bhetwal, K. V. Kanani, and M. Rahman, “Maturity model taxonomy for digital transformation,” in Proc. 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2023, doi: 10.1109/IDAACS58523.2023.10348792.
S. Berghaus and A. Back, “Gestaltungsbereiche der digitalen Transformation von Unternehmen: Entwicklung eines Reifegradmodells,” Die Unternehmung, vol. 70, no. 2, pp. 98–123, 2016, doi: 10.5771/0042-059X-2016-2-98.
M. Palade and G. Carutasu, “Organizational readiness for artificial intelligence adoption,” Scientific Bulletin of the Politehnica University of Timisoara, Transactions on Engineering and Management, vol. 7, nos. 1–2, 2021, doi: 10.59168/FDMS6321.
P. Frangos, “An integrative literature review on leadership and organizational readiness for AI,” in Proc. 4th European Conference on the Impact of Artificial Intelligence and Robotics, vol. 4, no. 1, 2022, pp. 145–152, doi: 10.34190/icair.4.1.834.
R. B. Sadiq, N. Safie, A. H. A. Rahman, and S. Goudarzi, “Artificial intelligence maturity model: A systematic literature review,” PeerJ Computer Science, vol. 7, Art. no. e661, 2021, doi: 10.7717/peerj-cs.661.
T. Schuster, L. Waidelich, and R. Volz, “Maturity models for the assessment of artificial intelligence in small and medium-sized enterprises,” in Digital Transformation. Springer, 2021, pp. 22–36.
F. Rego, J. Rodrigues, and P. Ruivo, “Evaluating enablers for digital transformation readiness and resulting benefits,” Procedia Computer Science, vol. 239, pp. 307–313, 2024, doi: 10.1016/j.procs.2024.06.176.
M. Zohaib, M. A. Akbar, S. Hyrynsalmi, and A. A. Khan, “Agentic AI in 6G software businesses: A layered maturity model,” in Proc. 2025 International Conference on Frontiers of Information Technology (FIT), 2025, pp. 1–6, doi: 10.1109/FIT67061.2025.11333593.
S. Zhou, “AI empowerment and measurement: Reconstructing the enterprise digital maturity model and its implications for service ecosystem,” Frontiers in Sustainable Development, vol. 5, no. 9, pp. 173–180, 2025, doi: 10.54691/tt5b9s51.
S. M. Hosseininasab, “A maturity-driven selection model for effective digital transformation governance mechanisms in large organizations,” Kybernetes, vol. 54, no. 9, pp. 5106–5132, 2025, doi: 10.1108/K-10-2023-2136.
I. Krakovskaya, J. Korokoshko, and Y. Slushkina, “Digital maturity of industrial enterprises: Evaluation experience,” St. Petersburg University Journal of Economic Studies, vol. 40, no. 3, pp. 433–459, 2024, doi: 10.21638/spbu05.2024.305.
C. Uba, T. Lewandowski, and T. Böhmann, “The AI-based transformation of organizations: The 3D-model for guiding enterprise-wide AI change,” in Proc. 56th Hawaii International Conference on System Sciences (HICSS), 2023, pp. 6129–6138, doi: 10.24251/HICSS.2023.182.
A. Landeta Echeberria, “AI integration in the digital transformation strategy,” in Artificial Intelligence for Business. Springer, 2022, pp. 115–140.
X. An-Ping, W. Hua-Jing, and Q. Zhong, “Research on integration method of legacy systems based on service-oriented architecture,” Computer Engineering and Applications, vol. 44, no. 4, pp. 80–83, 2008.
G. A. Lewis and L. Wrage, “Approaches to constructive interoperability,” Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA, USA, Tech. Rep. CMU/SEI-2004-TR-020, 2004.
S. Kuppuraju, A. Kumar, and G. P. Kumari, “Case study to verify the interoperability of a service oriented architecture stack,” in Proc. IEEE International Conference on Services Computing (SCC), 2007, pp. 678–679, doi: 10.1109/SCC.2007.39.
J. Pękala, “Some aspects of interoperability of enterprise and manufacturing systems,” Applied Mechanics and Materials, vol. 613, pp. 368–375, 2014.
X. Xiang-rong, G. Xin-jian, and Y. Zuo-liang, “Research on interoperability of enterprise system integration and application in distributed parts library,” Manufacturing Automation, vol. 28, no. 5, pp. 12–16, 2006.
N. Maddukuri, “The transformative impact of AI on modern system integration,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11, no. 1, pp. 229–236, 2025, doi: 10.32628/CSEIT25111218.
P. K. Chakilam, “Interoperability in healthcare: Leveraging enterprise integration architecture for seamless data exchange,” European Modern Studies Journal, vol. 9, no. 4, pp. 652–661, 2025, doi: 10.59573/emsj.9(4).2025.63.
D. Chanda, “AI based data architecture impact analysis,” EasyChair Preprint Series, no. 1124, 2019.
A. Liapis, S. Christiaens, P. De Leenheer, and R. Meersman, “Collaboration across the enterprise: An ontology based approach for enterprise interoperability,” in Semantic Enterprise Application Integration for Business Processes. IGI Global, 2010, pp. 1–18, doi: 10.4018/978-1-60566-804-8.ch001.
C. Gaie, “From secured legacy systems to interoperable services: The careful evolution of the French tax administration to provide new possibilities while ensuring the primary tax recovering objective,” International Journal of Computational Systems Engineering, 2020, doi: 10.1504/IJCSYSE.2020.111204.
V. Acharya, “Governing the agentic enterprise: A governance maturity model for managing AI agent sprawl in business operations,” arXiv:2604.16338, 2026.
K. Huang, J. Huang, Y. Mehmood, H. Atta, M. Z. Baig, and M. A. U. Haq, “AAGATE: A NIST AI RMF-aligned governance platform for agentic AI,” arXiv:2510.25863, 2025.
P. S. Nwachukwu, O. K. Chima, and C. H. Okolo, “The artificial intelligence governance framework for finance: A control-by-design approach to algorithmic decision-making in accounting,” Finance & Accounting Research Journal, vol. 7, no. 8, pp. 350–379, 2025, doi: 10.51594/farj.v7i8.2016.
G. Falco et al., “Governing AI safety through independent audits,” Nature Machine Intelligence, vol. 3, no. 7, pp. 566–571, 2021, doi: 10.1038/s42256-021-00370-7.
M. Sloane and E. Moss, “Assessing the assessment: Comparing algorithmic impact assessments and AI audits,” SSRN, 2023, doi: 10.2139/ssrn.4486259.
E. P. Goodman and J. Trehu, “AI audit washing and accountability,” SSRN, 2022, doi: 10.2139/ssrn.4227350.
V. Damen, M. Wiersma, G. Aydin, and R. van Haasteren, “Explainable AI for EU AI Act compliance audits,” Maandblad voor Accountancy en Bedrijfseconomie, vol. 99, no. 4, pp. 231–242, 2025, doi: 10.5117/mab.99.150303.
M. Dokumacı, “Legal frameworks for AI regulations,” Human Computer Interaction, vol. 8, no. 1, 2024, doi: 10.62802/ytst2927.
E. Kazim, D. M. T. Denny, and A. Koshiyama, “AI auditing and impact assessment: According to the UK information commissioner’s office,” AI and Ethics, vol. 1, pp. 301–310, 2021, doi: 10.1007/s43681-021-00039-2.
D. Dey and D. Bhaumik, “APPRAISE: A governance framework for innovation with artificial intelligence systems,” Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, vol. 7, no. 1, pp. 328–340, 2024, doi: 10.1609/aies.v7i1.31640.
L. Kandikatla and B. Radeljic, “AI and human oversight: A risk-based framework for alignment,” arXiv:2510.09090, 2025.
E. Bandara et al., “Think before you act: A neurocognitive governance model for autonomous AI agents,” arXiv:2604.25684, 2026.
B. H. Kang, W. Yang, and M. B. Amin, “Trustworthy orchestration artificial intelligence by the ten criteria with control-plane governance,” arXiv:2512.10304, 2025.
R. Abulibdeh et al., “Who’s really in the loop? Rethinking oversight in AI-assisted health care,” The Lancet, vol. 407, no. 10545, pp. 2340–2344, 2026, doi: 10.1016/S0140-6736(26)00204-7.
Z. Engin, “Human-AI governance (HAIG): A trust-utility approach,” Journal of Responsible Technology, vol. 26, Art. no. 100167, 2026, doi: 10.1016/j.jrt.2026.100167.
K. Hiniduma, S. Byna, and J. L. Bez, “Data readiness for AI: A 360-degree survey,” ACM Computing Surveys, vol. 57, no. 9, Art. no. 219, pp. 1–39, 2025, doi: 10.1145/3722214.
F. Alborzi, R. Chirkova, J. Doyle, and Y. Fathi, “Determining query readiness for structured data,” in Lecture Notes in Computer Science, vol. 9263. Springer, 2015, pp. 3–14, doi: 10.1007/978-3-319-22729-0_1.
M. Y. Ng et al., “Perceptions of data set experts on important characteristics of health data sets ready for machine learning,” JAMA Network Open, vol. 6, no. 12, Art. no. e2345892, 2023, doi: 10.1001/jamanetworkopen.2023.45892.
B. J. Douthit, G. Del Fiol, C. J. Staes, S. L. Docherty, and R. L. Richesson, “A conceptual framework of data readiness: The contextual intersection of quality, availability, interoperability, and provenance,” Applied Clinical Informatics, vol. 12, no. 3, pp. 675–685, 2021.
S. P. Singh, “The critical role of master data management in AI readiness,” World Journal of Advanced Engineering Technology and Sciences, vol. 15, no. 1, pp. 1516–1532, 2025, doi: 10.30574/wjaets.2025.15.1.0353.
W. Brewer, P. Widener, V. Anantharaj, F. Wang, T. Beck, A. Shankar, and S. Oral, “Data readiness for scientific AI at scale,” arXiv:2507.23018, 2025.
S. Afzal, A. Chaudhary, N. Gupta, H. Patel, C. Spina, and D. Wang, “Data-debugging through interactive visual explanations,” in Trends and Applications in Knowledge Discovery and Data Mining. Springer, 2021, pp. 133–142.
S. Afzal, R. C, M. Kesarwani, S. Mehta, and H. Patel, “Data readiness report,” arXiv:2010.07213, 2020.
F. Olsson and M. Sahlgren, “Data readiness for natural language processing,” arXiv:2009.02043, 2020.
J. Jöhnk, M. Weißert, and K. Wyrtki, “Ready or not, AI comes: An interview study of organizational AI readiness factors,” Business & Information Systems Engineering, vol. 63, no. 1, pp. 5–20, 2021, doi: 10.1007/s12599-020-00676-7.
R. Nama, “From evaluator to principal: The agentic AI literacy framework (AALF) for delegated autonomy,” AI and Ethics, vol. 6, no. 3, pp. 415–430, 2026, doi: 10.1007/s43681-026-01167-3.
Y. Yoo, Y. H. Cho, and J. Choi, “A systematic review on the competences of human-AI collaboration,” in Proc. International Conference on Computers in Education, 2023, pp. 478–487, doi: 10.58459/icce.2023.4788.
B. Moore, “What competencies will be needed to manage artificial intelligence in the workplace? (An AI perspective),” Assessment and Development Matters, vol. 16, no. 1, pp. 4–8, 2024.
A. Benlian and M. Pinski, “The AI literacy development canvas: Assessing and building AI literacy in organizations,” Business Horizons, 2025, doi: 10.1016/j.bushor.2025.10.001.
K. Hölzle, R. Rose, and V. L. Kaschub, “The interplay of humans, technology, and organizations in realizing AI’s productivity promise,” The Economists’ Voice, vol. 21, no. 2, pp. 357–362, 2024, doi: 10.1515/ev-2024-0053.
M. Khumalo et al., “The integration of artificial intelligence in higher education institutions’ workforce: A literature review,” International Journal of Research in Business and Social Science, vol. 14, no. 6, pp. 393–399, 2025, doi: 10.20525/ijrbs.v14i6.4046.
P. Chandra, A. Dubey, S. K. Sharma, and S. Karsoliya, “A novel conceptualization of AI literacy and empowering employee experience at digital workplace using generative AI and augmented analytics: A survey,” Journal of Electrical Systems, vol. 20, no. 2, pp. 2582–2603, 2024, doi: 10.52783/jes.2031.
A. Suhara, “Human capital strategies to address artificial intelligence challenges in the contemporary work environment,” Jurnal Ilmiah Manajemen Kesatuan, vol. 13, no. 5, pp. 3793–3802, 2025, doi: 10.37641/jimkes.v13i5.3907.
G. S. Sunkar, “Professional adaptation and success in the artificial intelligence era: A paradigm shift in workforce evolution,” European Modern Studies Journal, vol. 9, no. 4, pp. 291–298, 2025, doi: 10.59573/emsj.9(4).2025.30.
I. Nodiyarjo, “21st century labor challenges: The relationship of competence and organizational culture,” Adpebi Science Series, vol. 1, no. 1, 2024, doi: 10.54099/icemat2023.v1i1.268.
M. Santana and M. Díaz-Fernández, “Competencies for the artificial intelligence age: Visualisation of the state of the art and future perspectives,” Review of Managerial Science, vol. 17, no. 6, pp. 1971–2004, 2023, doi: 10.1007/s11846-022-00613-w.
F. Firouzi, M. Daneshmand, J. Song, and K. Mankodiya, “Guest editorial special issue on empowering the future generation systems: Opportunities by the convergence of cloud, edge, AI, and IoT,” IEEE Internet of Things Journal, vol. 10, no. 5, pp. 3681–3685, 2023, doi: 10.1109/JIOT.2022.3232084.
P. Sandner, J. Gross, and R. Richter, “Convergence of blockchain, IoT, and AI,” Frontiers in Blockchain, vol. 3, Art. no. 522600, 2020.
K. D. S. Vyshnavi, “Integration of blockchain, internet of things and AI,” International Journal of Research in Science & Technology, vol. 12, no. 4, pp. 31–36, 2022, doi: 10.37648/ijrst.v12i04.006.
E. Muhati, D. B. Rawat, and B. M. Sadler, “A new cyber-alliance of artificial intelligence, internet of things, blockchain, and edge computing,” IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 104–107, 2022, doi: 10.1109/IOTM.001.2100181.
F. Firouzi, S. Jiang, K. Chakrabarty, B. Farahani, M. Daneshmand, J. Song, and K. Mankodiya, “Fusion of IoT, AI, edge-fog-cloud, and blockchain: Challenges, solutions, and a case study in healthcare and medicine,” IEEE Internet of Things Journal, vol. 10, no. 5, pp. 3686–3705, 2023, doi: 10.1109/JIOT.2022.3191881.
S. P. J, A. S, U. L, T. F. A, M. S, and M. S, “Revolutionizing industries through IoT, blockchain and AI integration,” in Proc. 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), 2023, doi: 10.1109/ICPCSN58827.2023.00166.
A. Mehmood, M. Arif, and F. Mehmood, “Towards a unified digital ecosystem: The role of platform technology convergence,” Electronics, vol. 14, no. 24, Art. no. 4787, 2025.
S. Li, W. Ma, X. Wang, X. Zhang, H. Xu, E. Garces, and T. Daim, “Technology convergence and its influence on innovation networks: An assessment of emerging trends,” in Proc. PICMET ’24: Portland International Conference on Management of Engineering and Technology, 2024, pp. 1–9.
S. Saha, K. Banerjee, S. Ghosh, S. Mitra, and D. Pal, “AI-driven edge computing for IoT: A comprehensive survey and future directions,” International Journal of Advanced Research in Science, Communication and Technology, vol. 3, no. 1, 2023, doi: 10.48175/IJARSCT-12921.
M. V. Chowdary, “The convergence of artificial intelligence, cloud computing, and edge computing: Transforming the tech landscape,” Research and Analysis Journal, vol. 3, no. 12, pp. 365–391, 2020, doi: 10.18535/raj.v3i12.211.
P. D. Sawant, “Agentic AI: A quantitative analysis of performance and applications,” Journal of Advances in Artificial Intelligence, vol. 3, no. 2, pp. 132–140, 2025, doi: 10.18178/JAAI.2025.3.2.132-140.
D. Thillaivasan and C. N. Wickramasinghe, “Conceptualizing the impact of AI and automation on leadership, human capital and organizational performance,” Journal of Business and Technology, vol. 4, nos. 1–2, pp. 1–19, 2020, doi: 10.4038/jbt.v4i1-2.37.
K. R. Kotte, L. Thammareddi, D. Kodi, V. R. Anumolu, A. K. K, and S. Joshi, “Integration of process optimization and automation: A way to AI powered digital transformation,” in Proc. 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), 2025, doi: 10.1109/CE2CT64011.2025.10939966.
G. U. Parab, “Agentic AI in data analytics: Transforming autonomous insights and decision-making,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10, no. 6, pp. 1752–1759, 2024, doi: 10.32628/CSEIT241061220.
W. AlShikh, M. S. Ali, B. Kennedy, and D. Mozolevskyi, “Towards outcome-oriented, task-agnostic evaluation of AI agents,” arXiv:2511.08242, 2025.
P. T. Bryant, “Evaluation of performance,” in Augmented Humanity. Palgrave Macmillan, 2021, pp. 199–223.
K. I. Meutia, “Aligning AI impact with strategy: Cross sector metrics for sustainable business model transformation,” Novatio: Journal of Management Technology and Innovation, vol. 2, no. 3, pp. 174–188, 2024, doi: 10.61978/novatio.v2i3.850.
E. S. Mityakov and V. V. Yatsenko, “Metrics for evaluating the results of digital transformation,” Regional and Branch Economy, no. 4, pp. 116–121, 2024, doi: 10.47576/2949-1916.2024.4.4.014.
S. Sethi, “Bridging KPI gaps: A scalable approach to cross-vertical performance alignment,” International Journal for Multidisciplinary Research, vol. 4, no. 6, 2022, doi: 10.36948/ijfmr.2022.v04i06.40776.
N. Alsufyani and A. Q. Gill, “Digitalisation performance assessment: A systematic review,” Technology in Society, vol. 68, Art. no. 101894, 2022, doi: 10.1016/j.techsoc.2022.101894.
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