900+ Titles Secure Razorpay Checkout Instant eBook Delivery
[email protected] Main Site
Proceedings / International Conference on Intelligent Engineering & Future Technologies (ICIEFT-2026) / Paper

Structuring Organizational Readiness for AI-Augmented Decisions: A Synthesis-Based Framework for Management Assessment

Anushka Vispute, Raksha Thakur, Darshan Badgujar, Sai Kannadkar, Tejas Jadhav, Vyankatesh Pingle
School of Computer Science and Engineering, Sandip University, Nashik, India
pp. 55-68

Abstract

Artificial Intelligence is becoming a vital tool for organisations in enabling informed decisions on strategic priorities, allocation of resources, operational coordination and evaluation of performance. However, having AI technologies doesn't automatically mean an organisation has the managerial skills to use AI effectively. One of the key issues is how to measure technological readiness, decision-making power, human oversight, and organisational learning in a single governance framework. This work tackles this challenge by proposing the Integrated AI Decision Management (IADM) framework using systematic literature synthesis. A PRISMA-guided review of publications from IEEE Xplore, Springer, Scopus, and ACM Digital Library led to the identification of 156 cleaned publications from research published from 2015–26. Twenty-eight studies were selected for thematic analysis, according to pre-defined eligibility criteria. The synthesis was broken down into five areas of assessment: Strategic alignment, Technological infrastructure, Human-AI collaboration, Governance mechanisms, and Continuous improvement. The dimensions are not viewed as stand-alone adoption factors, but are linked to organisational assessment via their operational definitions, key performance indicators, target benchmarks, and 4-level maturity scale. This is a structure that aims to facilitate learning for organisations about the areas in which they are lacking, the management of their decisions to use AI, and the prioritisation of progressive implementation. The mean rating from these five experts specialising in AI and management was 4.2/5.0. The study also results in decision-making steps regarding phased implementation and mapping of the challenges with the assessment results to present a pathway for the managerial steps. The main contribution is a structured approach to convert the general needs of making decisions with AI assistance into measurable characteristics of an organisation's capability. The system is used as a foundation for the systematic diagnosis of an organization, but needs to be empirically tested in other sectors.

Keywords

AI Integration Framework, Organisational Decision-Making, Systematic Literature Review, AI Governance, Digital Transformation, Human-AI Collaboration, Enterprise AI Management, Maturity Assessment

Cite This Paper

Anushka Vispute, Raksha Thakur, Darshan Badgujar, Sai Kannadkar, Tejas Jadhav, Vyankatesh Pingle, "Structuring Organizational Readiness for AI-Augmented Decisions: A Synthesis-Based Framework for Management Assessment," in International Conference on Intelligent Engineering & Future Technologies (ICIEFT-2026), pp. 55-68.

References

  1. W. Liu et al., "How do we move towards true artificial intelligence," IEEE HPCC/DSS/SmartCity/DependSys, Haikou, China, 2021, pp. 2156-2158.
  2. K. Qamar and S. Narad, "Emotional Intelligence Integration in Artificial Intelligence," in Smart Trends in Computing and Communications, LNNS, vol. 1458, Springer, Singapore, 2026.
  3. A. Bock, "Beyond Narrow Decision Models: Toward Integrative Models of Organizational Decision Processes," IEEE 17th CBI, Lisbon, Portugal, 2015, pp. 181-190.
  4. F. Nader and R. Chalal, "Organizational Decision Support System (ODSS) for bidding process," 3rd IEEE ICTTA, Damascus, Syria, 2008, pp. 1-7.
  5. M. Schirmer et al., "Quality of Decision-Making Processes in Teams: Effects of Culture and AI," Group Decis. Negot., vol. 35, no. 8, 2026.
  6. L. Liang et al., "Generation mechanism of behavioral risk for organizational decision-makers in financial institutions," Humanit. Soc. Sci. Commun., vol. 11, no. 1196, 2024.
  7. S. Munir et al., "Emotional intelligence, support, and organizational culture's impact on decision-making," Discov. Educ., vol. 4, no. 269, 2025.
  8. R. Dlugatch et al., "AI-driven decision support systems and epistemic reliance: qualitative study," BMC Med. Ethics, vol. 25, no. 6, 2024.
  9. S. Shapiro Ben David et al., "AI driven decision support reduces antibiotic mismatches," npj Digit. Med., vol. 8, no. 61, 2025.
  10. D. Stoffels et al., "How Explainable AI Methods Support Data-Driven Decision-Making," LNISO, vol. 74, Springer, Cham, 2025.
  11. M. Vasudevan et al., "AI-Driven Decision Support Systems for Project Management in Automobile Engineering," IEEE CE2CT, Bhimtal, India, 2025, pp. 946-949.
  12. S. Vaduka et al., "Optimizing Personal Finance Management through AI-Driven Decision Support Systems," IEEE TENSYMP, New Delhi, India, 2024, pp. 1-6.
  13. P. Klaykul et al., "Web-Based Intelligent Decision Support System for Toll Plaza Management," IEEE HPEC, Wakefield, MA, USA, 2025, pp. 1-8.
  14. A. Dattijo and S. Jo, "Human strategic innovation against AI systems," Discov. Artif. Intell., vol. 5, no. 321, 2025.
  15. N. Bositkhanova and S.M.U. Dadaboyev, "Revolutionizing workforce planning: the strategic role of AI in HR strategy," Discov. Glob. Soc., vol. 3, no. 100, 2025.
  16. P. Hofmann et al., "AI-enabled information systems: Teaming up with intelligent agents in networked business," Electron. Markets, vol. 34, no. 52, 2024.
  17. R. McIvor, "Digital Transformation Strategy," in Digital Transformation, Palgrave Executive Essentials, Palgrave Macmillan, Cham, 2025.
  18. H. Chen et al., "Investigating corporate entrepreneurship strategy through digital transformation," J. Innov. Entrep., vol. 14, no. 95, 2025.
  19. M. Rubio-Andrés et al., "Tackling digital transformation strategy: effects on firm innovation," J. Technol. Transf., vol. 50, pp. 1893-1918, 2025.
  20. B. Ye et al., "Unlocking digital transformation potential in manufacturing: technology-strategy fit," Asian Bus. Manage., vol. 24, pp. 652-684, 2025.
  21. J. Osakwe et al., "AI-Driven Approaches for Optimising Data Governance in Large Enterprises," IEEE ETNCC, Windhoek, Namibia, 2025, pp. 205-209.
  22. N.V. Vadlamudi et al., "Leveraging Generative AI Trained on Enterprise Data for Business Value," IEEE TEMSCON LATAM, Cartagena, Colombia, 2025, pp. 1-6.
  23. G. Sravanthi et al., "Ethical AI for Data Governance: Fairness-Centric Approach," IEEE ICMCTC, Subang Jaya, Malaysia, 2025, pp. 1-5.
  24. A. Bodnari and J. Travis, "Scaling enterprise AI in healthcare: governance in risk mitigation frameworks," npj Digit. Med., vol. 8, no. 272, 2025.
  25. D. Allen et al., "A roadmap for governing AI: technology governance and power-sharing liberalism," AI Ethics, vol. 5, pp. 3355-3377, 2025.
  26. S.H. Chung et al., "Data driven operational risk management," Ann. Oper. Res., vol. 348, pp. 777-781, 2025.
  27. M.B. Bürger and M.D. Kadenic, "Framing AI, constructing identity: institutional discourse shapes AI adoption," AI Soc., 2026.
  28. D.W. Wendt, "The AI Adoption & Management Framework," in AI Strategy and Security, Apress, Berkeley, CA, 2025.
International Conference on Intelligent Engineering & Future Technologies (ICIEFT-2026)
· Xilvora Ink
View full volume →
View Volume