Decision-Calibrated Human–AI Collaboration: A Governance Framework for Generative and Agentic AI in Management
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Abstract
Organizations are rapidly moving from predictive Artificial Intelligence (AI) toward Generative Artificial Intelligence (GenAI) and increasingly agentic systems that can recommend, create, coordinate, and sometimes execute managerial actions. Yet higher model capability does not by itself produce better organizational decisions: underreliance wastes useful machine competence, whereas overreliance can amplify hallucination, bias, automation complacency, accountability diffusion, and skill erosion. This conceptual study develops a decision-calibration framework for human–AI collaboration that treats the central design problem as aligning the degree of AI influence with verified AI competence, task risk, decision reversibility, evidentiary verifiability, and human expertise. The study integrates research on organizational decision design, trust in automation, algorithm aversion and appreciation, explainable AI, responsible AI governance, and recent evidence on GenAI-enabled knowledge work. The resulting framework distinguishes four operating modes, human-led, AI-augmented, AI-led with human veto, and dual-control, and links them to a lifecycle governance architecture comprising task qualification, evidence-grounded generation, confidence and uncertainty signaling, independent verification, escalation, audit logging, and post-decision learning. Eight propositions explain how calibration quality should affect decision accuracy, speed, accountability, organizational learning, and resilience. The paper further introduces practical metrics for calibration error, override quality, verification coverage, and decision reversibility. The framework shifts managerial attention from the simplistic question of whether humans or AI should decide toward the more consequential question of when, how, and under what governance conditions each should influence a decision. This perspective offers a theoretically grounded and operationally actionable basis for designing reliable human–AI decision systems in contemporary organizations.
Keywords
Agentic AI Decision calibration Generative artificial intelligence Human–AI collaboration Organizational governance Responsible AIReferences (35)
- [1] Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
- [2] Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of management review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
- [3] Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257
- [4] Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. MIS quarterly, 45(3), 1433–1450. https://doi.org/10.25300/MISQ/2021/16274
- [5] Puranam, P. (2021). Human–AI collaborative decision-making as an organization design problem. Journal of organization design, 10(2), 75–80. https://doi.org/10.1007/s41469-021-00095-2
- [6] Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005
- [7] von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of management discoveries, 4(4), 404–409. https://doi.org/10.5465/amd.2018.0084
- [8] Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of management annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
- [9] Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE transactions on systems, man, and cybernetics—part A: Systems and humans, 30(3), 286–297. https://doi.org/10.1109/3468.844354
- [10] 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
- [11] Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57(3), 407–434. https://doi.org/10.1177/0018720814547570
- [12] Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of management annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
- [13] Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., & Beck, H. P. (2003). The role of trust in automation reliance. International journal of human-computer studies, 58(6), 697–718. https://doi.org/10.1016/S1071-5819(03)00038-7
- [14] 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
- [15] 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
- [16] Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management science, 64(3), 1155–1170. https://doi.org/10.1287/mnsc.2016.2643
- [17] Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of marketing research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788
- [18] 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
- [19] Alufaisan, Y., Marusich, L. R., Bakdash, J. Z., Zhou, Y., & Kantarcioglu, M. (2021). Does explainable artificial intelligence improve human decision-making? Proceedings of the AAAI conference on artificial intelligence, 35(8), 6618–6626. https://doi.org/10.1609/aaai.v35i8.16819
- [20] Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. S. (2021). Does the whole exceed its parts? The effect of AI explanations on complementary team performance. Proceedings of the 2021 CHI conference on human factors in computing systems, 81, 1–16. https://doi.org/10.1145/3411764.3445717
- [21] Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on human-computer interaction, 5(CSCW1), Article 188, 1–21. https://doi.org/10.1145/3449287
- [22] Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human–AI interaction. Proceedings of the 2019 CHI conference on human factors in computing systems, 3, 1–13. https://doi.org/10.1145/3290605.3300233
- [23] Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International journal of human–computer interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
- [24] Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1, 389–399. https://doi.org/10.1038/s42256-019-0088-2
- [25] Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & 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
- [26] Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature machine intelligence, 1, 501–507. https://doi.org/10.1038/s42256-019-0114-4
- [27] Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and engineering ethics, 26(4), 2141–2168. https://doi.org/10.1007/s11948-019-00165-5
- [28] Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., ... & Barnes, P. (2020, January). 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). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372873
- [29] National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). U.S. department of commerce. https://doi.org/10.6028/NIST.AI.100-1
- [30] Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The journal of strategic information systems, 34(2), Article 101885. https://doi.org/10.1016/j.jsis.2024.101885
- [31] Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
- [32] Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at work. The quarterly journal of economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
- [33] Dell’Acqua, F., Ayoubi, C., Lifshitz, H., Sadun, R., Mollick, E., Mollick, L., ... & Lakhani, K. R. (2026). The cybernetic teammate: A field experiment on generative AI and teamwork. Organization science, 37(4), 1217-1242. https://doi.org/10.1287/orsc.2025.20702
- [34] Lee, H. P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025, April). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. In Proceedings of the 2025 CHI conference on human factors in computing systems (pp. 1-22). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713778
- [35] Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G. J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information & management, 57(2), Article 103. https://doi.org/10.1016/j.im.2019.103174
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- Published 2026-12-03
- Issue Vol. 3 No. 4 (2026): Management Analytics and Social Insights (MASI)
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