Keywords
Scenario planning
MLOps
Governance models
Anticipatory systems
Continuous learning
Adaptive decision-making
Automation pipelines
Uncertainty analysis
Policy intelligence
How to Cite
Abstract
A productive approach to integrating strategic Foresight and machine learning is the Generalized Strategic Foresight Model embedding Machine Learning Operations (MLOps) (GSF(M)²), a unified governance architecture that combines the interpretive depth of long-term scenario-based Foresight with the adaptivity of real-time machine learning pipelines. The model addresses structural deficiencies in existing decision-making systems, where Foresight methods generate anticipatory insights but lack operationalization mechanisms, while machine learning algorithms automate processes but ignore strategic and participatory context as well as socio-organizational specificity. A systematic literature review following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology (16 publications in each block—Foresight and machine learning lifecycle) identified methodological gaps in both fields when compared against reference architectures. GSF(M)² synthesizes the strengths of both approaches by embedding Foresight logic into adaptive machine learning processes and integrating automated feedback loops into scenario planning. The result is a continuously learning ecosystem that recalibrates scenarios, model parameters, and strategic options in real time. The synthesis of anticipatory analytics, continuous horizon scanning, and data-driven prioritization enhances policymaking effectiveness and institutional agility under conditions of international and technological uncertainty. GSF(M)² represents the first dual-core framework for the co-evolution of strategic Foresight and adaptive algorithms within a unified reflexive governance architecture.
References
Abuzaid A.N. (2018) Scenario planning as approach to improve the strategic performance of multinational corporations (MNCs). Business: Theory and Practice, 19, 195–207. https://doi.org/10.3846/btp.2018.20
Amer M., Daim T.U., Jetter A. (2013) A review of scenario planning. Futures, 46, 23–40. https://doi.org/10.1016/j.futures.2012.10.003
Araujo G., Kalinowski M., Endler M., Calefato F. (2024) Professional Insights into Benefits and Limitations of Implementing MLOps Principles (arXiv preprint 2403.13115). https://doi.org/10.48550/arXiv.2403.13115
Berberi L., Kozlov V., Nguyen G., Sáinz-Pardo Díaz J., Calatrava A., Moltó G., Tran V., López García A. (2025) Machine learning operations landscape: Platforms and tools. Artificial Intelligence Review, 58, 167. https://doi.org/10.1007/s10462-025-11164-3
Burt G., Nair A.K. (2020) Rigidities of imagination in scenario planning: Strategic foresight through ‘unlearning’. Technological Forecasting and Social Change, 153, 119927. https://doi.org/10.1016/j.techfore.2020.119927
Chermack T. (2018) An Analysis and Categorization of Scenario Planning Scholarship from 1995–2016. Journal of Futures Studies, 22(4), 45–60. https://doi.org/10.6531/JFS.201806.22(4).0004
Cordova-Pozo K., Rouwette E.A.J.A. (2023) Types of Scenario Planning and Their Effectiveness: A Review of Reviews. Futures, 149, 103153. https://doi.org/10.1016/j.futures.2023.103153
Durance P., Godet M. (2010) Scenario building: Uses and abuses. Technological Forecasting and Social Change, 77(9), 1488–1492. https://doi.org/10.1016/j.techfore.2010.06.007
Eken B., Pallewatta S., Tran N.K., Tosun A., Babar M.A. (2024) A Multivocal Review of MLOps Practices, Challenges and Open Issues (arXiv preprint 2406.09737). https://doi.org/10.48550/arXiv.2406.09737
Faubel L., Schmid K. (2024) A Systematic Analysis of MLOps Features and Platforms. WiPiEC – Works in Progress in Embedded Computing Journal, 10(2), 97–104.
Godet M. (2001) Creating Futures: Scenario-Planning as a Strategic Management Tool, Paris: Economica.
Godet M., Durance P. (2011) Strategic foresight: For corporate and regional development, Paris: Dunod.
Hanchuk D.O., Semerikov S.O. (2024) Implementing MLOps practices for effective machine learning model deployment: A meta synthesis. In: Proceedings of the 7th CEUR International Workshop on Augmented Reality in Education (AREdu 2024) (eds. S. Semerikov, A. Striuk, M. Marienko, O. Pinchuk), Aachen: RWTH Aachen University, pp. 329–337.
John M.M., Holmström H.O., Bosch J. (2025) An empirical guide to MLOps adoption: Framework, maturity model and taxonomy. Information and Software Technology, 183, 107725. https://doi.org/10.1016/j.infsof.2025.107725
Kobes M., Loy T.R. (2020) Whatever Happened to Scenario Planning? A Systematic Literature Review (SSRN Paper 3536419). http://dx.doi.org/10.2139/ssrn.3536419
Kreuzberger D., Kühl N., Hirschl S. (2023) Machine Learning Operations (MLOps): Overview, Definition, and Architecture (arXiv preprint 2205.02302). https://doi.org/10.48550/arXiv.2205.02302
Lima A., Monteiro L., Furtado A.P. (2022) MLOps: Practices, Maturity Models, Roles, Tools, and Challenges. Paper presented at the 24th International Conference on Enterprise Information Systems (ICEIS 2022), April 25–27 (virtual event). https://doi.org/10.5220/0010997300003179
MacKay R.B., Stoyanova V. (2017) Scenario planning with a sociological eye: Augmenting the intuitive logics approach to understanding the Future of Scotland and the UK. Technological Forecasting and Social Change, 124, 88–100. https://doi.org/10.1016/j.techfore.2016.08.026
Mehmood Y., Sabahat N., Ijaz M.A. (2024) MLOps critical success factors – A systematic literature review. VFAST Transactions on Software Engineering, 12(1), 183–209. https://doi.org/10.21015/vtse.v12i1.1747
Najafabadi F.A., Bogner J., Gerostathopoulos I., Lago P. (2024) An Analysis of MLOps Architectures: A Systematic Mapping Study (arXiv preprint 2406.19847). https://doi.org/10.1007/978-3-031-70797-1_5
Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., Shamseer L., Tetzlaff J.M., Akl E.A., Brennan S.E., Chou R., Glanville J., Grimshaw J.M., Hróbjartsson A., Lalu M.M., Li T., Loder E.W., Mayo-Wilson E., McDonald S., McGuinness L.A., Stewart L.A., Thomas J., Tricco A.C., Welch V.A., Whiting P., Moher D. (2021) The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 89. https://doi.org/10.1186/s13643-021-01626-4
Ramírez R., Churchhouse S., Palermo A., Hoffmann J. (2017) Using scenarios planning to reshape strategy. MIT Sloan Management Review (Summer issue). https://sloanreview.mit.edu/article/using-scenario-planning-to-reshape-strategy/
Ramírez R., Wilkinson A. (2016) Strategic Reframing: The Oxford Scenario Planning Approach, Oxford: Oxford University Press.
Reich S. (2024) Reference Architectures for MLOps: A Comparative Case Study, Stuttgart: University of Stuttgart.
Sossa J.W., Álvarez Ríos V.T., Grajales López C.A., Palacio Piedrahita J.C. (2021) Foresight by scenarios – a literature review. International Journal of Foresight and Innovation Policy, 15(4), 230–249. https://doi.org/10.1504/IJFIP.2021.118801
Stone J., Patel R., Ghiasi F., Mittal S., Rahimi S. (2025) Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers. Paper presented at the 2025 IEEE Conference on Artificial Intelligence (CAI), 05–07 May 2025, Santa Clara, CA, USA. https://doi.org/10.1109/CAI64502.2025.00118
Subramanyam S.K. (2022) Robust MLOps Frameworks for Automating the AI/ML Lifecycle in Cloud Environments. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 307–316.
Symeonidis G., Nerantzis E., Kazakis A., Papakostas G.A. (2022) MLOps – Definitions, Tools and Challenges. Paper presented at the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA. https://doi.org/10.1109/CCWC54503.2022.9720902
Tagliabue J., Bowne-Anderson H., Tuulos V., Goyal S., Cledat R., Berg D. (2023) Reasonable Scale Machine Learning with Open-Source Metaflow (arXiv preprint 2303.11761). https://doi.org/10.48550/arXiv.2303.11761
Varum C.A., Melo C. (2010) Directions in scenario planning literature – A review of the past decades. Futures, 42(4), 355–369. https://doi.org/10.1016/j.futures.2009.11.021
Vecchiato R. (2012) Environmental uncertainty, foresight and strategic decision making: An integrated study. Technological Forecasting and Social Change, 79(3), 436–447. https://doi.org/10.1016/j.techfore.2011.07.010
Von der Gracht H. (2023) The Delphi method: How experts see the future. In: Strategic Foresight. An Introductory Guide to Practice (ed. J.O. Schwarz), London: Routledge, рр. 67–81. https://doi.org/10.4324/9781003302735
Zarour M., Alzabut H., Al-Sarayreh K.T. (2025) MLOps best practices, challenges and maturity models: A systematic literature review. Information and Software Technology, 183, 107733. https://doi.org/10.1016/j.infsof.2025.107733
This work is licensed under a Creative Commons Attribution 4.0 International License.