Foresight, Technological Uncertainty, and Strategic Decision-Making in Complex Systems

In recent decades, foresight and strategic planning have evolved into well-established domains supported by a wide range of analytical frameworks, managerial practices, and decision-support tools. Yet their practical application remains particularly challenging in sectors characterized by rapid technological change, institutional turbulence, and systemic interdependence. As digitalization accelerates and artificial intelligence reshapes economic structures, organizations increasingly operate in environments where traditional planning approaches struggle to capture the speed, scale, and uncertainty of technological transformation. The fundamental challenge is therefore not the absence of analytical tools, but the capability of organizations to interpret emerging signals and anticipate systemic change before it becomes visible in markets.

Research in technological forecasting and strategic foresight highlights the importance of developing structured anticipatory capabilities capable of integrating uncertainty into long-term decision-making. Contributions collected in the Handbook of Technological Forecasting and Strategic Foresight argue that organizations must increasingly move beyond linear forecasting models toward adaptive foresight practices combining technology roadmapping, horizon scanning, and scenario analysis. Foresight therefore functions less as a predictive instrument and more as a strategic capability enabling organizations to navigate technological discontinuities and complex systemic transformations (Manyuchi & Phaal, 2026).

The need for such capabilities is reinforced by the growing complexity of the global environment. As noted by Marina Dabić and Tugrul U. Daim, contemporary economies are shaped by overlapping forces including digital transformation, geopolitical realignments, financial crises, and institutional restructuring. These dynamics continuously reshape the formal and informal rules governing organizational behaviour. Under conditions of institutional turbulence, strategic planning must shift from prediction toward anticipation, requiring organizations to develop tools capable of detecting weak signals and structural shifts at an early stage (Dabić & Daim, 2026).

Technological foresight methods are particularly valuable in sectors shaped by emerging technologies. Studies of technological transitions show that innovations such as artificial intelligence, advanced computing infrastructures, and digital platforms generate nonlinear innovation trajectories that cannot be captured through traditional planning approaches. As demonstrated by Kudzai Manyuchi and Ralph Phaal, retrospective roadmapping allows organizations to analyze previous technological transitions and extract insights that can inform future innovation strategies. Understanding how past technological pathways evolved therefore becomes a critical mechanism for anticipating future systemic transformations (Manyuchi & Phaal, 2026).

Artificial intelligence provides a particularly illustrative example of technological uncertainty. Emerging technologies simultaneously create unprecedented opportunities and profound strategic ambiguity across multiple sectors, including healthcare, transportation, finance, and manufacturing. Research by Saeid Kousari and Saeid Ghazinoory highlights that technological breakthroughs often generate complex interactions between innovation systems, regulatory frameworks, and market structures. As the pace of technological change accelerates, foresight methodologies become essential instruments for exploring multiple plausible futures and guiding strategic decision-making under uncertainty (Kousari & Ghazinoory, 2026).

At the same time, the rapid diffusion of artificial intelligence is transforming the practice of foresight itself. According to Martino Agostini, generative AI systems are significantly reducing the cost and time required to produce strategic scenarios. As scenario generation becomes increasingly automated, the strategic advantage shifts toward interpretation, sense-making, and governance. When artificial intelligence makes scenarios cheap to produce, the critical capability becomes the human ability to interpret signals and construct coherent strategic narratives (Agostini, 2025a).

The proliferation of digital data further complicates strategic decision-making. Despite the exponential growth of available information, the quality of managerial decisions does not necessarily improve. Research by Vladimir Abramov and colleagues highlights a paradox of the artificial intelligence era: organizations possess unprecedented volumes of data but frequently struggle to transform this information into meaningful strategic insight. More data does not automatically lead to better decisions; structured analytical frameworks remain essential for converting information into strategic understanding (Abramov et al., 2026).

Empirical evidence from European enterprises reinforces the importance of foresight capabilities. Studies conducted by Maria A. Weresa and colleagues show that technology foresight initiatives improve organizational problem-solving capabilities, strengthen innovation strategies, and enhance long-term strategic alignment. Organizations that institutionalize foresight practices are therefore better positioned to detect emerging opportunities and mitigate systemic risks (Weresa et al., 2026).

The governance implications of these developments are increasingly visible in the context of artificial intelligence. As argued by Agostini (2026a), effective governance in technologically complex environments requires the development of futures literacy — the ability to use structured exploration of multiple possible futures to improve present-day decision-making. Futures literacy allows leaders to transform uncertainty from a source of strategic paralysis into a resource for strategic learning.

The emergence of autonomous AI agents further expands these governance challenges. As organizations deploy systems capable of autonomous decision-making, traditional governance mechanisms must adapt to supervise hybrid human–machine ecosystems. Corporate governance is therefore evolving from retrospective oversight toward anticipatory governance frameworks capable of managing human–AI decision systems (Agostini, 2025b).

This transformation is particularly evident in emerging agent economies, where artificial intelligence systems increasingly coordinate economic activity. Early experimental platforms demonstrate how AI agents can allocate tasks, verify outputs, and coordinate interactions with human workers through digital infrastructures. These developments indicate a transition from AI as a tool toward AI as an economic coordination layer embedded within complex socio-technical systems (Agostini, 2026b).

These insights align with broader theoretical frameworks developed in innovation studies and systems thinking. The multi-level perspective on socio-technical transitions developed by Frank W. Geels explains how technological change emerges through interactions between niche innovations, dominant industrial regimes, and broader societal landscapes (Geels, 2002). Similarly, the theory of techno-economic paradigms developed by Carlota Perez demonstrates that technological revolutions unfold through long cycles characterized by experimentation, diffusion, and institutional adaptation (Perez, 2002). Technological transformation therefore occurs as a systemic process involving complex interactions between technology, markets, institutions, and infrastructure.

From a systems perspective, foresight functions as a governance mechanism capable of addressing complexity and uncertainty. The interactions between technological innovation, institutional change, and economic transformation generate feedback dynamics that cannot be effectively managed through conventional planning approaches alone. Foresight methodologies convert uncertainty into structured strategic exploration, enabling decision-makers to examine alternative trajectories and design adaptive strategies (Meadows, 2008).

Consequently, foresight should not be understood merely as a predictive technique but as a core strategic capability embedded within organizational governance. Organizations that institutionalize foresight practices gain the capacity to transform uncertainty into strategic learning, innovation, and long-term resilience (Agostini, 2025c; Day & Schoemaker, 2005). In an era defined by accelerating technological change and systemic disruption, the integration of foresight into decision-making processes becomes essential for sustaining competitiveness and navigating complex socio-technical transitions.

References (APA 7th Edition)

Abramov, V., Trusina, I., Osipov, S., & Vagin, S. (2026). Scientific management culture in the age of AI: Balancing innovation, ecology, and strategic foresight. Scientific Culture.

Agostini, M. (2025a). When AI makes scenarios cheap, foresight must get smarter. Medium. https://medium.com/@tarifabeach/when-ai-makes-scenarios-cheap-foresight-must-get-smarter-415a13145023

Agostini, M. (2025b). How board members can stay relevant — and in the loop — in the AI agent economy: Corporate governance is still the name of the game. Medium. https://medium.com/@tarifabeach/how-board-members-can-stay-relevant-and-in-the-loop-in-the-ai-agent-economy-corporate-c51f2687effa

Agostini, M. (2025c). Beyond prediction: Understanding the strategic value of foresight vs. futures in the age of AI. Medium. https://medium.com/@tarifabeach/beyond-prediction-understanding-the-strategic-value-of-foresight-vs-futures-in-the-age-of-ai

Agostini, M. (2026a). Why futures literacy is the most critical leadership capability for AI governance in 2026. Medium. https://medium.com/@tarifabeach/why-is-futures-literacy-the-most-critical-leadership-capability-for-ai-governance-in-2026

Agostini, M. (2026b). When AI agents hire humans: RentAHuman.ai and the emerging agent economy. Medium. https://medium.com/@tarifabeach/when-ai-agents-hire-humans-rentahuman-ai

Dabić, M., & Daim, T. U. (Eds.). (2026). Handbook of technological forecasting and strategic foresight. Springer.

Day, G. S., & Schoemaker, P. J. H. (2005). Scanning the periphery. Harvard Business Review, 83(11), 135–148.

Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: A multi-level perspective. Research Policy, 31(8–9), 1257–1274.

Kousari, S., & Ghazinoory, S. (2026). Uncertainties and methods of futurology, forecasting, foresight, and futures research: A case study of artificial intelligence. In M. Dabić & T. U. Daim (Eds.), Handbook of technological forecasting and strategic foresight. Springer.

Manyuchi, K., & Phaal, R. (2026). Foresight insights from hindsight using retrospective roadmapping. In M. Dabić & T. U. Daim (Eds.), Handbook of technological forecasting and strategic foresight. Springer.

Meadows, D. H. (2008). Thinking in systems: A primer. Chelsea Green Publishing.

Perez, C. (2002). Technological revolutions and financial capital: The dynamics of bubbles and golden ages. Edward Elgar.

Weresa, M. A., Kowalski, A. M., & Lewandowska, M. S. (2026). Integrating technology foresight for problem-solving in European enterprises. In M. Dabić & T. U. Daim (Eds.), Handbook of technological forecasting and strategic foresight. Springer

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