Lilla Hortoványi

Strategy Without Templates

Adaptation in Digital Environments

3.1 The Failure of Foresight Under Structural Uncertainty

In template-free environments, experimentation is not an episodic activity within strategy but its default mode of operation. Classical strategy theory assumes that firms can analyze their environment, anticipate future developments, and design courses of action accordingly (Porter, 1980; Andrews, 1971). Even in more dynamic frameworks such as dynamic capabilities, strategy remains anchored in the ability to sense, seize, and transform based on interpretable environmental signals (Teece, 2007). These approaches recognize uncertainty but retain a crucial assumption: the environment can be sufficiently interpreted to guide purposeful action.

This assumption rests on the idea that knowledge precedes action. Firms are expected to know, then choose, then act. Even when adaptation is emphasized, it is typically understood as adaptation to a world whose relevant structure can, in principle, be known before decisive commitment.

In template-free environments, this sequence becomes structurally unstable. The problem is not simply that prediction becomes more difficult. The problem is that the conditions that would make prediction reliable are themselves weakened. The structures of coordination, visibility, and access do not remain stable long enough to support robust foresight. Therefore, the strategist is not merely information-constrained but structurally constrained.

This distinction is critical. Under informational uncertainty, better data or improved analytics can reduce ambiguity. Under structural uncertainty, the environment itself evolves, selection criteria change, and interaction mechanisms are recursively modified (Faraj et al., 2022; Nambisan et al., 2017). Under such conditions, the future cannot be reliably inferred from the past, and analysis loses its status as a sufficient basis for action.

The distinction between informational and structural uncertainty marks a fundamental theoretical divide that has been insufficiently recognized in strategy research. The difference is not simply that there is more uncertainty, but that the nature of uncertainty itself is different. Informational uncertainty presumes a stable structure within which particular parameters remain unknown. Therefore, the strategic problem is one of knowledge acquisition: gathering data, refining estimates, and reducing ambiguity about variables whose causal roles remain fundamentally stable. Under such conditions, learning operates through variance reduction. As firms accumulate experience, confidence intervals narrow, predictions improve, and action becomes more precisely calibrated to environmental conditions. The environment remains a stable object that becomes progressively knowable through observation and analysis.

Structural uncertainty is different. It arises when the causal architecture itself is unstable. The problem is not unknown parameters within a known structure but an unknown and evolving structure. Variables may enter or exit the relevant system. Relationships between action and outcome may reverse. Feedback loops may amplify or weaken effects in unpredictable ways. Under structural uncertainty, learning does not operate primarily through variance reduction but through a process of configuration search. Firms do not converge on increasingly accurate models of a stable world; they continuously construct provisional models of a world that is changing itself.

This distinction generates divergent strategic logics. Under informational uncertainty, analysis precedes action. Firms invest in data collection, market research, and forecasting in order to reduce ambiguity before committing resources. The objective is sufficient confidence to justify decisive action. Under structural uncertainty, action precedes understanding. Firms must act in order to reveal the current configuration of the system, because that configuration cannot be inferred reliably from historical data alone. The objective is not confidence, but adaptive capacity: the ability to interpret feedback, adjust course, and maintain viability despite ongoing structural change.

Critically, structural uncertainty cannot be resolved through better information. More data does not reduce structural uncertainty if the structure itself is moving. This helps explain the recurrent failure of data-intensive approaches in digitally mediated environments. Firms accumulate enormous quantities of behavioral data, engagement metrics, and performance signals, yet remain unable to predict outcomes reliably. The problem is not data scarcity, but structural instability: the rules linking action to outcome are themselves evolving, rendering historical data an unstable guide to future performance.

Prior literature has partially recognized these limits. Mintzberg and Waters (1985) showed that realized strategy often diverges from intended strategy, while March (1991) emphasized the role of exploration under uncertainty. However, these perspectives retain an implicit stabilizing assumption: that learning processes will eventually yield more reliable guidance for action.

This is precisely where they become insufficient.

In template-free environments, learning itself cannot stabilize reliably. The environment continuously alters the conditions under which past learning remains valid. What is learned today may lose relevance tomorrow due to changes in platform governance, algorithmic selection, or ecosystem configuration. As a result, the relationship between experience and future action becomes unstable.

This leads to a fundamental shift. Knowledge is no longer a prerequisite for action but increasingly an outcome of it. Strategy cannot precede action in a fully specified form; it must emerge through action. Foresight does not disappear, but it loses its role as the dominant organizing principle. It becomes provisional, partial, and continuously revised.

The theoretical implication is consequential: strategy theory must differentiate. Frameworks developed for informational uncertainty – such as probabilistic forecasting, scenario planning, or real-options reasoning – remain useful where causal structure is stable enough to sustain them. But they become insufficient under structural uncertainty, where the assumptions underwriting their coherence no longer hold. Therefore, a distinct theory of strategic action is required: one that does not aim primarily to reduce uncertainty, but to enable action despite its irreducibility.

The strategist, therefore, moves from a position of design to one of engagement: not specifying the future in advance but constructing partial knowledge through iterative interaction with an evolving system.

Tartalomjegyzék

Kiadó: Akadémiai Kiadó

Online megjelenés éve: 2026

ISBN: 978 963 664 204 4

Gazdaság

What happens when understanding comes only after action has already begun?

Traditional strategy rests on the assumption that organizations can understand their environment before deciding how to act. Yet the conditions that once allowed organizations to rely on benchmarking, best practices, and proven strategic templates can no longer be taken for granted. Today, organizations increasingly face situations for which no clear roadmap exists. Established assumptions become less reliable, familiar reference points lose their clarity, and strategic decisions must be made before their consequences can be fully understood.

Strategy Without Templates explores how organizations learn, adapt, and navigate environments in which uncertainty is pervasive and established templates are absent or no longer sufficient. Instead of treating strategy as a process of prediction and planning, the book explores how strategic paths take shape through action, experimentation, adjustment, and learning.

A central insight in the book is that temporary solutions are often necessary. What begins as a practical response to an immediate challenge may gradually shape future possibilities in unexpected ways. Some solutions create new opportunities and sources of advantage. Others become constraints that are difficult to overcome.

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