Lilla Hortoványi

Strategy Without Templates

Adaptation in Digital Environments

9.8 Where Classical Learning Models Fall Short

The mechanisms point to a common conclusion: digital infrastructures do not merely add more information to organizational environments. They alter the conditions under which feedback can support learning and control. These mechanisms can be read as parts of a single process rather than as isolated effects. Organizational action no longer leads to directly interpretable feedback. Instead, it passes through systems that filter visibility, obscure causal relations, and shape the signals to which firms respond. Figure 4 summarizes this transformation and shows why learning increasingly depends on interpretation, inference, and continuous calibration rather than on straightforward control.

Figure 4 illustrates the chapter’s central argument: organizational actions do not generate directly observable and readily interpretable outcomes in digital environments. They pass through algorithmic systems that shape what becomes visible, what can be inferred, and what remains hidden. The resulting signals are rich in data but limited in interpretability. Organizations, therefore, respond by translating these signals into locally usable knowledge, forming provisional rules, and recalibrating their actions over time. Read in this way, the figure helps clarify why mediated feedback does not eliminate learning but changes its form. This is also why classical learning models remain useful yet have become insufficient: they assume a more direct relation between action, feedback, and correction than many digital environments now allow.

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Figure 4 Mediation and learning under algorithmic feedback

Source: Author’s own elaboration.

This chapter makes three theoretical contributions. First, it reconceptualizes feedback from a control mechanism into an interpretive challenge, showing that algorithmic mediation systematically transforms observability, causality, and controllability. Second, it demonstrates that demand is not an external given that firms observe, but a partly constructed signal shaped by platform infrastructures: a claim that challenges foundational assumptions in strategy and marketing theory. Third, it specifies how organizations learn under these conditions through inferential adaptation, knowledge brokerage, and continuous calibration rather than through causal understanding and corrective control. Together, these contributions explain why classical cybernetic and organizational learning models, while still valuable, are no longer sufficient for understanding strategic adaptation in algorithmically mediated environments.

Classical learning theories remain useful, but they are not sufficient for these conditions. Cybernetic models assume that feedback identifies deviations that can then be corrected. This presumes that the loop between action and outcome is legible enough for correction to be meaningful. Under algorithmic mediation, however, feedback is filtered through systems the organization does not control and cannot fully inspect.

Single-loop learning assumes that organizations can observe performance gaps and modify behavior accordingly. Yet under selective visibility, performance gaps may themselves be incomplete or misleading. Double-loop learning assumes that organizations can question the assumptions behind action. But when the connection between assumptions, visible outcomes, and hidden mechanisms is unstable, even reflective revision becomes difficult to anchor. Control theory also assumes a reasonably stable relation between inputs and outputs. In algorithmic environments, that relation can shift, remain hidden, or respond in nonlinear ways.

The point is not that classical theories become irrelevant. They still identify important dimensions of learning. The point is that they presuppose a more direct relation between action, feedback, and correction than many digital environments now allow. Firms no longer confront the market in an unfiltered way. They confront infrastructurally produced representations of the market.

This chapter’s contribution is, therefore, to show that the problem is no longer only uncertainty. It is selective visibility, causal opacity, mediated demand, and distributed control. Under such conditions, organizations do not learn primarily by extracting stable causal knowledge from transparent feedback. They learn by interpreting filtered signals, testing local adjustments, and recalibrating action under changing platform conditions.

This also strengthens the larger argument of the book. Strategy Without Templates is not simply about more turbulence. It is about a change in the mechanisms of adaptation. Experimentation generates variation, stabilization retains workable responses, partial integration gives those responses temporary structure, and mediated feedback guides their ongoing adjustment. The organization remains adaptive, but not through transparent control.

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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