Lilla Hortoványi

Strategy Without Templates

Adaptation in Digital Environments

9 Abundant Feedback Without Control: The Loss of Strategic Visibility

In classical management theory, feedback is usually treated as a mechanism of control. Organizations act, observe the results, and then adjust accordingly. This feedback loop rests on three basic conditions. First, outcomes must be visible. Second, the relationship between action and outcome must be understandable. Third, actors must be able to change their behavior in response to what they learn. When these conditions hold, feedback supports learning, correction, and strategic refinement. It closes the loop between intention and outcome and provides the basis for cybernetic models of adaptation, as well as single-loop and double-loop learning.

In digital environments, these assumptions weaken. Feedback is still abundant, often more abundant than ever before, but its structure has changed. Firms receive many signals, yet these signals are filtered, ranked, transformed, and partly produced by external algorithmic systems they do not control. Platforms influence which products are shown, which customers are reached, how alternatives are compared, and how performance is measured. As a result, firms do not confront demand directly. They confront a platform-shaped version of demand.

This creates a central paradox. More data increases the volume and speed of signals, but algorithmic mediation makes those signals harder to interpret. Organizations face what can be called the interpretability gap: the distance between data abundance and causal understanding. This gap is not a temporary information problem that better analytics can solve. It is a structural condition produced by the distributed and proprietary nature of algorithmic systems. Managers face dashboards, analytics, and performance metrics that describe outcomes in great detail, yet these tools often say little about why those outcomes occurred. This is not just a practical inconvenience. It changes the conditions of organizational learning. Feedback no longer works as a straightforward tool of control. It becomes a mediated signal that must be interpreted, tested, and continuously adjusted to.

The main claim of this chapter is simple. In algorithmically mediated environments, organizations do not learn from feedback in the classical sense. They learn from signals that are selective, partly opaque, and shaped by infrastructures outside the firm. This changes what can be observed, what can be inferred, and what can be controlled. It also changes strategy. Strategy becomes less a matter of acting on transparent feedback and more a matter of adjusting to signals whose production the firm can influence only indirectly.

This chapter develops the argument in two steps. It first outlines a foundational transformation in how feedback operates under algorithmic mediation. It then identifies five mechanisms through which organizations respond to these conditions in practice. It proceeds as follows. It first shows how digital systems transform what organizations can observe, infer, and control. It then examines five mechanisms that follow from this shift: platforms shape visibility by determining what is surfaced and what remains hidden; outcomes become harder to explain as causality is distributed across systems and actors; demand itself is partly shaped by recommendation systems and interface design; organizations rely on inferential learning based on patterns rather than full explanation; and finally, they develop interpretive practices and continuous calibration to keep action aligned with changing conditions.

Together, these mechanisms explain a distinct learning problem. Firms still adapt, but they do so under conditions of partial visibility, causal ambiguity, and distributed control. Earlier chapters showed how firms experiment, stabilize temporary solutions, and build partially integrated structures. This chapter explains how those organizations continue to learn when the feedback they receive is immediate and consequential, yet not fully intelligible.

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.

Hivatkozás: https://mersz.hu/hortovanyi-strategy-without-templates//

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