Lilla Hortoványi

Strategy Without Templates

Adaptation in Digital Environments

4.4 The Failure of Benchmarking: Comparative Opacity and Hidden Mechanisms

Benchmarking has long served as a central mechanism of strategic learning. Its analytical value rests on the assumption that performance differences can be rendered interpretable through comparison. In this respect, benchmarking differs from best practices in its analytical focus. While best practices address what should be implemented by identifying practices associated with superior performance, benchmarking addresses where a firm stands relative to others by comparing performance outcomes. Best practices presuppose transferability. Benchmarking presupposes interpretability.

This distinction becomes critical in digital environments. While firms can observe performance differences, their capacity to interpret those differences is systematically constrained. Performance is increasingly mediated by algorithmic, infrastructural, and data driven mechanisms whose internal logic remains only partially visible (Kellogg et al., 2020). Firms can observe outcomes such as growth, visibility, customer reach, conversion, or market share, but they cannot fully access the causal processes that produce those outcomes. What is observable are comparative positions; what remains opaque are the generative mechanisms that produce them.

This creates a condition of comparative opacity. Comparative opacity arises when outcomes are observable but the mechanisms generating them remain partially hidden, distributed across multiple systems, or dependent on historically accumulated conditions that cannot be reconstructed from outside. Under such conditions, comparison retains descriptive value but loses explanatory power. Similarity or difference in outcomes no longer provides a reliable basis for inferring underlying processes.

Several mechanisms contribute to this opacity. Algorithmic mediation plays a central role. Platforms shape visibility, ranking, pricing, and access through computational systems whose logic is proprietary, adaptive, and continuously updated (Alaimo & Kallinikos, 2021; Kellogg et al., 2020). Firms interact with these systems through their effects rather than through direct knowledge of their operation. Data asymmetry further intensifies the problem. Successful firms often rely on accumulated interaction histories, proprietary data assets, and feedback loops that are not externally observable and cannot be reconstructed. Performance also depends on infrastructural embedding, meaning integration into specific interfaces, standards, and technical arrangements that vary across firms (Tilson et al., 2010). Finally, outcomes reflect historical layering. What appears to be the effect of current practice is often the cumulative result of prior decisions, adjustments, and positional advantages sedimented over time.

These conditions render benchmarking analytically constrained in digital contexts. Classically, benchmarking supports sensemaking by providing external reference points through which managers can notice deviations, interpret their causes, construct shared understanding, and guide action. In digital settings, however, this process becomes destabilized. Managers can still notice differences, but the interpretive link between observed cues and underlying causes is weakened. Firms can identify performance gaps but cannot reliably attribute those gaps to specific causal mechanisms. Benchmarking, therefore, continues to indicate where a firm stands relative to others, but it cannot establish why those differences arise.

This leads to a paradox that is especially pronounced in digital environments: greater transparency at the level of outcomes does not produce greater causal understanding. On the contrary, it amplifies the risk of misinterpretation by encouraging firms to infer causal relationships from patterns that are in fact highly contingent and temporally constructed. The implication is that digital environments fundamentally reshape the role of benchmarking. Benchmarking no longer reduces ambiguity by clarifying causal relationships.

Benchmarking, however, does not lose its value entirely. Rather, its epistemic status changes. It can no longer function as a mechanism for identifying best practice, yet the identification of observable differences remains relevant. Its role is reduced to that of an attention directing device. It highlights gaps in where the focal firm may perform better or worse. As a result, benchmarking supplies attention-directing signals. Managers must construct interpretations without access to the mechanisms that generate observed outcomes, relying more heavily on inference, experimentation, and situated judgment. The interpretation of whether a gap reflects a disadvantage or a potential opportunity rests on managerial sensemaking rather than on objective inference. Directing attention to a bounded set of observable differences nevertheless retains analytical value.

Consequently, in digital environments benchmarking does not eliminate uncertainty, but redistributes it by narrowing attention to a limited set of observable differences while leaving their causal interpretation unresolved. In this sense, benchmarking remains part of the strategy process through which uncertainty is navigated, even as its explanatory function declines.

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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