Lilla Hortoványi

Strategy Without Templates

Adaptation in Digital Environments

9.6 Interpretation: Knowledge Brokerage and Ongoing Calibration

Filtered signals and partial inference do not automatically become organizational knowledge. They have to be translated, questioned, and fitted to local decisions. For that reason, a final step is needed inside the firm. Organizations develop interpretive practices that make opaque outputs usable, and they increasingly rely on continuous calibration rather than strong forms of control.

One part of this process is knowledge brokerage. Waardenburg et al. (2022) show that specialized actors often translate algorithmic outputs into forms others inside the organization can use. These actors do not simply transfer information. They interpret outputs, connect them to context, and judge what is relevant enough to act upon. Lebovitz et al. (2022) show a related process in medical settings, where professionals do not accept AI outputs at face value but interrogate them in relation to domain knowledge. Feedback becomes usable only after it is placed in context.

Collective sensemaking also matters. When no single actor can fully understand how the system works, observations must be pooled and compared. Möhlmann et al. (2023) show this among platform workers who share informal theories about algorithmic management. Similar dynamics can emerge inside firms when teams compare observations across campaigns, channels, products, or time periods. The point is simple: feedback does not arrive as ready-made knowledge. It becomes knowledge through interpretation and social processing.

The mechanisms discussed thus far – selective visibility, causal opacity, demand construction, and inferential adaptation – explain how external infrastructures transform the signals organizations receive. Yet these filtered signals do not automatically become organizational knowledge. They have to be translated, questioned, and fitted to local decisions.

Once filtered signals have been translated into locally meaningful knowledge, the strategic problem does not disappear. It changes form. Organizations must still act under conditions where feedback remains selective and shifting, which is why strategy increasingly takes the form of ongoing calibration rather than strong control.

At the same time, strategy shifts from control to calibration. In classical strategy, control is often treated as the aim: analyze, plan, and shape outcomes. In algorithmically mediated environments, firms still act strategically, but they cannot fully predict how platforms will translate changes in content, timing, price, advertising, or quality signals into outcomes. Strategy, therefore, takes the form of tuning, monitoring, and revising rather than imposing a stable plan on a transparent system.

This calibration imperative has several practical consequences. Organizations move from periodic review toward continuous monitoring. They rely on provisional targets and frequent revision. They coordinate with external infrastructures and actors they do not fully command. Malgonde et al. (2022) show that recommender systems themselves are continuously recalibrated in response to endogenous reactions. Firms operating within such systems must do something similar. Stelmaszak et al. (2024) likewise show that decision-making in Uber is partitioned across humans and algorithms. Control is not absent, but it is segmented and shared.

The broader implication is that strategic competence changes form. In algorithmic settings, the strategist is less a planner acting on a fully intelligible system and more a calibrator working within an externally structured environment. This does not mean strategy disappears, rather strategy becomes the disciplined management of continuous adjustment under partial knowledge.

This final step is crucial for the book’s wider argument. Earlier chapters showed that organizations experiment, stabilize temporary solutions, and build partially integrated structures. The present chapter shows how those organizations remain aligned with changing conditions once feedback itself is selective, filtered, and difficult to explain. Without interpretation and calibration, earlier solutions quickly lose fit.

This calibration imperative has important implications for the patchwork architectures described in Chapter 8. Partially integrated structures are not merely the residue of past adaptations – they also shape how organizations interpret and respond to mediated feedback. When feedback itself is selective and opaque, firms cannot easily determine which components of their layered architecture are performing well and which are not. Therefore, calibration must proceed locally, across partially decoupled modules, rather than through system-wide optimization. This creates a recursive relationship: patchwork structures emerge from stabilized adaptations (Chapter 7), but those structures then constrain how firms learn from algorithmic feedback. Organizations become path-dependent not only in their structural configurations but also in their interpretive capacities.

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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