Lilla Hortoványi

Strategy Without Templates

Adaptation in Digital Environments

9.1 From Direct Feedback to Mediated Signals

The first shift is a basic one. In classical feedback systems, organizations are assumed to face a relatively direct relation between action and outcome. A firm changes a product, adjusts a process, or launches a campaign, then observes the result and updates its behavior. The signal is treated as a reasonably direct reflection of market response.

Digital platforms disrupt this directness. They stand between firms and markets, and they do not merely pass information through. They shape what becomes visible and what becomes actionable through ranking systems, recommendation systems, pricing architectures, search design, and performance metrics. Algorithms influence which products are shown, which customers are reached, how competitors are ordered, and which outcomes the seller can actually see. Under these conditions, the firm no longer interacts with demand in an unfiltered way. It interacts with a platform-shaped representation of demand.

This transformation changes three things at once: observability, causality, and controllability.

First, observability is narrowed and structured. Not all outcomes are equally visible. Platforms decide which interactions are surfaced and which remain hidden. A seller may see click-through rates, conversion rates, review scores, or advertising metrics. Yet the seller does not see the weighting logic that determined which users encountered the product in the first place, which competitors were prioritized, or how relevance was calculated. What the firm sees is, therefore, only a selective window into a larger process.

Second, causality becomes harder to read. A rise in sales may come from a better product description, a temporary ranking boost, a competitor’s stockout, a change in platform criteria, a shift in user preferences, or some combination of these. The firm sees the outcome but often cannot separate the causes. The problem is not simply missing data. The problem is that the producing mechanism remains partly hidden.

Third, controllability becomes distributed. Firms can still change prices, keywords, descriptions, images, delivery promises, or advertising budgets. But they cannot directly control the ranking and recommendation systems that translate those inputs into outcomes. Control does not disappear, but it is no longer concentrated inside the firm. It is spread across platform rules, algorithmic infrastructures, rival behavior, and user responses.

Empirical research supports this view. Waardenburg et al. (2022) show that organizations need knowledge brokers to translate opaque algorithmic outputs into forms others can use. Lebovitz et al. (2022) show that medical professionals do not simply apply AI outputs. They compare them with domain expertise and judge when they can be trusted. Möhlmann et al. (2023) similarly show that platform workers engage in extensive sensemaking when dealing with algorithmic management. Across these settings, the same pattern appears: feedback is no longer self-explanatory. It requires interpretive work before it becomes meaningful for action.

This is the starting point of the chapter. The issue is not just that digital environments are uncertain. It is that organizations receive signals that have already been shaped by external systems. Learning, therefore, begins from a different place than in classical models.

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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