Lilla Hortoványi

Strategy Without Templates

Adaptation in Digital Environments

10.5 Calibrating to Algorithmic Signals

Chapter 9 showed that firms increasingly operate under conditions of feedback without control. They receive many signals, yet those signals are filtered, shaped, and partly generated by algorithmic systems that remain outside their control. This creates a difficult learning environment. Firms have data, but not transparency. They have outcomes, but not full causal visibility. The strategic challenge is to learn and adapt under conditions of abundant but opaque feedback. Learning under these conditions relies less on causal transparency and more on iterative inference and adjustment.

To do this, organizations need three closely connected capabilities: signal interpretation, adaptive calibration, and platform awareness. Together, these capabilities form what can be called algorithmic literacy. This is the ability to function effectively when performance is mediated by opaque computational systems.

Signal interpretation is the first capability. Organizations must learn to detect meaningful patterns in noisy, mediated data. As Chapter 9 argued, algorithmic mediation creates epistemic opacity. Firms can observe results, but they cannot fully see the mechanisms producing those results. This changes the nature of learning. Instead of learning through transparent causal explanation, organizations often learn through inference, pattern recognition, and careful comparison of outcomes across interventions.

Waardenburg et al. (2022) provide a useful insight here. They show that algorithms can sometimes create breaches in opacity – moments when hidden system logics become partially visible through anomalies, failures, or unexpected results. Such breaches do not make the system fully transparent, but they do provide occasions for inference. Firms can use these moments to build partial working theories about how algorithmic systems behave. In that sense, learning comes less from direct access to the code and more from careful observation of the effects.

Adaptive calibration is the second capability. It means adjusting behavior continuously in response to observed signals, even when underlying mechanisms remain only partly understood. This differs from classical learning models in which understanding is supposed to come first and action second. Under algorithmic mediation, understanding remains incomplete and provisional. Firms must act anyway. They form hypotheses, test them in practice, observe the outcomes, and revise their assumptions.

Research on algorithm sensemaking among platform workers makes this process especially visible. Möhlmann et al. (2021) show that workers often construct folk theories about how algorithmic systems function. These theories are imperfect, and some are partly wrong. Still, they may be good enough to guide practical action. What matters is not perfect understanding, but workable understanding that can be improved over time. The same logic applies at the organizational level. Firms need practical theories that support adaptation under opacity.

Platform awareness is the third capability. Firms must recognize that their performance is shaped not only by market competition, but also by platform governance. Alaimo and Kallinikos (2021) argue that multisided platforms function as operational logics that coordinate action through data, architecture, contractual conditions, and algorithmic control. These governance structures influence what firms can do, how visible they become, and what outcomes they can realistically expect. Strategic success, therefore, depends partly on understanding the rules, incentives, and constraints built into platform environments.

Tiwana, Konsynski, and Bush (2010) add an important lifecycle perspective by showing that platform governance challenges differ across phases of platform evolution. Early-stage platforms face one set of regulatory and coordination issues, while mature platforms face another. This means that firms cannot treat platform conditions as fixed. They must monitor governance shifts, anticipate changes in platform priorities, and adapt their strategies as the platform itself evolves.

Calibration to algorithmic signals also raises the issue of automation and human judgment. As algorithmic systems play a larger role in organizational decision-making, firms must decide whether to replace human judgment, augment it, or redesign decision processes more fundamentally. Page and Kallapur (2025) frame these alternatives as replacement, augmentation, and disruption. Each has strengths and risks. Heavy reliance on algorithmic outputs may increase speed and scale, but it can also deepen opacity and reduce interpretive control. Stronger human involvement may improve contextual judgment, but it may slow response. The challenge is to decide where algorithmic support should be trusted, where it should be questioned, and where hybrid forms of judgment are preferable.

A final complication is that algorithmic systems do not stand still. They are retrained, updated, and recalibrated. A strategy that worked well last month may fail after a platform update, even if the firm’s own actions have not changed. For that reason, calibration is never finished. Firms need the capacity to detect shifts in algorithmic behavior, update their working assumptions quickly, and remain strategically flexible under changing computational conditions.

The contribution of this section is to show that in digital environments, learning no longer rests mainly on transparent feedback loops. It rests on continuous calibration under opacity. That marks a clear break with classical views of feedback as a relatively clear mechanism of 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.

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

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