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Background: The idea of computational thinking is underpinned by the belief that anyone can learn and use the underlying concepts of computer science to solve everyday problems. However, most studies on the topic have investigated the development of computational thinking through programming activities, which are cognitively demanding. There is a dearth of evidence on how computational thinking augments everyday problem solving when it is decontextualized from programming. Objectives: In this study, we examined how computational thinking, when untangled from the haze of programming, is demonstrated in everyday problem solving, and investigated the features of such solvable problems. Methods: Using a multiple case study approach, we tracked how seven university students used computational thinking to solve the everyday problem of a route planning task as part of an 8‐week‐long Python programming course. Results and Conclusions: Computational thinking practices are latent in everyday problems, and intentionally structuring everyday problems is valuable for discovering the applicability of computational thinking. Decomposition and abstraction are prominent computational thinking components used to simplify everyday problem solving. Implications: It is important to strike a balance between the correctness of algorithms and simplification of the process of everyday problem solving. Lay Description: What is already known about this topic: Computational thinking is a problem solving skill.Computational thinking is different from programming.There is dearth of evidence on how computational thinking augments everyday problem solving. What this paper adds: Computational thinking practices are latent in everyday problem solving.Intentional restructuring of problems is required to uncover how computational thinking applies in solving them. Implications for practices: Decomposition and abstraction are critical in solving everyday problems.Computational thinking should be taught explicitly through everyday problems.Finding balance between the correctness of algorithm and simplification of the problem solving process is important. [ABSTRACT FROM AUTHOR]