Strategic use of AI in product lines: Analysis of the role of AI-enabled process co-adaptation on environmental and operational performance.
TL;DR
The core findings show that both augmentation- and automation-based AI strategies contribute to product performance, though through different mechanisms, and underscore the importance of evaluating the role of AI technologies in product processes, depending on whether the strategic objective of such technologies emphasizes adaptability and responsiveness to customer demands or environmental outcomes.
Abstract
The study evaluates how the implementation of AI-enabled strategies (i.e., augmentation and automation) influences product environmental and operational performance. We also explore if the positive impact of such AI-based strategies amplifies in products whose processes are characterized by high levels of operational flexibility and adaptiveness. To test the proposed hypotheses, we apply multilevel regression models to a unique dataset of 138 product lines from Costa Rican manufacturing and professional service firms in 2024. The core findings show that both augmentation- and automation-based AI strategies contribute to product performance, though through different mechanisms. 'AI-augmented process co-adaptation' improves the connection between process adaptiveness and operational performance by leveraging human-AI collaboration to support rapid and effective product reconfigurations, whereas 'AI-automated process co-adaptation' amplifies the positive effect of process adaptiveness and environmental performance, thus ensuring that eco-efficient routines are executed consistently across product processes. These findings underscore the importance of evaluating the role of AI technologies in product processes, depending on whether the strategic objective of such technologies emphasizes adaptability and responsiveness to customer demands or environmental outcomes. By focusing on the product line as the unit of analysis, this study contributes to both the AI strategy and environmental management literatures by showing how distinct AI-based strategic logics interact with products' adaptive capabilities to generate superior environmental and operational performance. From a practical perspective, the study offers guidance for managers on configuring AI-enabled strategies in ways that align operational flexibility, sustainability objectives, and product-level value creation.