When Does AI Pay Off? Environmental Dynamism as a Contingency for AI-Enabled Competitive Strategies

Purpose

Despite accelerating organizational investment in artificial intelligence (AI), the conditions under which AI-enabled competitive strategies translate into performance gains remain poorly understood. This study examines how three AI-enabled leveraging strategies, entrepreneurial opportunity, market opportunity, and resource advantage, affect competitive performance, and whether environmental hostility and market dynamism moderate these relationships.

Methodology

Drawing on resource orchestration theory and environmental contingency perspectives, we develop and test a moderated model using partial least squares structural equation modeling (PLS-SEM) on survey data from 105 organizations in the United Kingdom.

Findings

Environmental conditions are the dominant predictors of competitive performance, absorbing the direct effects of all three strategy types. Crucially, a significant interaction between market opportunity strategies and market dynamism reveals that AI-driven market responsiveness yields stronger competitive returns specifically in dynamic environments.

Originality

This study is among the first to introduce environmental contingency as a boundary condition for the AI strategy–performance relationship, demonstrating that the returns to AI-enabled strategies are context-dependent rather than universal. It extends resource orchestration theory by specifying the environmental conditions under which leveraging processes create value.

Practical implications

Managers should calibrate their AI strategy mix to their competitive environment. In dynamic markets, investment in AI-powered market sensing and customer responsiveness should be prioritized; in stable environments, expectations about competitive returns from market-oriented AI strategies should be tempered.