When Knowing Outpaces Doing: Knowledge Management and AI Implementation Gap in Innovation Partnerships

Abstract

Innovation partnerships (research consortia, digital innovation hubs, public-private collaborations) are increasingly expected to implement artificial intelligence technologies such as machine learning, computer vision, natural language processing, robotics, and data analytics. Yet many partnerships exhibit a persistent disconnect between AI knowledge and AI implementation. This knowing-doing gap (Pfeffer and Sutton, 2000) is well documented within individual firms, but its antecedents and consequences in multi-partner settings, where knowledge asymmetries, distributed governance, and heterogeneous absorptive capacities complicate knowledge transfer (Cohen and Levinthal, 1990), remain poorly understood. This study draws on survey data from 45 European innovation partnerships across 15 countries and employs a two-stage analytical design. First, OLS regression examines the knowledge management antecedents of the knowing-doing gap. Results (R² = .31, p = .01) indicate that measurement readiness significantly narrows the gap (B = −0.67, p = .015), while technology diversity (B = 0.41, p = .002) widens it. Second, hierarchical OLS regression examines whether the gap predicts innovation performance beyond baseline knowledge management variables. Adding the knowing-doing gap significantly improves the model (ΔR² = .10, F-change = 10.05, p = .003), with both the gap (B = 0.46, p = .003) and measurement readiness (B = 1.10, p < .001) independently predicting innovation performance. We introduce the concept of productive knowledge disequilibrium to theorise this finding: in rapidly evolving technological domains, the misalignment between action and measurement can function as a generative force, enabling learning-by-doing that complements formal knowledge management practices.