The current study examines the concept of AI readiness gap, which can be defined as the difference between current capabilities and what is needed to effectively implement and leverage intelligent automation in terms of parity with competitors. In order to identify factors behind AI readiness and determine the performance implications of AI readiness gap, the study makes use of quantitative mixed-methods methodology applied in relation to a sample of 847 organizations operating within five industries in the United States and United Kingdom. It is found that less than 25 percent of the survey respondents demonstrate AI readiness exceeding the benchmark of 50 out of 100, with the degree of AI readiness gap varying between industries, firms of different sizes, and quality of data infrastructure. Quality of data infrastructure (β = 0.41), density of AI talent pool (β = 0.34), and technology stack modernization (β = 0.31) account for the highest impact in the regression equation (R² = 0.53). The qualitative examination of 36 interviews conducted with executives and senior managers reveals four major themes based on experience, which are the disconnect between strategy execution, workforce anxiety and resistance, data management impasse, and leadership capability. A five pillar AI Organisational Readiness Framework will be recommended by this study, and it will be proposed that the AI readiness gap is not a technological issue but rather an organizational capabilities issue.
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