The causal mechanisms linking Big Data Analytics Capability (BDAC) to AI-Driven dynamic capabilities: A mixed-methods investigation

In the high-stakes race for AI-driven agility, a shocking 89% of companies can't turn their huge expenditures in Big Data Analytics Capability (BDAC) into a long-term competitive edge. This widespread "algorithmic paralysis" arises from a significant oversight: an inadequate comprehension of the exact causal mechanisms that connect BDAC's diverse resources to the AI-specific dynamic capabilities—sensing, seizing, and transforming—that drive strategic renewal. The thorough mixed-methods research cuts through the confusion and shows the complicated socio-technical alchemy that is needed for success. By using structural equation modeling on 412 senior executive surveys from different S&P 500 industries, the study shows that BDAC's human resources, which are specialized skills in data science and strategic translation, are the most important factor in AI sensing capabilities (? = 0.72). Almost half (48%) of the important relationship between real BDAC infrastructure and AI capturing capabilities goes through strong data governance frameworks. Comparative case studies of six technology, healthcare, and manufacturing companies show two different, empirically validated activation pathways: the "Technical Leverage" route, which is common in high-BDAC tech firms, and the "Orchestration-Driven" pathway, which is common in medium-BDAC manufacturers, where human expertise and governance councils unlock transformative potential. The results give CEOs a plan they can use, showing that technology alone doesn't give a company an edge; instead, it's the careful coordination of infrastructure, human skills, and governance that closes the AI execution gap. Keywords: Big Data Analytics Capabilities (BDAC), AI-Driven Dynamic Capabilities, Data Governance, AI Literacy, Technical Leverage Pathway, Orchestration-Driven Pathway, Sensing, Seizing, Transforming, Competitive Advantage.