Assessing industrial enterprise readiness for artificial intelligence implementation as a basis for strategic digital transformation directions

The relevance of the study is driven by the necessity for industrial enterprises to transition from fragmented experiments with artificial intelligence (AI) to its systemic implementation as a driver of digital transformation. Despite growing investments in Industry 4.0 technologies, a gap persists between ambitions and tangible outcomes. The core problem lies in the absence of a standardized tool for the objective diagnosis of organizational readiness — a company’s ability not only to launch a pilot project but also to ensure the sustainable integration, scaling, and continuous development of AI solutions across the entire value chain. The aim of the research is to bridge this methodological gap by developing, testing, and verifying an Integrated Enterprise AI Readiness Index (AIRI), and to define differentiated strategic trajectories for industrial enterprises with varying levels of digital maturity based on this instrument. Research methods include systems analysis for structuring success factors, comparative analysis for identifying best practices and international trends, as well as the in-depth case study method for empirical validation. The developed index is a weighted integrated model that quantitatively assesses five interrelated components of organizational maturity: data readiness, process maturity, technological architecture, human capital and competencies, and strategy and governance. Validation on five enterprises from different industrial sectors revealed a significant variance in readiness levels and confirmed the tool’s high diagnostic value. Typical «bottlenecks» were identified, such as data fragmentation and competency deficits, which hinder transformation. It has been proven that the key success factor for digital transformation is organizational and process maturity, not merely technological sophistication. The practical significance lies in providing management with a tool for audit, investment prioritization, selection of adequate AI solutions, and realistic forecasting of their return. Research prospects include refining the index's weighting coefficients for various industries, integrating it with strategic management systems, and conducting cross-cultural comparative studies.