A MULTIDIMENSIONAL METHODOLOGY FOR ASSESSING THE ECONOMIC EFFICIENCY OF ARTIFICIAL INTELLIGENCE ADOPTION IN INDUSTRIAL ENTERPRISES
Keywords:
artificial intelligence, industrial enterprise, economic efficiency, multidimensional assessment, composite index, KISE-AI, productivity, cost efficiency, digital transformationAbstract
This study proposes a multidimensional methodology for assessing
the economic efficiency of artificial intelligence adoption in industrial enterprises. The
central argument is that conventional ROI is insufficient because AI value emerges
simultaneously through financial performance, productivity, resource efficiency,
quality, resilience, and organizational adoption. A composite KISE-AI (Kompleks
Iqtisodiy Samaradorlik – AI) index is introduced as a complementary decision-support
instrument, while discounted cash-flow indicators remain the primary investment
criterion. The methodology integrates normalized indicators, hybrid expert-data
weights, risk penalties, and a benefit-realization gate. It also defines an evaluation
architecture linking technical AI metrics to process KPIs and monetary outcomes. The
framework is designed for ex-ante portfolio prioritization and ex-post benefit
verification in manufacturing and other industrial contexts.
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