This special issue seeks empirical research demonstrating how artificial intelligence is embedded within and enhances digital production technologies in real operational contexts. The focus is on understanding implementation pipelines and socio-technical conditions that enable AI-enabled solutions to function effectively as operational workflows. Purely conceptual, literature-based, or modelling studies without empirical validation are excluded.
Acceptable empirical research uses real-world operational evidence such as production logs, IoT sensors, machine telemetry, or industrial case studies, potentially employing machine learning or optimization when grounded in real data and validated in industrial settings with measurable outcomes. Studies must address the full AI implementation pipeline, from translating production decisions into AI use cases, to ensuring data readiness, to embedding AI outputs into human-AI workflows linked to measurable production outcomes.
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