This study develops a systematic methodology for identifying and prioritizing artificial intelligence (AI) applications according to enterprise sustainability improvement needs.
A quality function deployment (QFD)-based framework links organization-specific sustainability objectives (WHATs) with relevant AI applications (HOWs). Prioritization is based on normalized improvement scores at two hierarchical levels—sustainability objectives and AI applications—using the mean square error (MSE) criterion. The framework also incorporates synergies among AI applications. A supplementary analysis assesses implementation barriers across different manufacturing stages. The methodology was applied in a chemical processing company using expert assessments from a multidisciplinary QFD process.
Seven sustainability objectives and seven AI applications were evaluated. Greenhouse gas reduction emerged as the highest-priority sustainability objective. Multi-objective optimization platforms, AI-based predictive maintenance, and predictive process optimization received the highest AI-application priorities. The resulting rankings reflect the cumulative relationships between sustainability objectives and AI applications, organization-specific sustainability improvement priorities, and cross-application synergies. The supplementary analysis identified technological, operational, and organizational barriers that may affect AI implementation.
The results reflect organization-specific expert judgments and should not be interpreted as population-level estimates. Future research may examine inter-expert agreement, sensitivity, and post-implementation sustainability outcomes using larger expert panels and additional organizational settings.
The framework provides managers with a structured decision-support process for prioritizing AI applications and informing resource allocation and implementation planning.
This study extends and operationalizes an established QFD-based decision architecture for enterprise sustainability by linking organization-specific sustainability improvement priorities to AI applications and their synergies.
