Planning Concerts With AI: An Integrated Decision-Support Framework For Demand, Sustainability, And Economic Feasibility

Abstract

Concert organizers must balance financial success, operational efficiency, and environmental sustainability, yet these factors are often evaluated separately. Artificial intelligence and workflow automation offer opportunities for integration, but integrated decision-support systems remain limited in concert planning. This study develops an AI-assisted concert-planning decision-support system implemented in n8n. It combines user-defined event parameters with external artist-popularity metrics and currency exchange services to estimate audience demand, environmental indicators, and financial feasibility, and generate structured planning recommendations within one workflow. A case study based on Coldplay's Music of the Spheres World Tour concert at Olympiastadion Berlin demonstrates the framework's functionality. The system predicted attendance of 72,306 spectators, approximately 97% of stated venue capacity, and generated planning estimates for energy consumption, carbon emissions, waste, transportation, ticket revenue, costs, and profitability. Because complete verified concert-specific data were unavailable, these outputs demonstrate system functionality and plausibility rather than validate predictive accuracy or reconstruct actual concert performance. The framework illustrates how integrated AI and workflow automation can support efficiency, transparency, and consistency in pre-event decision-making. It also shows how environmental sustainability and economic viability can be assessed together rather than as independent objectives. Intended to support early-stage feasibility assessment, the system does not replace detailed operational analyses or comprehensive life-cycle assessments. It provides a practical approach to more sustainable, transparent, and data-driven planning in the live event industry.

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