Economic Viability of AI-Based Dynamic Performance Monitoring in Construction: A Cross-Market Cost-Benefit Comparison

Adoption cost, not technical capability, is the binding constraint on AI-based performance monitoring in construction, particularly for small and medium enterprises and firms in reconstruction markets. This study quantifies deployment economics for an AI-based dynamic performance monitoring framework validated across an international case portfolio using a Delphi-confirmed KPI taxonomy. Three deployments ran the same architecture and codebase, differing only in the active KPI subset: a British Columbia SME contractor with estimated annual turnover of CAD 4.8M across up to 30 projects, a USD 30M residential compound in Erbil, and a USD 18M high-rise development in Baghdad. Two models are built. The firm-wise model amortises one deployment across the SME portfolio; the project-wise model sets full deployment cost against single-project value. Year 1 cost is USD 1,600 to 8,800 at 2025 prices, covering platform configuration, model calibration, serverless hosting and hardware, and excluding framework development, enterprise integration, cybersecurity and formal training. Cost is reported both per monitored project-month and as a share of value, at 0.04 to 0.25% of SME turnover and below 0.05% of Iraqi contract values, normalising for differing deployment windows, labour rates and organisational scale, and sits one to two orders of magnitude below ERP integration at USD 80,000 to 500,000, presented as a monitoring-layer cost reference rather than a claim of functional equivalence. Project profiles, timelines and stress events are drawn from the actual projects; monthly KPI values derive from project records, documented site conditions and published regional benchmarks. Benefits are therefore reported as calibrated estimates with explicit attribution discounts of 10 to 50% rather than audited actuals, and safety outcomes qualitatively, because short observation windows and low exposure hours make incident rates unstable at low values. Payback is computed undiscounted over a one-year horizon on upper-bound cost, falling below three months in both quantifiable cases at conservative attribution and under 12 and 23 months respectively when only administrative automation is credited. The Baghdad case is reported cost-side only, its two-month live window falling below the framework’s six-month minimum viable deployment boundary. The contribution is the segment-specific viability condition, not a universal payback claim.