IoT-enabled adaptive workplace systems: economic viability and spatial optimization through real-time reconfiguration

Purpose This research quantifies the economic viability and spatial efficiency of Internet of Things (IoT)-enabled adaptive furniture systems (IoT-hybrid adaptive and full robotic) in corporate real estate contexts. This study aims to address the persistent misalignment between static workplace configurations and dynamic occupancy patterns, which results in 62.2% average vacancy rates despite substantial real estate investments. Design/methodology/approach This study used a sequential mixed-methods design comprising four integrated phases: (1) empirical analysis of 12.4 billion occupancy data points collected from 130 organizations over a 36-month observation period; (2) development and testing of a functional prototype incorporating Arduino-based sensor networks with multiple control mechanisms governing three actuation systems; (3) computational spatial analysis using digital twin modeling; and (4) economic evaluation using net present value and internal rate of return calculations across diverse market contexts. Findings Adaptive systems achieved 60% space reduction in time-weighted analysis (65–70 square feet per person versus 92 square feet conventional), while fully robotic configurations achieved 46.2 square feet per person with 77% improvement in spatial efficiency. The spatial case study demonstrated capacity improvements from 19 workstations (conventional) to 28 (adaptive) to 40 (fully robotic) within identical 2,179-square-foot floor area. Economic analysis identified market-dependent viability thresholds: full robotic systems require annual rental rates exceeding $75 per square foot (5%–8% market coverage), hybrid adaptive systems prove viable above $48 per square foot (42% market coverage), while IoT-informed reallocation strategies achieve positive returns at just $21 per square foot (95% market coverage). Practical implications Corporate real estate executives could consider implementing tiered automation strategies based on local market conditions: IoT-based reallocation for markets below $48 per square foot, hybrid adaptive systems for $48–75 per square foot markets and full robotic automation only in premium markets exceeding $75 per square foot. The framework provides specific decision criteria and phased implementation guidance. Originality/value This study presents a novel integration of empirical validation, prototype testing, spatial case study analysis and market-specific economic modeling to establish adoption thresholds for adaptive workplace technologies adoption in corporate real estate.