Optimize Space Utilization in CRE Using Thermal AI Sensors

Thermal AI sensor systems follow a pipeline from sensing to integration enabling both real-time controls and long-term planning.

System stages

• Sensing: Sensors detect heat signatures and transmit anonymized data.
• Edge processing: Some pre-processing filtering and privacy enforcement happens on device.
• AI inference: Algorithms count occupants track flows and classify space use.
• Analytics and visualization: Dashboards present metrics and trends for stakeholders.
• Integration: Data feeds into building management workplace apps or energy control systems to automate responses.

This pipeline enables real-time controls such as adjusting HVAC and long-term decisions like redesigning underused floors.

Thermal AI sensors support a wide range of commercial real estate applications across space operations leasing workplace experience and retail.

Space and portfolio optimization

• Identify underutilized areas to reconfigure or sublease.
• Measure true desk and room utilization for hybrid workplace strategies.

Energy and operations

• Link occupancy to HVAC lighting and ventilation control to reduce energy waste.
• Schedule cleaning and maintenance based on actual usage rather than fixed timetables.

Leasing and revenue management

• Provide prospective tenants with occupancy patterns to justify rents or concessions.
• Support dynamic space-as-a-service offerings like bookable desks and flexible suites.

Workplace experience and safety

• Monitor meeting room usage and reduce friction for employees.
• Use occupancy counts for emergency planning and compliance with capacity limits.

Retail and amenity optimization

• Analyze foot traffic to place signage services and concessions effectively.
• Measure dwell time near displays to inform merchandising strategies.

Meaningful KPIs turn sensor data into decisions. Define targets for these KPIs tied to financial and operational goals.

• Occupancy rate: Percentage of capacity in use over time.
• Peak utilization: Highest occupancy during a defined period.
• Average dwell time: Typical duration people spend in a space.
• Turnover and flow: Frequency of entries exits and movement between zones.
• Desk and room utilization: Percent of booked versus actual use for rooms or desks.
• Energy per occupant: Energy consumption normalized by active occupancy.

Follow a phased approach to reduce risk and demonstrate value when deploying thermal AI sensors across a portfolio.

1 Define objectives

• Identify business questions such as reducing energy spend repurposing floors or improving tenant metrics.
• Set measurable targets and timelines.

2 Pilot small iterate quickly

• Deploy sensors in one floor lobby or set of conference rooms.
• Validate accuracy privacy controls and integration paths.

3 Integrate systems

• Feed occupancy data into the building management system for automated controls.
• Connect analytics to workplace apps and leasing platforms.

4 Scale deployment

• Use learnings from the pilot to deploy across the portfolio.
• Standardize sensor placement data governance and reporting templates.

5 Operationalize insights

• Create decision workflows such as monthly space reviews and energy optimization cycles.
• Assign owners for data-driven initiatives and measure ROI.

Thermal AI sensors are inherently more privacy-friendly than cameras but governance is still essential to maintain trust and compliance.

• Ensure sensors do not store visual images and only capture anonymized heat data.
• Define retention policies and access controls for aggregated data.
• Communicate transparently with tenants and employees about what is collected and why.
• Align with local privacy laws and corporate policies.

Clear privacy practices increase tenant trust and reduce legal risk.

ROI from thermal AI sensors comes from multiple sources and varies by portfolio size and usage patterns. Estimate baseline costs pilot results and projected savings to build a business case.

• Reduced energy costs by aligning systems to occupancy.
• Savings from repurposed or subleased space.
• Better lease negotiations supported by utilization data.
• Lower operating costs through targeted cleaning and maintenance.
• Higher tenant retention due to improved experience.

Many customers realize payback within months to a few years depending on portfolio characteristics.

Thermal AI sensors provide CRE stakeholders with accurate privacy-preserving insights that unlock smarter space utilization energy savings and improved tenant experiences. By defining clear objectives running focused pilots and integrating occupancy data into operational systems property teams can turn heat-based sensing into measurable financial and environmental benefits.

For CRE teams exploring this approach consider vendors that combine robust thermal sensing with AI-driven analytics and strong privacy practices. Butlr is one such provider focused on anonymous heat-based sensing for intelligent buildings learn more at https://www.butlr.com/.