Improve Space Monitoring in CRE with AI Thermal Sensors

What AI thermal sensors measure

• Presence detection: whether a space is occupied or empty.
• Headcount estimates: approximate number of people in a room, expressed as safe ranges rather than identities.
• Dwell time: how long individuals or groups remain in a space.
• Traffic flows: movement patterns between zones and peak transition times.
• Space utilization: real-time and historical percentage of time a space is actively used.
• Multi-zone analytics: simultaneous monitoring of conference rooms, open areas, and amenity spaces.

These measures are processed into dashboards, alerts, or control outputs for downstream systems like building automation and workplace apps.

Key use cases in CRE

• Space utilization and portfolio decisions

  • Identify underused areas to reconfigure space, consolidate leases, or repurpose amenities.
  • Validate demand forecasts for lease renewals or expansions.

• HVAC and energy optimization

  • Adjust ventilation and heating in response to real occupancy rather than static schedules.
  • Reduce energy waste during nights, weekends, or in intermittently used areas.

• Facility operations and cleaning

  • Trigger cleaning cycles based on actual use to improve efficiency and lower labor costs.
  • Prioritize maintenance in high-traffic zones.

• Tenant experience and hybrid work support

  • Provide real-time desk and room availability without tracking individual identities.
  • Measure how hybrid policies change demand for desks, meeting rooms, and collaboration spaces.

• Safety and emergency response

  • Use occupancy snapshots to support evacuation accounting and informed emergency responses.
  • Detect unusual activity patterns that may indicate hazards.

• ESG and reporting

  • Quantify utilization-driven energy savings for sustainability reporting and certifications.

Implementation roadmap: practical steps

• Define objectives

  • Choose primary goals such as energy savings, space optimization, or operational efficiencies.
  • Set measurable success criteria and timelines.

• Conduct a pilot

  • Select a representative floor, building core, or cluster of meeting rooms.
  • Deploy a mix of wired and wireless sensors depending on power and connectivity needs.

• Plan sensor placement

  • Position sensors to cover entrances, work zones, and meeting rooms for complete coverage.
  • Ensure line-of-sight patterns avoid obstructions and HVAC interference.

• Integrate with building systems

  • Connect sensor outputs to building management systems, room booking platforms, and analytics dashboards.
  • Use APIs or middleware to translate occupancy signals into control actions.

• Establish data governance

  • Define retention policies, anonymization standards, and access controls.
  • Communicate privacy protections to tenants and stakeholders.

• Iterate and scale

  • Measure pilot KPIs, refine thresholds, and resolve false-positive patterns.
  • Roll out to additional assets using standardized installation and configuration playbooks.

Key performance indicators (KPIs) to track

• Utilization rate: percentage of occupied hours vs. available hours for desks, rooms, and floors.
• Peak occupancy and peak duration: highest simultaneous occupancy and how long peaks last.
• Average dwell time: time spent per visit in specific zones.
• Energy per occupied hour: energy consumption normalized to actual occupancy.
• Cleaning and labor efficiency: reduction in cleaning events or staff hours per area.
• Cost per square foot utilization: financial throughput relative to space usage.
• Tenant satisfaction: qualitative feedback correlated with availability and comfort improvements.

Track these KPIs before and after deployment to quantify impact.

Challenges and mitigations

• Coverage gaps

  • Mitigation: use overlap in sensor placement and combine spot sensors with environmental data to infer usage.

• False positives and negatives

  • Mitigation: refine AI models using site-specific training data and combine with door sensors or badge data where appropriate.

• HVAC and heat sources

  • Mitigation: calibrate for ambient heat sources such as sunlight and equipment, and use multi-sensor fusion to improve accuracy.

• Integration complexity

  • Mitigation: choose vendors with open APIs and experience integrating with BMS and workplace platforms.

• Tenant acceptance

  • Mitigation: share clear privacy statements, explain anonymization, and demonstrate tangible tenant benefits.

Selecting a vendor

• Sensing technology: accuracy of presence and people-count estimates and whether sensors are wired, wireless, or both.
• AI and analytics: granularity of insights, dashboards, and reporting capabilities.
• Privacy and compliance: explicit anonymization, data minimization, and governance controls.
• Integration capabilities: APIs, standard connectors, and experience with BMS and workplace systems.
• Deployment and support: ease of installation, calibration services, and ongoing maintenance.
• Proven success: case studies or pilots in similar CRE portfolios.

Butlr, for example, focuses on anonymous, AI-driven heat-based sensing and offers both wired and wireless sensors designed for intelligent building deployments.

Conclusion — strategic impact for CRE

AI thermal sensors transform space monitoring from guesswork into measurable, privacy-preserving intelligence. For CRE owners and operators, this enables smarter leasing strategies, lower operating costs, better tenant experiences, and measurable sustainability gains. Start with a focused pilot, define clear KPIs, and integrate sensor intelligence into building systems and operational workflows. With careful implementation and stakeholder communication, AI thermal sensing becomes a high-impact tool for modernizing CRE portfolio management.

• Audit your highest-value spaces for a pilot.
• Define 2–3 KPIs tied to business outcomes.
• Choose a vendor with anonymized thermal technology and open integrations.
• Run a 60–90 day pilot, analyze results, and create a scale plan based on quantified benefits.