Smart Buildings Index

Number of digital systems running independently in a large commercial building: 10–20+¹

Number of connected devices in commercial buildings worldwide: ≈10–20 billion²

Share of building data unused or unanalyzed: ≈80–90%³

Share of global energy consumed by buildings: ≈30–37%⁴

Share of global energy-related carbon emissions from buildings and construction: ≈39%⁵

Variance among energy use in similar buildings: up to 3×⁶

Reduction in building energy use from smart systems: up to 25%⁷

Share of building operations that can be automated with existing technology: ≈30–50%⁸

Maintenance costs reduced by predictive analytics: ≈20–30%⁹

Equipment lifespan increased by predictive analytics: ≈20–40%¹⁰

Unplanned downtime reduced by predictive maintenance: ≈30–50%¹¹

Sources
¹ McKinsey & Company; Deloitte, Smart buildings: How IoT technology aims to add value for real estate companies
² IDC; Gartner; Statista estimates on IoT devices in commercial/enterprise buildings and smart infrastructure
³ IBM; Deloitte; Microsoft (Azure IoT) — widely cited estimate that 80–90% of IoT/building data is not used
⁴ International Energy Agency (IEA); UNEP GlobalABC — buildings account for ~30–37% of global energy consumption
⁵ World Green Building Council; International Energy Agency (IEA); UNEP GlobalABC — buildings and construction account for ~39% of global energy-related carbon emissions
⁶ U.S. Department of Energy; IEA — studies showing energy use intensity can vary by up to 3× across similar buildings due to operational differences
⁷ IEA; McKinsey; World Economic Forum — smart building systems (BMS, controls, analytics) can reduce energy use by up to ~25%
⁸ McKinsey Global Institute — estimates that ~30–50% of activities in many sectors (including facility operations) are automatable with existing technology
⁹ Deloitte; PwC; McKinsey — predictive maintenance reduces maintenance costs by ~20–30%
¹⁰ Deloitte; McKinsey; IBM — predictive maintenance can extend equipment life by ~20–40%
¹¹ McKinsey; Deloitte; IBM — predictive maintenance reduces unplanned downtime by ~30–50%