AI Energy Management Cuts Building Energy Use by 30%, KPMG Finds

KPMG: How AI Systems Can Cut Building Energy Waste by 30%

KPMG's latest research makes a clear point for executives: AI-augmented strategic energy management (SEM) can reduce energy use in commercial buildings by up to 30%. The approach prioritises eliminating waste before investing in expensive retrofits or new generation assets.

Decarbonisation rests on two levers-cleaner supply and lower demand. The report argues that traditional retrofits alone will not meet 2050 net-zero targets unless baseline consumption is first reduced through better operations.

What this means for management

• Start with demand reduction. It delivers faster savings and improves the ROI of any future retrofit or renewable deployment.
• Use AI to automate control decisions across HVAC, lighting, and schedules, then keep humans in the loop for oversight and accountability.

Proof in the field

"AI is already helping buildings cut waste by 20-30% in our projects, no matter the climate or the age of the property," says Donatas Karčiauskas, CEO of Exergio. He adds, "those savings only last if there's smart energy management behind them."

AI-augmented SEM: how it works

SEM frameworks connect to building control systems via IoT to adjust setpoints, schedules, and equipment sequencing in real time. Human operators supervise changes, validate comfort, and manage exceptions so efficiency becomes a continuous management task-not something postponed until the next renovation.

The three-tier efficiency model

• Tier 1 - Optimise existing systems: AI auto-tunes HVAC, lighting, and controls using occupancy and sensor data for quick, measurable savings.
• Tier 2 - Upgrade equipment: Replace inefficient boilers, chillers, and pumps after operational waste is removed to right-size capex.
• Tier 3 - Add clean supply: Deploy on-site renewables and arrange long-term electricity contracts once the baseline load is minimised.

Governance: human-centric AI

• Transparency: explainable recommendations, audit trails, and clear operating bounds.
• Guardrails: comfort bands, override rights, and fail-safe modes to protect occupants and assets.
• Continuous operations: anomaly detection, seasonal re-tuning, and weekly performance reviews.

Execution playbook (first 90 days)

• Weeks 1-2: Set targets and baselines (EUI, kWh/m², peak, emissions). Map meters, sensors, and control points.
• Weeks 3-4: Securely connect to BMS/IoT. Validate data quality and tag points for accurate control.
• Weeks 5-8: Pilot AI controls on select zones with hard comfort limits. Run A/B periods to quantify savings.
• Weeks 9-12: Scale site-wide, enable alerts, and establish an operator training and review cadence.

Metrics that matter

• Energy Use Intensity vs. weather-normalised baseline.
• Runtime and load factors for major assets.
• Peak demand and demand-response performance.
• Comfort compliance (temperature/humidity) and occupant tickets.
• Emissions per m² and per occupant.

Risk checklist

• Data quality: Fix sensor drift, gaps, and tagging before scaling to multiple sites.
• Change management: Define who approves setpoint changes and how exceptions are handled.
• Cybersecurity: Network segmentation, least-privilege access, MFA, and full change logs.
• Interoperability: Support for open protocols and exportable data to avoid lock-in.

Questions to ask vendors

• Where have you delivered 20-30% savings, and how were results measured?
• What comfort and safety guardrails are enforced by default?
• How are recommendations explained and audited?
• Can you integrate with our existing BMS and meters without rip-and-replace?
• What's the plan for seasonal re-tuning and ongoing performance assurance?

Where to learn more

For broader context on building efficiency, see the IEA Buildings overview and practical resources from the U.S. DOE Better Buildings program.

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