The new 2026 research from the research team at Memoori, is presented as a rigorous independent assessment of the state of artificial intelligence (AI) in commercial buildings. It assesses 69 AI use cases across 12 application domains, grading every performance claim against an evidence framework that separates independently verified outcomes from vendor-reported figures. It is the first in a two-part series covering the full AI smart buildings landscape through 2031.
The AI story in commercial buildings is more complicated than the headlines suggest. While the researchers found that corporate AI investment reached $252.3 billion globally in 2024, and survey data shows 92% of commercial real estate organisations are now piloting or planning AI, the conversion to meaningful results has been startlingly poor: fewer than 5% report achieving most of their AI programme goals.
This is the third edition of Memoori’s analysis of artificial intelligence in smart commercial buildings, extending editions published in 2021 and 2024. It is the first in a two-part series. This volume examines market dynamics, technology foundations, use cases, and the opportunity landscape.
The research draws on programme evaluations from NYSERDA, NREL, LBNL, and the DOE; peer-reviewed academic research; industry surveys; and systematic analysis of vendor case studies assessed against an explicit evidence-grading framework that distinguishes independently verified outcomes from vendor claims.
Highlights of the report:
● The most under-appreciated barrier to AI in commercial buildings, according to Memoori, is neither model sophistication nor cloud cost, it is data readiness. In documented deployments, up to 75% of engineering effort and budget goes to making existing building systems legible to the analytics layer, not to the AI itself.
● Vendor-reported energy savings commonly cite 20–50%, but portfolio-scale independent evaluations consistently find verified savings of just 3–15%. NYSERDA’s Real-Time Energy Management program — the largest independent evaluation to date, covering 654 sites — found vendors claimed roughly double what was independently verified.
● A new and largely overlooked risk has emerged on the insurance side. From January 2026, standardised ISO endorsements introduce absolute AI exclusions covering bodily injury and property damage arising from machine learning systems. The more autonomous control granted to AI, the wider the potential coverage gap.
Energy management is the only domain in the top deployment tier, scoring 15.3 out of 20. But even here, the evidence reveals a critical hierarchy of outcomes. Passive dashboards deliver around 2–3% energy savings; fault detection and diagnostics around 9%; and autonomous supervisory optimisation achieves verified electric savings of approximately 12–13% in independently evaluated programmes. The distinction between alerting a facilities manager to a fault and autonomously correcting it is not marginal; it is order-of-magnitude.
An important counter-intuitive finding from the independent evidence base is that smaller commercial buildings consistently outperform larger ones under rigorous evaluation, suggesting that light commercial buildings, historically underserved by sophisticated vendors, may represent a disproportionate near-term opportunity.
The energy management domain is also expanding to encompass grid-interactive commercial buildings, virtual power plants, EV charging integration, and, critically, automated measurement and verification, which is becoming a strategic battleground determining who controls the source of truth for energy savings claims.
