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At NexusCon 2025, Blake Standen (Director, Technical Sales/Business Development, Brainbox AI) and Niharika Kishore (Sr. Sustainability Specialist, Amazon) walked through how Amazon is trying to squeeze real HVAC savings out of an operating portfolio that can’t pause for retrofits.
The core problem: HVAC is a “base load” for Amazon—often 30–40% of building energy—across more than 4,000 buildings in North America (~800 million sq ft). They get specific about why “just shut the building down and retrofit” isn’t an option (they cited ~$10M/hour in lost revenue if a facility goes offline), and what prerequisites Amazon had to put in place before a third-party optimization layer could even be considered.
Behind the paywall, you’ll hear the parts most teams gloss over: what it actually took to make an AI pilot executable inside a giant org, and why the technical work wasn’t the hardest part. They talk candidly about the six-month slog just to find the right internal owners (legal, FM, BAS experts, on-site techs, M&V), how they selected pilot sites to represent different building types/climate zones/ages (including buildings dating back to the 1980s), and why leadership education became its own workstream (especially when the question is “why can’t we build this in-house?”).
You’ll also get a clear look at the operational guardrails (when external systems have to disengage during peak periods) and how they structured measurement and verification so savings don’t get masked by operational changes like adding equipment.
Watch the full recording inside Nexus Pro →
At NexusCon 2025, Blake Standen (Director, Technical Sales/Business Development, Brainbox AI) and Niharika Kishore (Sr. Sustainability Specialist, Amazon) walked through how Amazon is trying to squeeze real HVAC savings out of an operating portfolio that can’t pause for retrofits.
The core problem: HVAC is a “base load” for Amazon—often 30–40% of building energy—across more than 4,000 buildings in North America (~800 million sq ft). They get specific about why “just shut the building down and retrofit” isn’t an option (they cited ~$10M/hour in lost revenue if a facility goes offline), and what prerequisites Amazon had to put in place before a third-party optimization layer could even be considered.
Behind the paywall, you’ll hear the parts most teams gloss over: what it actually took to make an AI pilot executable inside a giant org, and why the technical work wasn’t the hardest part. They talk candidly about the six-month slog just to find the right internal owners (legal, FM, BAS experts, on-site techs, M&V), how they selected pilot sites to represent different building types/climate zones/ages (including buildings dating back to the 1980s), and why leadership education became its own workstream (especially when the question is “why can’t we build this in-house?”).
You’ll also get a clear look at the operational guardrails (when external systems have to disengage during peak periods) and how they structured measurement and verification so savings don’t get masked by operational changes like adding equipment.
Watch the full recording inside Nexus Pro →
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This is a great piece!
I agree.