AI IN SUSTAINABILITY
Q. HOW CAN AI PROVIDE MEANINGFUL LEVERAGE IN HELPING SMALL TEAMS SCALE THEIR IMPACT WITHOUT ADDING HEADCOUNT?
» Saleh: This question hits on the promise of recent AI advancements: you are no longer constrained by the size of your team or the number of hours you have in a day. A small team can replicate the expertise and scale of the most advanced sustainability and energy optimisation organisations. We’ ve seen this promise delivered across our customer base, with many 1- or 2-person teams managing massive global operations, like Aadhar Kulshrestha and Meredith Smith at TTI, Inc. or Ian Pope at McCarthy Building Companies. is hiding in plain sight in the data companies already have. AI makes it practical to surface those inefficiencies at scale, across hundreds of sites and dozens of energy sources, without an army of sustainability or energy efficiency consultants.
We go further, using AI to model the cash flow of the opportunities that shake out of that exercise and generate a vendormatched project that translates the exercise into improvements to your bottom line – power factor corrections with capacitors, demand response contracts with your utility, energy generation and storage systems.
Q. AS AI ADOPTION ACCELERATES, HOW DOES GRAVITY THINK ABOUT THE TECHNOLOGY’ S OWN SUSTAINABILITY FOOTPRINT?
» Ted: It’ s a fair question and one we take seriously. The compute costs of running AI models are real, and we’ re deliberate about where we deploy them, focusing on workflows where the value delivered meaningfully outweighs the energy cost of the models.
The broader point is that the emissions reductions we enable across our customer base far outweigh the compute required to run our models – but that’ s not a license to be careless. We’ re building efficiency into how we use AI, the same way we help our customers build efficiency into their energy use.
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