An AI product that promises lower energy use, less waste, and higher profits deserves a careful look at the evidence. Those are three different outcomes. A company can improve one without improving the others, and a polished sustainability report does not prove that an improvement occurred.
If you help choose software at work or run a small business, start with a specific operational problem. You do not need to become a climate scientist to ask useful questions about a supplier’s claims.
Define the proposed change
Replace “make our business greener with AI” with a concrete question. Perhaps a cafe wants to reduce unsold food, or an office wants to understand electricity use outside working hours. Identify the activity, the person responsible, and the information available.
Ask what the AI actually does. Does it forecast demand, identify unusual meter readings, recommend a schedule, or directly control equipment? A reporting tool and a control system need different checks. A chatbot that summarizes a bill is not itself managing the building.
For your first evaluation, keep changes under the supervision of the appropriate operator. Do not let a general chatbot alter safety-critical settings or substitute for qualified building, electrical, or food-safety expertise.
Build a claim-and-evidence sheet
Copy the supplier’s exact claim into a document, with the source and date. Next to it, record the measured outcome, comparison period, sites included, exclusions, and who checked the result. If the supplier provides only a percentage, request the underlying quantities and method.
For example, “20% less waste” raises several questions: less than when, measured by weight or cost, across which products, and during what level of sales? A trial at one location may be informative without demonstrating the same result at yours.
Give AI this limited task:
Review the supplied proposal. Make a table of environmental claims, business claims, evidence provided, assumptions, and unanswered questions. Separate measured results from forecasts and marketing language. Use only this document. Do not invent supporting studies or decide that the product is compliant, sustainable, or profitable.
Compare the table with the original. AI may turn “could reduce” into “reduces,” or omit a qualification in a footnote. Preserve those distinctions.
Choose a fair comparison
Record how the current process performs before the trial. Decide what you will count, how often, and who will check the records. Account for obvious changes such as opening hours, production volume, weather, occupancy, or a menu change.
Imagine a fictional cafe that discards less food in a quiet week after installing forecasting software. That observation alone does not show that the software caused the reduction. Compare similar operating conditions and examine whether stock shortages or missed sales increased.
Test a simple alternative too. Better ordering notes or an existing timer may solve the problem with less cost and complexity. Your decision is about the best workable approach, not about proving that AI was necessary.
Separate energy, emissions, and spending
A smaller bill can reflect a tariff change rather than reduced electricity use. A reduction in electricity use is not automatically the same percentage reduction in emissions. Define each measure and use an appropriate method rather than allowing AI to substitute one for another.
The International Energy Agency’s Energy and AI report examines both AI’s potential applications in the energy system and the electricity required to operate it. That two-sided view matters: include the new service’s relevant equipment, computing, and operating requirements in your assessment.
You may not be able to calculate every environmental effect yourself. Record missing information honestly and ask the supplier or a qualified specialist to explain it. Avoid presenting a rough internal estimate as a verified carbon account.
Count the work around the software
Include setup, sensors if needed, subscriptions, training, data preparation, checking, maintenance, and dealing with mistakes. Assign an owner for the system after the demonstration ends. Ask how you can export useful records and return to the previous process if the trial disappoints.
A tool that produces a clear report but requires hours of weekly correction may be poor value. Conversely, a modest improvement can still be useful if the process is reliable and manageable. Keep those judgments separate from claims about total environmental benefit.
Communicate only what you can support
In the United States, the FTC’s Green Guides summary cautions against broad, unqualified environmental claims and calls for specific, substantiated benefits. Other jurisdictions have their own requirements. AI-generated marketing language does not establish compliance.
For an internal trial report, state the site, dates, measurement, comparison, and limitations. Do not turn a small reduction in one waste category into “our business is carbon neutral.” Have public claims reviewed by someone qualified to assess the evidence and applicable requirements.
At the end, choose to continue, modify, or stop the trial based on the records. A good AI sustainability project should be explainable in ordinary language: what changed, what was measured, what remains uncertain, and why the benefit justifies the work.
AI disclosure: This article was written and revised with AI assistance. Sources checked September 25, 2026. See our disclaimer.
Illustrative photograph by American Public Power Association on Unsplash.
