Beyond AI's Energy Footprint: What The Sustainability Conversation Misses

Mikko Kärkkäinen, CEO, RELEX Solutions.

​The sustainability debate around AI has largely focused on energy consumption. Data centers draw measurable power, model training has a calculable carbon cost and inference requests can be metered. These impacts are visible and auditable, often attracting attention from boards, regulators and ESG teams.

The response is predictable. Companies publish AI energy commitments, invest in offsets and treat AI as a sustainability liability to be managed. That framing isn’t wrong, but it’s incomplete.

Some of the highest environmental costs in retail and manufacturing never appear on the same ledger as energy use. Overproduction, excess inventory, food spoilage and half-empty trucks generate significant emissions, land-use pressure and water consumption that don’t show up in a sustainability report.​

The International Energy Agency (IEA) estimates that data centers account for roughly 1% of global energy-related carbon dioxide emissions. That footprint is real and important to manage. But energy is only part of the picture. The UN Environment Programme (UNEP) estimates food loss and waste at around 8% to 10% of global greenhouse gas emissions, making the climate cost of food waste roughly 10 times that of all data centers worldwide.

That ratio reframes where sustainability attention should be directed. When AI is applied to the planning decisions around demand, replenishment and network coordination, the waste avoided dwarfs the energy spent running the systems.

The sustainability implication is direct. Deploying an LLM for a problem a lighter, purpose-built model could handle well adds to the very footprint sustainability teams are working to reduce. As the range of available AI tools grows, organizations need a deliberate framework for matching computational approach to task type, treating energy efficiency as a selection criterion alongside accuracy and cost.