Efficient AI Isn't About Doing Less.
Efficiency has a PR problem in AI. When you tell an engineering team their AI usage is inefficient, they hear a threat. That framing has done real damage.
When you tell an engineering team their AI usage is inefficient, they hear a threat. They hear: you're going to be told to use AI less, or use cheaper models, or submit requests through an approval queue. They hear the IT department arriving with a spreadsheet.
That's not what AI efficiency means. But the framing has done real damage — to the conversations Finance needs to have with Engineering, and to the decisions enterprises are making about how to govern AI spend.
The real definition
AI efficiency isn't about reducing capability. It's about eliminating waste without touching output. The same response, delivered for less. The same accuracy, achieved with fewer tokens. The same workflow, running on a model that fits the task rather than the most capable one available.
The inefficiency that costs enterprises the most isn't bold experimentation with frontier models. It's routine: duplicate queries answered at full inference cost, model selection driven by habit rather than task fit, context windows padded with information that contributes nothing to the output.
What the data shows
Semantic caching alone — serving cached responses to queries that are functionally identical — reduces inference spend by 50–80% in most enterprise deployments. The output is identical. The cost is a fraction. The user doesn't notice a difference.
Intelligent routing adds another layer: matching queries to the most cost-effective model capable of answering them accurately. Not every query needs GPT-4. Most don't. Routing them correctly doesn't degrade the product. It just stops the organisation paying frontier prices for commodity tasks.
The conversation Finance needs to have
The most productive framing for AI efficiency discussions isn't "we need to spend less." It's "we need to understand what we're getting for what we're spending."
That question — what are we getting? — is one most organisations cannot currently answer with numbers. When you can answer it, efficiency follows naturally. Not as a constraint imposed on engineering teams, but as a consequence of having visibility.
Teams optimise what they can see. Finance governs what is attributed. The board receives a number, not a narrative. Efficient AI isn't about doing less. It's about knowing what you're doing, and making sure it's worth doing.