You would not use a Ferrari for the school run. Why are you doing it for AI?

Not everyone owns a Ferrari, but I’m sure those who do rarely use it to pick their children up from school.

It is less energy-efficient, not as safe or spacious and may garner unwanted attention.

The analogy is simple: in AI, you do not always need the most powerful tool for everyday tasks.

It is a trap many businesses have fallen into since the first wave of AI, when early adopters were trying to prove they could access the cutting edge of the technology.

Everyone wanted to have the most powerful AI model, the most sought-after accelerators and the fastest route from the pilot stage to implementation.

But as AI moves from pilot into production, new costs are emerging, largely associated with usage.

A proof-of-concept used by a handful of people can look cheap, but running the same application thousands of times a day, or by agents calling upon multiple models for one task, is an entirely different proposition.

The mistake is not owning a “Ferrari”, but using the same model, depending on the same hardware choices or taking the same deployment path for every project .

That is where the next phase of enterprise AI is getting interesting, with new efficiencies coming from matching each workload to the most suitable model and accelerator, rather than defaulting to your biggest “engine”.

Every model has a different role to play There is a popular idea among some enterprises that AI is a ranking exercise, in which you should standardize as much as possible on the smartest model available.

In theory it sounds tidy, but it ignores the complex differences between each AI task and workload.

For instance, a customer service copilot needs to respond quickly enough that the user never notices the machinery behind it.

A large document summarization job can wait longer, with the user expecting the AI to “think” much more carefully.

Government applications of AI are likely to instead prioritize sovereignty and ownership, making where and how workloads run just as important as performance.