EY has built what it calls an “AI router,” a system that steers each task to the cheapest model that can handle it, in an effort to keep its own soaring AI bills under control.
The tool, reported by Business Insider, is the Big Four firm’s answer to a problem now spreading across corporate IT: the cost of the tokens that AI consumes.The logic is simple arbitrage.
Not every request needs the most powerful, most expensive model, so a router sends easy work to a cheap one and reserves the pricey models for the hard problems, trimming the bill without obviously trimming the output.
EY has reason to watch the meter. The firm invests more than $1 billion a year in AI, runs a fleet of some 1,000 AI agents, and has seen its AI-related consulting revenue jump around 30%, a scale at which token costs stop being a rounding error, in a market where the most AI-obsessed firms spend thousands per employee a month.
Its own research shows the anxiety is widespread. In EY’s latest AI Pulse survey of 534 senior US business leaders, 82% said they were concerned about token-usage costs, and 98% of those using token-based tools said the costs had made them reconsider their strategy.
Yet most companies are flying blind. Only 64% of the firms surveyed said they actively monitor token usage with budgetary guardrails, which means a third are spending on AI without a clear meter, a recipe for the bill shocks that have hit the sector.
The mood has shifted from more to enough. EY’s global AI consulting leader, Dan Diasio, put it plainly: “‘AI saves time’ is no longer sufficient when costs mount and remain unclear,” a line that captures the turn from adoption at any price to value at a known one.
The token economics are genuinely strange. The price per token has collapsed as models get cheaper, yet enterprise AI bills have tripled, because agentic tools that run many steps consume far more tokens than a single chatbot prompt ever did.
That is the paradox a router is built for. If each task can be matched to the least costly model that still does the job, a company can keep using AI aggressively while stopping the total from ballooning, which is exactly what EY is trying to prove at its own scale.
It is not alone in the effort. The industry spent two years urging staff to use as much AI as possible, a fashion nicknamed tokenmaxxing, and is now swinging the other way, with firms from Atlassian to Amazon imposing budgets and controls.
For a consultancy, though, the router is also a product. EY sells AI advice to other companies, so a tool that visibly tames its own costs doubles as a demonstration, evidence that the firm can do for clients what it has done for itself.
The survey points the same way. Some 76% of leaders told EY that off-the-shelf software no longer meets their needs, and 91% now see building AI tools in-house as critical, a shift that favours firms selling the expertise to build them.
The catch is that in-house building is hard. Nearly three-quarters of the leaders EY surveyed said their own AI development was slowing progress, and a third flagged shadow-IT and governance risks, the messy reality behind the clean promise of a router.
What the story really marks is a change of question. The first phase of the AI boom asked whether a tool worked; the second, which EY’s router belongs to, asks what it costs, and whether the value justifies the meter.
EY’s answer, for now, is to build the meter itself. A firm that spends a billion dollars a year on AI has decided that the way to keep spending is to watch every token, which is less a retreat from AI than a sign of how much of it is now in use.
