Inside Accenture, one of the largest consulting firms in the world, the people burning through the most AI capacity are not the software engineers. They are everyone else. That is the observation from Justice Kwak, Accenture's agentic AI strategy lead, reported by 404 Media:

"We're seeing from some of the data internally at least that it's actually not our engineers that are driving the token consumption. It's a lot of the non-engineers that are doing some of those behaviors [...] you were talking about," Justice Kwak, Accenture's agentic AI strategy lead, said [...]

A "token" is the unit AI systems meter: roughly a chunk of a word, counted both when the model reads your input and when it writes its reply. Every prompt you send, every document the assistant digests, every retry and re-ask costs tokens. Most companies running AI assistants at scale pay for them by volume, so token consumption is effectively a meter running on every conversation.

The assumption in most organizations has been that engineers dominate that meter — they were the earliest adopters, they run coding assistants that chew through entire codebases, and they talk about context windows the way accountants talk about depreciation. Kwak's internal data says otherwise. The heaviest usage is coming from non-engineers: people in operations, sales, HR, consulting, finance, using assistants for drafting, summarizing, analyzing, and the sprawling multi-step workflows that fall under the label "agentic" — where an assistant does not just answer once but chains through a task, reading files, calling tools, and revising its own output, all of which multiplies the token count far beyond what a single chat message would suggest.

If you use an AI assistant for everyday work, this is about you. Your habits — uploading long documents, letting an agent loop over a task unattended, re-prompting when the first answer disappoints, keeping one endlessly long conversation instead of starting fresh — are probably a larger line item in your company's AI bill than anyone assumed, including you. That is not an accusation; it is just how metering works when usage is invisible. Most consumer-style AI interfaces show no token counter, no cost estimate, no indication that one approach to a task costs ten times another.

It is worth being precise about what this is and is not. This is not a product or a feature — there is nothing to adopt. It is a data point from inside one company, shared by an executive whose job is planning how Accenture deploys AI. It is plausible that it generalizes, given how widely assistants have spread beyond engineering teams, but a single firm's internal numbers are not an industry-wide measurement. Kwak also does not say which behaviors specifically are driving the consumption, how large the gap is, or what it costs in dollar terms. The framing "not our engineers" is a comparison, not a figure.

For managers, the practical implication is about visibility rather than restriction. If non-engineering usage is the bigger share of spend, then usage policies, training, and cost forecasting built only around developer workflows are aimed at the smaller part of the problem. For individual employees, the takeaway is simpler: there is a meter running, even though you cannot see it. Treating long-running agent tasks, giant file uploads, and repeated re-prompts as free is how a quiet workflow becomes a large bill.

The claim is current and shipping-adjacent in the sense that it describes behavior happening now, not a future plan. What remains unresolved is whether anyone — including Accenture — will act on it with better metering, guidance, or limits for the non-engineers doing the spending.