In September 2025, Anthropic published something most vendors would have kept to themselves. Its Economic Index report, built on a million Claude.ai conversations and a million API transcripts from August 2025, found that “77% of business uses involve automation usage patterns, compared to about 50% for Claude.ai users.” Same company, same models, same week. Through the API, roughly three quarters of the work was AI completing tasks with little human involvement. Through the chat interface, it was about half.
That gap is the most useful number in enterprise AI right now, and not for the reason it usually gets quoted. It says the split between AI that replaces work and AI that expands what people can do is not a property of the model. It is a consequence of how you deployed it. Two companies can buy the same license, point it at the same employees, and end up with entirely different mixes, which means the mix is a decision somebody in your organization is making, probably without knowing they are making it, and almost certainly without measuring the result.
The argument everyone is having is already over
The automation-versus-augmentation distinction is not new. Erik Brynjolfsson drew the line cleanly in 2022: “When AI augments human capabilities, enabling people to do things they never could before, then humans and machines are complements,” whereas “when AI replicates and automates existing human capabilities, machines become better substitutes for human labor.” He also named the trap, which is assuming that productivity gains mostly come from the substitution side. Most of history says otherwise.
Four years on, the strategic question has largely been settled in public. Analysts, consultancies and CEO surveys have converged on the view that the augmentation side is the bigger and more durable prize, and that treating AI purely as a headcount lever tends to disappoint. You do not need another post arguing that case, and this is not one. The interesting problem is what happens next, because an organization can agree completely that augmentation matters more and still be structurally incapable of proving any of it happened.
Only one of the two halves has a baseline
Automation ROI is a subtraction problem, and subtraction problems are comfortable. A task existed, it took a measurable amount of time and money, and now it takes less. You know the denominator because you were paying it last year. Every metric that gets presented confidently in a board meeting has this shape, including the ones we build: cost per pull request, cost per execution, cycle time on AI-assisted work, tickets deflected, cost per outcome. They all compare the present against a documented past.
Augmentation has no such comparator, by definition. If a team is now producing analysis that nobody in the company produced before, there is no prior cost to subtract from, no baseline year, no line item that used to carry it. The work did not get cheaper. The work started existing. Ask what the ROI was and the honest answer is that the question is malformed, which is a terrible thing to say to a CFO who has just approved the spend.
This is not a gap in enterprise discipline. It is a gap in the economics itself. The empirical literature on AI and work, from task-substitution models through the field experiments on knowledge work, measures augmentation as a delta on tasks that already existed. Faster, better, cheaper versions of work someone was already doing. None of it has a method for valuing output categories that only exist because the tool exists. The one paper that confronts the problem directly, Coyle and Poquiz’s Making AI Count for the NBER in October 2025, concludes that “current statistical frameworks would likely struggle to capture” generative AI’s full economic impact, and offers the right analogy for why. Input-output data from the industrial revolution would have shown rising coal tonnage and cotton volumes while revealing nothing at all about the transformation in how production worked. As they put it, “the input-output relationship will not necessarily show the transformation, but rather, eventually, its consequences.” You cannot see a new category from inside the measurement frame built for the old one.
| Automation | Augmentation | |
|---|---|---|
| What changes | Existing work costs less | New work becomes possible |
| Baseline | Last year’s cost per task | None exists |
| Natural metric | Cost per unit, time saved | Adoption depth, outcome KPIs |
| Who sponsors it | Finance, operations | Function leaders, Head of AI |
| Board reception | Credible, defensible | Treated as anecdote |
| Where it shows up | Agents, pipelines, coding tools | Chat, copilots, embedded AI |
What the asymmetry does to budgets
Money follows measurability, and you can watch it happen. BCG’s AI Radar 2026, a survey of nearly 2,400 executives including 640 CEOs, found corporate AI spending on track to roughly double from 0.8% to 1.7% of revenue, with leading organizations putting around 60% of their AI budgets into agentic development, and over 90% of organizations planning to maintain or increase investment regardless of near-term returns. The budget is concentrating on the half with a clean denominator, which is rational behaviour from people who have to defend a number.
The returns on that half are not arriving as promised. Bain’s 2026 survey of 951 global companies found that “nearly 40% of companies that measured AI cost savings landed below 10%, despite targeting 11% to 20%,” and that only 7% are running fully autonomous agents in production, with human approval remaining the dominant operating model at 38% of respondents. Bain also notes that 90% of those same companies are increasing budgets again anyway. So the automation thesis is underdelivering against its own targets, the augmentation thesis cannot be held to a target at all, and spending is going up in both cases. That is not a technology problem or a change-management problem. It is what happens when an enormous investment is governed by one metric that is measurable but disappointing and another that is promising but unmeasured.
Make the ratio a number you track
The practical fix is less exotic than the economics suggests. You will not get a dollar figure for work that never existed before, and chasing one wastes a quarter. What you can get, and what nobody is currently putting in front of a board, is the ratio itself: for each tool, team and vendor, what share of your AI usage is completing work on its own, and what share is a person working alongside it. Anthropic can compute that for its own products. Almost no enterprise can compute it for its own stack.
Tracked over time, that one ratio answers questions that currently get answered by instinct. Whether a function is drifting toward autonomy faster than its governance is ready for. Whether a team that looks expensive is expensive because it is experimenting at the frontier or because nobody has looked at its licenses in six months. Whether the agentic programme you funded at 60% of budget is actually running autonomously or is still routing every action through a human approver, as it is for most of Bain’s respondents. These are governance questions as much as finance questions, and they are unanswerable without a record of what your AI actually did.
That record is the product category Olakai occupies, and the two halves map onto it directly. Olakai Agentic instruments the automation side, measuring what agents and coding tools execute, what each execution costs, and how cycle time moves against the documented baseline. Olakai Assistive instruments the augmentation side across chatbots, copilots and more than 800 embedded AI tools, where the meaningful signals are behavioural rather than financial: who is using what, how deeply, and whether usage is concentrating in a few power users or spreading. Because both run on one vendor-neutral measurement layer rather than on each vendor’s own console, the ratio between them is something you can actually compute instead of estimate.
Measuring the half that has no baseline
Three instruments do most of the work on the augmentation side, and none of them pretends to be a cost comparison. Adoption cohorts sort people into power, casual, new and idle usage, which measures depth of engagement rather than output volume, and tells you where capability is genuinely compounding. AI fluency measurement looks at how people work with AI rather than how much the AI produced, which is the closest available proxy for whether anyone is handing the tools bigger goals. And Custom KPIs are where augmentation value actually gets booked, because they let a business define an outcome metric that has no historical equivalent and then hold AI usage against it. We covered that architecture in detail in the four-layer system behind Olakai’s metrics.
Being specific about what this does and does not deliver matters more here than usual. None of these three produce a defensible dollar return on new work, and any vendor claiming otherwise is selling you a number they invented. What they produce is evidence that the capability is real, concentrated, growing or stalling, plus a framework for attaching it to an outcome the business already cares about. That is a weaker claim than a cost-per-task saving and a far stronger one than a testimonial, which is exactly the right place for it to sit.
Where to start
Pick one function and compute its ratio for a single month. Not a dollar figure, just the split: what proportion of that team’s AI usage ran autonomously, and what proportion had a person in the work. Most organizations discover two things immediately. The automation share is higher than anyone expected, as Anthropic’s own API number suggests it would be, and the augmentation share has never been reported to anyone. Then ask who decided that mix. If nobody can answer, the mix was not a strategy, and you have found something more actionable than another ROI estimate. For finance leaders carrying the AI line on the P&L, our CFO view walks through how this reporting fits into an existing budget cycle, and AI metrics that matter covers which of these numbers survive contact with a board.
The organizations that come out of this period ahead will not be the ones that picked the correct side of the automation and augmentation debate. They will be the ones that could tell you, with a record behind it, which one they were actually doing. Build that measurement layer with Olakai, or keep approving a doubling AI budget against one number that disappoints and one that does not exist.
Talk to an expert about measuring both halves of your AI investment in one system of record.