AI adoption measured by payments far exceeds survey estimates

Transaction-based measurement is reshaping what we know about AI adoption in business. Ramp’s July 2026 AI Index found that 55.7 per cent of American firms recorded paid spending on AI models, subscriptions, tokens or APIs, according to Ramp’s Economics Lab analysis of corporate payment data (https://ramp.com/data/ai-index). That figure is more than double the 21.6 per cent adoption rate estimated by the US Census Bureau through conventional surveys. Lead economist Ara Kharazian presented the index as a monthly measure drawn from card, invoice and ACH transaction data across more than 70,000 firms on Ramp’s platform.

The gap between these two numbers is not a rounding error. Survey-based estimates depend on self-reported understanding of what counts as AI use. That understanding can lag behind reality, particularly when adoption is rising quickly or when employees use tools without central procurement. Ramp’s approach takes a different tack: any positive payment to an AI product or service, identified through merchant names and receipt line items, counts as adoption. The method builds on earlier survey research by Bonney and colleagues in 2024, but the index claims faster and more complete coverage precisely because it does not depend on whether respondents recognise or recall their AI spending.

The methodology has notable blind spots. Free tools and employees using personal accounts leave no corporate payment trail. The true adoption rate could be higher still. Ramp’s figures capture only firms on its platform and only those whose payment data is granular enough to classify. These are not minor caveats: they define the boundary between what can be measured and what must be inferred.

For policy and institutional decision-making, the difference matters. A university governing body relying on Census-style survey data might conclude that AI use is a minority practice among its staff and students, shaping procurement timelines, training budgets and academic integrity policies accordingly. Transaction data suggesting majority adoption points toward a different set of obligations: consistent tool evaluation, transparent usage policies and evidence-based assessment design rather than reactive measures taken after widespread use is already a fact.

The index also introduces metrics such as spend per employee and model market share among firms using Ramp’s Token Spend Management product, providing a more granular picture of where spending concentrates. For education, these distribution patterns could matter as much as the headline adoption figure. If a small number of tools dominate institutional spending, procurement decisions carry outsized consequences for academic work.

What remains unclear is whether transaction data captures the full picture of AI use in practice, or only the paid and centrally tracked portion. The link between measured corporate spend and actual AI integration into workflows, assessment and knowledge production is not straightforward. The next useful step would be a direct comparison of transaction-based and survey-based adoption measures across the same firms and time periods, testing whether the divergence is methodological or reflects genuinely different populations being counted.

A 21.6 per cent Census figure and a 55.7 per cent transaction figure can both be accurate within their respective frames. The problem is that institutions making decisions about AI governance, academic integrity and workforce planning need to know which frame applies to their context, and what each leaves out.

The Neuron’s report on 😺 New Google, Meta, and (maybe) OpenAI models provides the source reporting for this article.

← Back to Blog