News

Enterprise AI Spending Surges, but Adoption Alone Does Not Explain the Bill

• By John K. Waters
• 11/04/2026

Enterprise AI spending is growing much faster than the number of businesses paying for it, according to new Menlo Ventures research that points to a shift from buying access for employees to financing work performed by agents.

The venture capital firm’s 2026 State of Enterprise AI report, released November 3, estimates deployment spending reached a $176.8 billion annualized run rate by September, up from $33 billion in 2025. The share of enterprises paying for AI rose from 55% to 65%.

That difference matters for buyers. A larger bill can reflect broader use inside an organization, but it does not independently establish that the additional work is useful or profitable.

A Spending Rate, not a Year-to-Date Total
Menlo’s methodology describes revenue figures as annualized run rates. The headline estimate therefore should not be read as $176.8 billion already spent during the first nine months of 2026.

The report also distinguishes unusually heavy spenders from the typical respondent. Average reported spending reached 4.7 times the prior year’s level, while the median reached 2.2 times. That gap suggests a smaller group of large spenders drove the average substantially.

The practical implication is that an industry growth figure may be a poor benchmark for an individual company’s budget. Buyers need to understand their own workload growth and the results it produces.

Agents Change What Drives Consumption
“Agents are changing the economics of software,” Menlo principal Derek Xiao said in the firm’s announcement.

Menlo reports that agents account for 80% of tokens on OpenRouter, a service providing access to multiple models. That measures activity on one platform, rather than all enterprise AI use. Token consumption also measures processing volume, rather than completed business outcomes.

An agent that makes repeated attempts can consume more resources without delivering proportionately more value. For procurement teams, the relevant comparison is consequently the cost of completing acceptable work, including the review it requires.

Menlo estimates that Anthropic, OpenAI, and Google capture 69% of enterprise AI deployment spending. That measure is distinct from their shares of enterprise language-model API spending, a narrower category. The figures should not be used interchangeably.

Demand Still Needs an Economic Test
The report draws on a Morning Consult survey of nearly 400 U.S. enterprise decision-makers responsible for more than $4 billion in AI spending. Market totals are modeled estimates informed by surveys, public filings, and third-party reports. Menlo discloses investments in Anthropic and OpenRouter.

A separate November 3 announcement from Information Services Group describes buyers putting more emphasis on the economics and reliability of production deployments. ISG says ongoing security and observability work remains necessary as organizations scale.

That provides a useful distinction between market demand and business value. Suppliers can generate growing revenue while customers still determine which deployments justify the cost.

For enterprise leaders, the new spending estimate strengthens the case for examining usage beyond license counts. It leaves the harder purchasing question with the customer: how much worthwhile work did the increased spending actually buy?