Menlo Ventures just released its fourth annual State of Enterprise AI report. Enterprises will spend $176.8 billion deploying AI in 2026, up 5.4x from $33 billion in 2025. AI deployment spend now equals about 30% of public cloud revenue, up from about 6% a year ago.
For B2B founders, the more useful number is where that money comes from.
Menlo surveyed roughly 400 U.S. enterprise decision-makers who together control more than $4 billion in AI spend, and asked which budget their incremental AI dollars came from. 55% was net-new budget. 22% came from software budgets. The rest came from labor and other lines.
Of the 22% from software budgets, 13 points went to incumbents converting their own seat licenses into AI products. AI-native vendors got 9 points, or $12.6 billion.
Five takeaways for anyone building, selling, or investing in B2B + AI:
- 55% of AI spend is new budget. Products that have to win a software budget are competing for the smallest pool.
- Incumbents kept 13 of the 22 points that moved out of software budgets.
- AI cut SaaS growth by about 2 points, from 13% to 11% on a market of more than $600 billion.
- The average enterprise spent about 2.5x its AI plan. The median spent about 1.2x. A small group of power users drives the totals.
- Incumbents held or gained share in nearly half the categories Menlo tracks. ServiceNow has 91% of AI spend in IT, and Workday has 68% in HR.
#1. $12.6 Billion Moved From Software Budgets to AI-Native Vendors. The Labor and Net-New Pools Are Several Times Bigger.
Here’s how incremental AI spend breaks down by source:
- 55% net-new budget
- 22% from software budgets
- 13 points to incumbents converting existing SaaS licenses to AI
- 9 points ($12.6 billion) to AI-native vendors
- About 23% from labor and other budgets
The matchup most founders picture is an AI-native startup and a legacy seat vendor fighting over the same software line. That’s the smallest of the three pools, and incumbents are winning most of it.
I’ve used a related rule in Fund II this year. I won’t invest in an agentic B2B company unless it unlocks budget beyond software, and the evidence I look for is deal sizes at least 2x what the pre-AI competitors get. A product priced like the seat product it replaces is competing for the 22%. A product priced at 2x or more that customers still buy is being paid for out of the other 78%.
We’ve seen the same thing at SaaStr. Our agent spend came out of headcount we didn’t backfill. We run with 3 humans and 20+ agents now, and revenue is higher than it was with a much larger team. Any vendor that wanted that budget had to take over work people were doing.
#2. AI Took About 2 Points Off SaaS Growth: 13% Down to 11%
Menlo estimates AI cut application SaaS growth from about 13% to about 11% on a market of more than $600 billion. That’s roughly $12 billion of growth that didn’t happen. For an individual seat-priced company, it can show up as a flat year.
It matches the public company results we’ve covered all year. Total software spend is growing faster than the median SaaS company, and the categories priced per human seat (CRM, sales, marketing, collaboration) are the ones that slowed.
Menlo found little evidence that enterprises are replacing their systems of record (CRM, HRIS, ERP) with AI. Those systems are staying. What’s shifting is how much new budget goes to them and how much goes to tools built around them.
#3. ServiceNow Has 91% of AI Spend in IT. Workday Has 68% in HR.
Menlo tracked AI app spend by department. Incumbents gained ground in nearly half the categories:
Where the incumbent owns the workflow, the data, and the permissions, the incumbent captures the AI spend. ServiceNow runs the ticket queue. Workday holds the employee record. For both, AI is an upsell to a base they already control.
Customer success went to AI-natives. Sierra and Decagon lead the fastest-growing department at 4.3x, because AI can handle a support conversation from start to finish and the answer can be checked.
Menlo says the fastest-growing functions share three conditions:
- Agents can take action in connected systems
- The context they need is available
- Outcomes can be verified
Support meets all three. HR and IT do too, but the incumbent owns the systems and the context. If you’re building in one of those categories, your plan for getting that data belongs in the first pitch.
#4. Sales Is a $1.4 Billion AI Category. Coding Is $34 Billion.
Coding is $34 billion, up 7.7x. Workplace assistants and chatbots are $25 billion. Together they make up 76% of AI application spend. Every other horizontal category combined comes to $8.8 billion. Vertical AI is $10.1 billion, led by healthcare at $3.7 billion and legal at $1.8 billion.
Sales AI at $1.4 billion is about 4% of what enterprises spend on AI for coding. We’ve written for months that agents have taken over SDR work, much of first-line support, and a lot of CSM work, while there’s still no good AI account executive. The data fits. Sales AI is growing 3.6x a year from a small base, and the leaders are prospecting and data tools like Clay.
The coding number also shows how much the labs are taking. Claude Code and Codex account for $24 billion, or 70% of coding spend, up from about $1 billion a year ago. Independent coding apps (Cursor, Cognition, Factory, Lovable, and others) total $10.1 billion, up 2.9x. A product that’s a thin layer on a frontier model now competes directly with the model’s maker.
#5. The Average Enterprise Spent 4.7x Last Year’s AI Budget. The Median Spent 2.2x.
Enterprise AI adoption went from 55% to 65%, and spend per enterprise nearly quintupled. The average and the median tell different stories:
- Average: spend reached 4.7x the prior year, against a planned 1.9x
- Median: spend reached 2.2x, against a planned 1.8x
Menlo attributes the gap to agents that run in parallel and keep working after the user logs off. Spend tracks the amount of work done, so a company running 20 agents overnight spends at a different scale than one whose employees open a chatbot a few times a day.
Two implications for founders:
- Most of your pipeline looks like the median. The typical enterprise buyer is spending about 2x last year and is still early.
- Per-seat pricing caps what you can earn from power users. These are the customers whose spend ran far past plan, and a seat price limits their bill to their employee count. Consumption, outcome, and resolution pricing scale with their usage.
#6. The Labs Took 69% of AI Deployment Dollars
Anthropic, OpenAI, and Google captured $122.7 billion, or 69% of deployment spend, up from 50%. Their spend grew 7.5x, versus 3.3x for everyone else. AI-native companies overall hold 77% of application spend (up from 63%) and 90% of infrastructure spend. Excluding the labs, startups still hold 61% of app spend.
This fits the departmental data. AI-natives lead in categories that didn’t exist before or that AI can handle end to end: coding, assistants, support, legal. Incumbents lead where AI is added to a workflow they already own: IT tickets, HR records, data platforms. Which kind of category you’re in should shape how you sell.
A disclosure for reading the report: Menlo is an investor in many of the companies it covers, including Anthropic, OpenRouter, Lovable, Factory, Fireworks, Modal, and OpenEvidence. The budget-source, overspend, and departmental figures come from Menlo’s survey of 400 enterprise decision-makers. The agent-versus-human token figures come from OpenRouter, whose users skew toward developers and startups.
