West Monroe Survey: AI Spending Surges as 74% of Enterprises Commit Over 10% of IT Budgets to AI

The share of surveyed buyers allocating more than 10% of IT budgets to AI rose to 74% in 2026 from 23% in 2025, according to West Monroe’s survey of 300 U.S. executives. Buyers more often said they primarily buy rather than build AI solutions (42%) than sellers expected (22%); buyers also favored enterprise subscriptions (33%) and usage-based pricing (24%), while 36% of sellers believed buyers preferred hybrid pricing that includes outcome-based pricing. The findings point to differences in buyer and seller views on AI investment, product priorities and pricing, rather than a reported change in company financial results.

Analysis

The investable signal is a potential mismatch between software vendors’ product/pricing roadmaps and how enterprise customers actually procure—not proof of durable AI revenue growth. If buyers favor predictable subscriptions while vendors push outcome-linked or hybrid contracts, near-term sales friction and discounting could rise; outcome pricing also transfers usage, performance, and attribution risk to vendors. Separately, agent-first product investment may outrun demand for foundational infrastructure, model access, integration, and talent, creating roadmap and capital-allocation risk for vendors that chase agents before buyers demonstrate willingness to pay.

Second order: successful assistants could reduce employee-seat counts or compress spend on overlapping point solutions, even as total AI budgets rise. This makes seat-based SaaS exposed where vendors cannot price value beyond seats; platforms with proprietary workflow context, trusted data, controls, and measurable outcomes have a better chance to defend economics. Infrastructure and model providers may see demand, but spend can accrue to cloud/model suppliers rather than software vendors, and rising consumption costs could pressure application margins.

Treat the survey as directional: 300 U.S. executives, self-reported preferences, and no evidence that budget allocation converts into realized spend, renewals, or returns. Days: limited standalone catalyst. Over 1–3 months, watch earnings commentary on AI attach, renewal concessions, consumption costs, and pricing changes. Over 6–18 months, adoption and seat displacement determine winners. Falsify the pricing-risk thesis if vendors retain renewal rates and margins while monetizing AI without broad discounting; falsify the AI-spend opportunity if disclosed deployments fail to translate into paid usage.

Key Decisions for Investors

• No event-driven trade on this release alone. Use it as a diligence screen across enterprise software: prioritize renewal commentary, net retention, seat counts, AI paid attach, and gross-margin impact over announced AI features.
• Watch for a relative-value setup: favor software platforms able to monetize workflow outcomes and proprietary data/control layers versus undifferentiated, seat-priced point solutions. Do not initiate solely from the survey; require evidence in company disclosures that AI is paid, retained, and margin-accretive.
• Treat cloud infrastructure and model-consumption exposure as a possible beneficiary, not a confirmed winner: verify incremental paid workloads and unit economics, since higher AI budgets may be offset by lower legacy software spend or vendor subsidies.
• At the next earnings cycle, flag vendors introducing outcome-based pricing without clear attribution or predictable customer bills. Reassess if renewal discounting, churn, or gross-margin deterioration appears; the thesis weakens if AI pricing sustains retention and margins.