US Corporate AI Spending Hits New High, Growth Slows Amid Departmental Shifts

US corporate AI spending achieved a new high in October 2026, according to The Decoder, which reported a modest 1.65 percent increase month over month based on the Ramp AI Index. This growth, while reaching a peak, signals a deceleration in the rapid expansion seen in previous periods.

What happened

The Ramp AI Index, which tracks AI spending among business customers of financial services provider Ramp, indicated that US corporate AI spending saw a 1.65 percent month-over-month increase in October 2026. The Decoder, citing Ramp, notes that the heaviest AI users significantly outspent typical companies; the top one percent of companies by AI spending paid a median of $10,200 per employee per month, representing a nine percent increase. In contrast, a typical company spent just $16.32, a slight increase of less than a dollar from the prior month.

Ramp's data, drawn from over 85,000 US companies, reflects market trends but may overstate overall AI adoption due to its customer base skewing towards growth-oriented, tech-savvy, and larger businesses. Free AI tools and usage through personal accounts are likely excluded from these figures, according to The Decoder. Provider-specific growth showed OpenAI increasing 2.1 percent month over month, while Anthropic grew by 0.5 percent. Ramp chief economist Ara Kharazian highlighted that one percent of companies drive 80 percent of revenue for both OpenAI and Anthropic, primarily venture-backed firms in tech, finance, and technical services, with AI adoption barely spreading outside these sectors.

Simultaneously, AI prices are falling; the average price per million processed text tokens dropped 21 percent last month, as reported by The Decoder, citing Ramp. This indicates that usage volume is growing faster than spending, driven by competitive pressure and open-weight models. Anthropic reached 44.8 percent adoption, up 1.1 percentage points, while OpenAI held steady at 40.6 percent. Overall adoption of AI services rose to 56.8 percent.

Departmental spending saw shifts: software development teams, while still accounting for 72 percent of all tracked AI spending, showed a decline from summer highs. Customer support and customer success departments experienced the strongest growth, with their share of AI spending climbing from 0.8 percent in June to 13.3 percent in October, potentially driven by AI chatbots. Leadership teams also increased their spending slightly. However, Kharazian noted that customer support growth is not matching the rapid pace previously seen in development teams, and these use cases often favor cheaper, faster AI models.

Why it matters for RevOps leaders

For RevOps leaders, these trends underscore a critical inflection point in AI adoption and investment strategy. The deceleration in overall spending growth, coupled with the concentration of high spending among a small fraction of large, tech-savvy firms, suggests that smaller and mid-sized companies must approach AI integration with a clear focus on measurable revenue outcomes, rather than broad, undifferentiated investment. The significant growth in AI spending within customer support and customer success departments presents a direct opportunity for RevOps to enhance the entire revenue lifecycle, from improving customer experience and retention to streamlining service operations that impact profitability. Integrating AI tools in these areas can generate valuable data for attribution models and forecasting, provided the data pipelines are robust enough to capture and analyze these new inputs.

The falling prices of AI models and the preference for cheaper, faster options in customer-facing roles highlight an opportunity for cost optimization. RevOps leaders can leverage these more affordable models to achieve specific operational efficiencies without incurring the higher costs associated with flagship models, thereby improving the return on AI investment. This requires a systems-thinking approach to evaluating AI solutions, aligning technology choices with specific departmental needs and overall revenue objectives. The disparity in adoption outside tech and finance sectors also signals that companies in other industries have a chance to gain a competitive edge by strategically deploying AI to optimize their unique revenue processes.

What to do this week

1. Audit Current AI Utilization: Review existing AI tools across your organization, particularly in customer-facing and development teams, to identify current spending, usage patterns, and potential areas for efficiency gains or expanded application.
2. Pilot AI in Customer-Facing Teams: Explore and pilot AI-powered chatbots or tools within customer support and success departments to enhance response times, personalize interactions, and gather data for improved customer retention and upsell opportunities.
3. Evaluate Cost-Effective AI Models: Investigate the feasibility of deploying cheaper, faster AI models for specific tasks within your revenue operations, ensuring they meet performance requirements while optimizing technology expenditure and improving ROI.

Sources

• US corporate AI spending hits a new high in October, but growth slows to 1.65 percent — The Decoder