Enterprise AI Spending Is Accelerating Faster Than Expected

Keywords: enterprise AI, AI spending, CIO survey, production deployment, token costs, software budgets, SaaS, generative AI, RBC research, AI adoption

Introduction

Enterprise adoption of artificial intelligence appears to be moving from experimentation to execution much faster than many investors had anticipated. New research from RBC Capital Markets suggests that large companies are not only investing heavily in AI, but are also preparing to spend even more in the coming quarters. This shift matters because it challenges several widely held assumptions about the pace, cost, and broader impact of AI adoption across the software and technology landscape.

The findings are particularly notable because they come from Rishi Jaluria, a technology analyst known for his cautious stance on AI enthusiasm. When a typically conservative voice reports that corporate AI spending momentum is strengthening, the signal deserves attention. The implications extend beyond model usage and into budget allocation, software procurement, and the future of enterprise technology stacks.

AI Is Moving Into Production

One of the clearest takeaways from the RBC survey is that enterprise AI is no longer confined to pilot programs. More than half of respondents said AI is already in production, while another 35% expect to reach production within six months. That is a major departure from the sentiment reflected in RBC’s survey at the end of last year, when many executives viewed AI as a longer-term experimental initiative.

This change suggests that businesses have crossed an important threshold. The initial phase of AI adoption was largely defined by proof-of-concept work, internal testing, and limited deployment. Now, companies are increasingly using AI in real business workflows, which typically means greater spending, higher integration demands, and stronger pressure to demonstrate measurable returns.

For investors, this transition is significant. Production deployment is usually where software becomes mission-critical, budgets become recurring, and usage scales rapidly. In other words, once AI moves into production, the conversation shifts from “Should we try it?” to “How much do we need to support it?”

Costs Are Rising, but Not Slowing Adoption

A common concern in the market has been that AI token costs would become the biggest barrier to enterprise adoption. The survey findings challenge that assumption. Nearly 90% of respondents said token budgets are manageable, even though nearly half have already exceeded their original spending plans.

This is an important nuance. Yes, AI spending is rising faster than expected, but companies do not appear to be treating cost overruns as a reason to stop. Instead, many are planning to allocate even more money to AI tokens in the future. That indicates that executives are still seeing enough business value to justify the expense.

This behavior also suggests that AI costs may be following a familiar enterprise technology pattern: early-stage adoption is often expensive, but once companies identify meaningful productivity or revenue gains, they become willing to absorb the cost. In that sense, the current spending trend may reflect not inefficiency, but conviction.

New Budgeting Patterns Are Emerging

Another striking result from the survey is the shift in how companies are budgeting for AI. A hybrid pricing model combining per-seat subscriptions and usage-based pricing has quickly become the preferred purchasing structure. At the same time, 100% of respondents said they are allocating budget specifically to AI and large language model initiatives.

Even more telling, 91% said they are creating entirely new AI budgets rather than simply reallocating existing spending. This is a powerful indicator of acceleration. It means AI is no longer being treated as a side project funded from other software lines; it is becoming a distinct budget category with strategic priority.

That distinction matters for the broader software industry. When companies create dedicated AI budgets, they are signaling that AI is not just another feature embedded in legacy products. It is becoming a separate investment area with its own procurement logic, management oversight, and expected outcomes.

The SaaS Disruption Story Looks Less Certain

For months, investors have worried that AI adoption would cannibalize traditional software spending and trigger a so-called “SaaS doom” scenario. The survey results offer a more restrained picture. Almost all respondents expect software spending to increase, and none expect it to decline. Even companies spending more on AI are generally not funding it by slashing other software expenses.

This suggests that AI may be expanding total technology budgets rather than merely redistributing them. If that proves true, the competitive pressure on software vendors may be serious, but not necessarily destructive in the way some bearish narratives have predicted.

The likely outcome is more complex. Some software products may become less valuable if AI automates parts of their functionality, while others may benefit from deeper integration with AI tools. In this environment, vendors that can embed AI into their products without losing pricing power will likely be best positioned.

Market Leaders and Model Preferences

The survey also provides a glimpse into current model preferences. ChatGPT was identified by 57% of respondents as the most commonly used AI-based service, while only 12% said Anthropic’s Claude was their primary choice. This does not necessarily settle the long-term competitive landscape, but it does show that enterprise familiarity and market momentum still matter.

Brand recognition, ease of deployment, and ecosystem maturity all influence adoption. In enterprise technology, the best model is not always the most technically advanced one; it is often the one that can be deployed fastest, integrated more easily, and supported reliably at scale.

What Investors Should Watch

Although the survey is encouraging for AI bulls, it has limitations. The respondent base is concentrated in technology companies and large enterprises with multibillion-dollar annual budgets. Smaller firms and non-tech industries are less represented. As a result, the findings may not fully reflect the pace of adoption across the broader economy.

Still, large enterprises often act as the leading edge of technological change. Their procurement decisions can shape vendor roadmaps, influence pricing models, and create adoption patterns that smaller companies later follow. If AI is entering production faster among the biggest buyers, it is reasonable to expect broader diffusion over time.

Conclusion

RBC’s latest research suggests that enterprise AI adoption is accelerating, budgets are expanding, and concerns about cost may be less restrictive than investors assumed. The move from pilot to production, the creation of dedicated AI budgets, and the willingness to absorb rising token costs all point to a more durable wave of corporate AI investment.

For the market, the message is clear: AI is not just a future promise. For many large enterprises, it is already becoming an operating expense, a strategic priority, and a core part of technology planning. The pace of adoption may be forcing investors to rethink not only how fast AI will grow, but also how deeply it will reshape enterprise software, spending behavior, and competitive dynamics across the sector.