RBC survey overturns 2026 AI mainstream narrative: enterprise AI spending accelerates across the board

Event Background

In 2026, there is no shortage of rhetoric about AI bubbles, cost overruns, and the disruption of SaaS. However, the latest semi-annual CIO survey released by RBC Capital Markets paints a completely different picture. The survey covers over 100 chief information officers and technology leaders, representing hundreds of billions of dollars in enterprise IT budgets. Analyst Rishi Jaluria had previously been cautious about enterprise AI adoption, but this survey has changed his view: “The momentum of enterprise spending is broadly positive, and AI adoption is shifting from pilot to production.”

Digital Economy Analysis

The survey results point directly to several core variables in the current digital economy:

• User growth and traffic changes: Enterprise AI usage has jumped from experimental pilots to production-level deployment, meaning that AI’s penetration into business processes is accelerating. More than half of respondents said AI is already in production, and another 35% plan to achieve this within six months, which will significantly boost the user base and call volume of AI services.

• Data value: Token budgets are manageable and planned to increase, indicating that enterprises have greater recognition of the value of AI outputs. Nearly 90% of respondents believe token spending is manageable, and even though nearly half have already overspent, they are still preparing to invest further. This counterintuitive phenomenon shows that AI investment returns are improving, and enterprises are deepening their reliance on data-driven decision-making.

• Platform expansion: OpenAI dominates competitors with a 57% usage rate (Anthropic only 12%), and also leads in performance perception by 44% to 24%. This reinforces the “winner-takes-all” effect in the AI platform economy—first movers lock in enterprise clients through brand, ease of use, and ecosystem.

Business Model Observations

The survey reveals several important business model signals:

• Profit model transformation: Hybrid pricing (seat licenses + usage-based pricing) is rapidly becoming the enterprise preference. This model combines fixed and variable revenue, ensuring a baseline income while capturing incremental AI usage. For AI vendors, this means more stable cash flow and higher customer lifetime value.

• Platform model advantages: OpenAI has built strong network effects through its API and ChatGPT Enterprise. The more enterprise users it has, the faster model optimization becomes, which in turn attracts more customers. This virtuous cycle is key to OpenAI maintaining its lead.

• SaaS is not dead: The “SaaSpocalypse” narrative has fallen flat—the vast majority of enterprises plan to increase software spending, with none reducing it. AI is not eating into software budgets but is creating new IT spending. Enterprises are paying for AI by adding AI budgets (91% of respondents) rather than cutting existing software.

Market Competition Analysis- OpenAI vs Anthropic: OpenAI holds a significant lead in both adoption rate and perceived performance. But can Anthropic close the gap before its IPO? The survey shows that Claude still has room to catch up in the enterprise market, but OpenAI's first-mover advantage and brand recognition have built a strong moat.

• Cloud Platform Competition: The surge in AI workloads will drive demand for cloud computing. Microsoft Azure (deeply integrated with OpenAI), Amazon AWS, and Google Cloud will all benefit. However, falling AI infrastructure costs (token prices are expected to plummet) may weaken cloud providers' profit margins, forcing them to pivot toward higher-value AI services.

• Traditional Software Vendors: SAP, Oracle, Salesforce, etc., need to accelerate AI integration. The survey shows enterprise software spending remains strong, but if they fail to effectively embed AI capabilities, they risk being eroded by emerging AI-native platforms.

Data & Regulatory Impact

• Data Governance: Large-scale enterprise AI deployment means more core business data will flow into models. Data privacy, compliance, and security become key issues. Regulations such as GDPR and the AI Act will impose higher requirements on how companies manage AI training data.

• Antitrust: OpenAI's leading position may attract regulatory scrutiny. If its market share continues to expand, the EU Competition Commission or the FTC may investigate its exclusive practices in the AI market.

• Cross-border Data Flows: The globalization of enterprise AI applications requires data to flow across regions. Digital sovereignty policies (such as the EU Data Act and China's Data Security Law) may increase compliance costs for AI deployment, driving demand for "sovereign AI" cloud services.

Global Trend Observations

This survey confirms that the AI economy has moved from incubation into a growth phase. The following long-term trends are worth noting:

• AI Economy: Enterprise AI investment is no longer experimental spending but a core strategic budget. 100% of respondents allocate budgets for AI, and 91% have set up new budgets, indicating that AI has become the third largest IT spending pillar alongside cloud and cybersecurity.

• Platform Economy: Enterprise adoption of AI platforms is far faster than previous technology cycles. The rapid prevalence of hybrid pricing (becoming the preferred model in a short time) reflects how the software business model is being reshaped by AI.

• Data Economy: The growth in token consumption means data flow is accelerating. Enterprises need more efficient data pipelines and governance tools, which will give rise to a new data middleware market.

• Digital Sovereignty: As AI deployment becomes cross-regional, countries may strengthen controls over AI data flows. Enterprises need to plan for localized AI infrastructure in advance.The most economically significant finding of the RBC survey is that enterprise AI spending is not a zero-sum game but incremental investment. 91% of companies have created new AI budgets rather than repurposing existing IT funds. This means AI is generating entirely new increments in the digital economy, rather than merely substituting.

For enterprises, the business model must shift from "whether to adopt AI" to "how to embed AI into core value creation." The rapid rise of hybrid pricing indicates that traditional software subscriptions are being replaced by more flexible pricing models tied to usage value. This has profound implications for SaaS companies' revenue models and customer relationship management.

Regarding the future digital economy landscape, we assess: competition in the AI platform economy will accelerate. OpenAI currently leads, but technology iteration and market changes (such as the sharp drop in token costs) may reshape the landscape. Enterprises must find a balance between the risk of vendor lock-in and embracing AI productivity gains. Data governance and regulation will become the main challenges for deep AI deployment. Companies that can build compliant, explainable AI architectures will prevail in the long run.

DigitalEcoNews Insight: The AI investment cycle has moved from "when to start" to "how to scale," which will be the core topic of corporate digital strategy from 2026 to 2028.