But there is a growing gap between AI ambition and AI readiness.

According to Gartner’s 2026 CMO Spend Survey, 70% of CMOs say becoming an AI leader is a critical goal for 2026, yet only 30% believe their organizations have the infrastructure needed to achieve that goal. Gartner also found that marketing teams allocate an average of 15.3% of their budgets to AI, while organizations better prepared to scale AI allocate closer to 21.3%.

The CMO’s AI paradox

The paradox is simple: AI is treated as urgent, but it is often funded like an experiment.

Many marketing teams are talking about AI transformation, but their actual investment remains fragmented across isolated tools, pilots and small productivity use cases. In practice, this means CMOs may buy AI-powered platforms, test generative content workflows or deploy chatbots, but without the data architecture, governance, process redesign and team training required to turn AI into a scalable growth engine.

This is why the issue is not only budget size. It is budget allocation.

Marketing leaders are under pressure to do more with limited resources. Gartner’s 2025 CMO Spend Survey found that marketing budgets had plateaued at 7.7% of company revenue, while many CMOs still felt their budgets were insufficient to deliver against expectations. In that context, AI is expected to create efficiency, but the investment needed to unlock that efficiency is often delayed.

Why spending is lagging

There are several reasons AI spending is not keeping pace with CMO ambition.

First, AI is still often seen as a software purchase rather than an operating model change. Buying tools is easier than redesigning workflows, cleaning data, training teams and integrating systems.

Second, many organizations are cautious because ROI is not always immediate. AI can reduce manual work, improve targeting and accelerate creative output, but those gains are difficult to measure if the company lacks proper attribution, analytics and performance dashboards.

Third, internal readiness is weak. Gartner reported that 70% of CMOs acknowledge their internal marketing processes are not mature enough to effectively implement and scale AI. This suggests that the bottleneck is not only financial; it is structural.

Finally, CMOs are still balancing AI investment against traditional priorities: media spend, brand building, customer acquisition, content, agencies, martech, CRM and analytics. AI competes for budget in a marketing environment where many teams are already stretched.

What this means for marketing teams

The next phase of AI in marketing will not be won by the companies that simply test the most tools. It will be won by the companies that connect AI to real business processes.

That means moving from isolated use cases to integrated systems:

AI-assisted content production connected to brand guidelines.
CRM automation connected to lead scoring and customer journeys.
AI chatbots connected to knowledge bases and support workflows.
Predictive analytics connected to campaign planning and budget allocation.
Personalization connected to real customer data, not generic automation.

For CMOs, the priority should be to shift AI from a side project to a structured capability.

The strategic lesson

AI is no longer just a marketing trend. It is becoming a competitive infrastructure layer.

But the current gap between priority and spending shows that many companies are still treating AI as optional, experimental or tactical. That creates risk. Competitors that invest earlier in data quality, workflow automation, AI governance and team enablement will be better positioned to scale faster, reduce operational friction and improve marketing performance.

The real question for CMOs is no longer: “Should we invest in AI?”

It is: “Are we investing enough in the foundations that make AI useful?”

Key takeaway

AI remains a top priority because CMOs understand its potential. Spending lags because many organizations are not yet ready to operationalize it. The winners will be the marketing teams that stop treating AI as a tool category and start treating it as a transformation layer across strategy, data, content, automation, customer experience and growth.