Artificial intelligence is no longer an experiment. Today, it’s a basic expectation within any organization.

However, in marketing there is a clear contradiction: CMOs want to lead AI transformation, but very few are truly prepared to do it.

The problem isn’t the intention.
The problem is the foundation on which they’re trying to build.

According to data cited by Gartner, CMOs say that artificial intelligence will be key to accelerating efficiency and growth. Yet at the same time, only a minority are seriously investing in developing the analytical capabilities needed to sustain that transformation.

The strategic ambition exists.
The analytical infrastructure, in most cases, does not.

And that’s where the real gap emerges.

AI doesn’t fail, the context in which it’s implemented does.

Over the past five years, the volume of data available to marketing teams has grown by 230%.

Paradoxically, 56% of marketing professionals say they don’t have enough time to analyze it in depth. At the same time, 57% are already using artificial intelligence to perform advanced analytics.

This reveals an uncomfortable reality:
many teams are trying to solve chaos with automation.

But automating a disorganized system doesn’t improve it.
It simply accelerates the disorder.

Implementing artificial intelligence on top of:

• fragmented data
• silos between teams
• inconsistent metrics
• simplified attribution models

…only amplifies the problem.

Real transformation starts before AI.

It starts with the data architecture.

The Right Order: Analytics Transformation First, Artificial Intelligence Second

Many organizations try to start at the end of the process, focusing on:

• generative assistants
• automated dashboards
• predictive models
• marketing copilots

But when the analytical foundation isn’t consolidated, these initiatives remain isolated and fail to influence strategic decision-making.

Experience from organizations that have successfully scaled artificial intelligence shows a very clear order:

1. Data centralization and normalization
2. Automation of repetitive analytical processes
3. Implementation of advanced models (Marketing Mix Modeling, quantitative audits)
4. Integration of artificial intelligence on top of that structured foundation

When this sequence is respected, results begin to appear.

Some organizations that implemented advanced optimization models achieved:

The difference isn’t created by the tool.

It’s created by the analytical framework that supports it.

The Real Gap: Analytical Skills, Not Technical Skills

The debate about whether CMOs know how to use artificial intelligence often focuses on tools, prompts, or platforms. But that’s not the critical skill.

The real gap is analytical and strategic.

Because leading marketing in an AI-driven environment requires the ability to formulate the right questions from data, distinguish between attribution and causality, interpret marginal ROI by properly evaluating true incrementality, and understand which model to apply in each context.

Today, for example, only 8% of brands systematically use incrementality testing in their marketing measurement.

That means most decisions are still based on simplified attribution models like last-click, even though the digital ecosystem is infinitely more complex.

Artificial intelligence can analyze millions of data points in seconds.

But if the question is wrong, the answer will be too.

From Hype to Competitive Advantage

Artificial intelligence is not a competitive advantage by itself, it is a multiplier.

It amplifies what already exists within an organization.

In companies where:

• data is centralized
• analytical processes are automated
• marketing science models are embedded in operations

AI accelerates decisions, detects inefficiencies, and optimizes budget with greater precision. In companies where information is still fragmented, AI simply produces faster dashboards. But not better decisions.

That’s why the relevant question is not whether CMOs want to use artificial intelligence.

The real question is:

Are they leading a true analytical transformation that allows them to use it well?

Data Literacy as a Basic Requirement

85% of leaders believe that data literacy will become as essential as knowing how to use a computer.

Everything suggests it will be one of the most in-demand skills by 2030.

This fundamentally redefines the role of the CMO.

It is no longer enough to master:

• creativity
• positioning
• brand growth

Today, it also means designing systems where data, models, and decisions coexist in an integrated way.

When that happens, artificial intelligence stops being an experimental tool.

And becomes strategic infrastructure.

The Leadership Ahead

The CMOs who will lead the next decade likely won’t be the ones adopting the most tools.

They will be the ones who build a system where:

• data is structured and accessible
• econometric models are part of everyday decision-making
• automation eliminates repetitive analytical tasks
• artificial intelligence relies on reliable information
• decisions are based on real incrementality, not superficial metrics

AI-driven transformation is not a passing trend.

It is a structural evolution of marketing.

Schedule a meeting with our team of experts and learn how to organize your data infrastructure to truly unlock the value of AI. 🚀