The AI Marketing Canvas: How CMOs Can Prioritize Projects

Until recently, most marketing teams only experimented with artificial intelligence in small test projects. AI wasn’t a fixed part of their budgets. Now it has become a regular expense, and in some companies, it already accounts for a large share of marketing spend.

According to Influencer Marketing Hub, nearly 20 percent of marketers now assign more than 40 percent of their budget to AI-driven campaigns. The question is no longer “Should we use AI?” but rather “Which AI projects deserve investment?”

Chief marketing officers now face pressure to show how these investments deliver results. University of Virginia Darden School of Business professor Rajkumar Venkatesan, who advises Fortune 500 companies on AI adoption in marketing, has developed a framework to help cut through the noise.

“AI can make marketing faster and cheaper, but the bigger prize is growth,” said Venkatesan. “If all you do is automate what you already do, you’re leaving transformational opportunities on the table.”

This is where his “Four Quadrants” framework comes in, offering marketers a way to categorize AI projects by the type of value they create.

Four Quadrants

His first step is to distinguish between four categories of AI use cases:

Internal productivity. Automating back-office tasks such as reporting or drafting creative briefs. “These don’t create new value,” Venkatesan noted, “but they free people up to focus on higher-impact work.”

Internal Growth. Using AI to analyse customer data and spot patterns you’d otherwise miss— for example, flagging which customers are likely to leave, or showing which marketing channels bring in the most sales. Here, AI informs the decisions that directly affect revenue.

External Productivity. Deploying AI tools like chatbots to answer routine questions or recommendation engines to suggest products. The customer gets quicker, more consistent help, but the way the business makes money stays the same.

External Growth. Instead of just saving time or cutting costs, companies offer something completely new to customers. Venkatesan pointed to Ikea’s AI-powered interior design-assistant, which gives every shopper free, personalized advice. “That’s turning what was once a luxury service into something mass-market,” he said. 

Quadrants in Action 

Defining the quadrants is only the first step. To see their practical value, Venkatesan maps them onto common marketing activities.

Competitive analysis: In competitive analysis, AI can scan reports, social media and market data much faster than people, which can boost internal productivity. It can also look at patterns and predict what a competitor might do next — that’s internal growth.

Segmentation: In segmentation, AI can automatically update customer lists, keeping the data current without manual work — another internal productivity gain. It can also highlight which customers are most likely to buy, helping marketers target them more effectively. And when those insights are used to deliver personalized offers to each shopper in real time, that becomes external growth.

Brand communication: Meanwhile in brand communication, AI tools can quickly suggest slogans or headlines, which saves time — that’s internal productivity. Other tools can analyse customer reactions and show which words or images work best, driving internal growth. Some systems can even track emotions in real time and adjust a campaign on the fly — that’s external growth.

By tracing these links, Venkatesan said, marketers avoid the trap of chasing shiny tools that offer little real impact. “The discipline comes from seeing clearly what kind of value a project is creating,” he explained.

The AI Marketing Canvas

The quadrants show where an AI project fits. To go further, Venkatesan has created another tool: the AI Marketing Canvas. This is a step-by-step map of how organizations usually adopt AI in marketing. The canvas shows five stages.

- At the foundation stage, a company puts its data in order and makes sure the basics of digital marketing are in place.

- Experimentation comes next, with small pilot projects to see what AI can do.

- In the expansion stage, those pilots that work are rolled out more widely across channels and teams.

- Transformation is when AI is no longer just a tool but changes how the business works — like offering every customer a personalized service at scale.

- The final stage is monetization, when firms turn their AI capabilities themselves into revenue streams, such as selling AI-driven services to others.

Venkatesan likened the canvas to a board game. “The goal is to move steadily up the board,” he said. “You don’t skip to transformation on day one. You start with the basics, learn fast, and then expand.”

For marketers, the canvas is also a way to explain AI plans to senior leaders. Rather than saying, “we’re testing an AI tool for YouTube ads,” they can point to a clear map that shows how today’s small tests build toward bigger goals, like personalized campaigns or new revenue streams.

With both the quadrants and the canvas in hand, Venkatesan argues that marketers now have a disciplined way to prioritize which AI projects to pursue.

How to Prioritize

Venkatesan said the real power comes from using the quadrants and the canvas together. They give marketers a way to judge which projects are worth pursuing. 

First, what is the upside — will it boost sales or improve the customer experience in a clear way? Second, is the company ready — do they have the data and systems in place to deliver? Third, what are the risks — could sensitive data leak, could the system give wrong answers, or could there be disputes over who owns the content AI produces?

Projects in the external growth space often carry the biggest upside, but they also demand the strongest foundations. A team that is still tidying its data should not jump straight into real-time personalization, said Venkatesan. The risks must be addressed from the start if AI is going to create real value.

He said companies that take this approach can ultimately grow faster. But he also warned that adoption should be careful. “Marketers must manage risks like data security, accuracy and ownership,” he said. “The opportunity is huge, but it is not risk-free.”

This article draws on the insights from the book "The AI Marketing Canvas: A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing," by Rajkumar Venkatesan and Jim Lecinski.