“If it were easy, everyone would do it.”

And they did. Adopting AI tools was relatively simple for most marketing organizations, and nearly every marketing team uses AI today. But almost no one is winning with it.  

Forty-seven percent of marketing leaders and 30% of laggards now describe AI as a core capability, up from 35% and 8%, respectively, only a year ago. Laggards have also caught up—or come close—on basic AI executions, such as media buying and content creation.

On the surface, it looks as though the AI gap between leaders and laggards is shrinking. In reality, the performance gap couldn’t be wider: Only 6% of marketing organizations—leaders included—say AI delivers significant performance impacts today.

That small subset is really pulling away from the pack: Leaders are twice as likely than laggards to attribute double-digit revenue growth or cost savings to AI initiatives (see Figure 1).

Figure 1

What marketing leaders do differently with AI

Leaders aren’t generating value because they have access to better AI tools—everyone is largely using the same underlying models. Instead, top performers are doing three things differently:

• Centralizing AI strategy to align enterprise and marketing priorities and remove functional silos so wins compound across the entire business
  
  
• Completely rebuilding workflows and restructuring teams around AI instead of doing old work faster
  
  
• Pointing AI at customer-focused use cases to generate deeper customer intelligence, implement more experiments, and compress innovation cycles

Instead of just automating tasks, leaders are redesigning their entire operating model around the customer and AI.

Marketing leaders execute top-down, centralized AI strategies

In leading organizations, marketers are 1.8 times more likely to follow a centralized AI roadmap with priorities set directly from the top (see Figure 2). Conversely, laggards are 10 times more likely to operate without a defined AI strategy for marketing.

Figure 2

With centralized strategies, leaders pursue full-scale transformations where performance gains compound across functions, strengthening both the business case and ROI for AI investments. This top-down alignment also clears the operational hurdles often holding laggards back, including budget constraints, unclear ROI targets, and legal and risk concerns.

Executive sponsorship also ensures financial backing: More than 40% of leaders dedicate 11% or more of their budgets to AI, compared to only a quarter of laggards. Leaders are also twice as likely to allocate over a quarter of their annual marketing budget toward AI use cases, licensing, data, or research (see Figure 3).

Figure 3

Marketing leaders redesign entire workflows and team models around AI 

Rather than layering AI onto existing processes, leaders are 3.7 times more likely than laggards to fully redesign workflows (see Figure 4) and twice as likely to restructure teams and job descriptions around AI capabilities.

Figure 4

Speed and capacity play a critical role in this shift. One chief marketing officer (CMO) said five- to 10-person teams can now generate output equivalent to traditional 40- or 50-person teams. Another shared that national promotions can now be launched in a fraction of the time. They said, “We don’t need as many doers. The process of doing will get faster every day, so you need people that are extremely good at knowing what to create the first time.”  

AI is also redefining marketing roles. One retail CMO predicted needing talent who can orchestrate AI agents across specialized marketing functions, while another suggested hiring marketers who are “generalist enough” to manage workflows and guide categories without deep technical skills. Narrow specializations built during the digital marketing boom are losing relevance as the industry returns to a more holistic talent model.

On the technology side, leading organizations are 2.4 times more likely to embed AI directly into their marketing tools. Just 6% of leaders rely on general-purpose tools (such as ChatGPT, Copilot, or Gemini), compared to 16% of laggards.

Marketing leaders point AI at customer-focused use cases

Rather than using AI to automate routine work, leaders pursue more advanced AI use cases to orchestrate omnichannel activations and deepen customer intelligence. Compared to laggards, leaders are 5.2 times more likely to focus on customer-centric use cases and 1.5 times more likely to use AI to enhance personalization and customer experiences (see Figure 5).

Figure 5

Leaders also leverage AI to accelerate the test-and-learn cycle—in fact, they’re 8.5 times more likely to run 100 or more experiments per month (see Figure 6).

For example, Nestlé built a proprietary AI platform to deepen consumer insights and accelerate concept testing, compressing development cycles from six months to just six weeks. Nestlé trained employees to use AI to analyze market trends and social media signals, generate and refine product concepts, and simulate consumer responses. The initiative has generated nearly three times the ROI from a combination of cost savings and top-line growth.

Figure 6

Leaders are also far more likely to adapt their strategy based on AI-driven insights. Nearly 70% of leaders regularly or extensively adjust their marketing strategy and spending based on AI, compared to just 31% of laggards (see Figure 7). Leaders are also two times more likely to leverage AI to improve demand forecasting and revenue modeling, and three times more likely to use AI insights to continuously reallocate spending across channels.

Figure 7

Walmart embedded AI across its merchandising value chain to help it monitor and adjust performance. It has created more than 1,700 digital twins—photo-realistic 3D stores rendered in the Nvidia Omniverse—to test traffic flows and planogram resets. AI agents have helped the retailer predict customer behavior and identify potential traffic bottlenecks before rollouts. Walmart’s digital twin technology has resulted in 10% faster resets, delivering substantial time and cost savings across its store network.

How to become a marketing leader with AI

Leadership demands enterprise-wide commitment—driven directly from the top—to reset the marketing operating model.

Laggards are rapidly closing gaps in basic AI adoption, media buying, and creative development. But simply putting AI tools into people’s hands isn’t enough to build a competitive edge. Leaders use AI to connect meaningfully with customers and execute faster, smarter programs.

To become a marketing leader, organizations must establish a centralized AI strategy that’s laser-focused on customer value. Organizations willing to undertake the hard work of enterprise transformation will reap the rewards: revenue growth, sustainable cost savings, and market leadership. 

• Methodology

  In April 2026, Bain & Company surveyed 1,397 CMOs, chief financial officers, and senior marketing and finance executives across technology, consumer, retail, financial services, media, applications, education, landmark, and home consumer services. Survey findings were supplemented by executive interviews and Bain’s client engagement experience to inform this report.

  In this study, leaders were distinguished by two factors: more than 11% annual revenue growth and more than a seven-point annual market share growth. Laggards recorded flat or declining revenue growth and flat or declining market share growth. A third group, “neutrals,” did not meet either criterion.

  This is the 10th annual marketing leadership study conducted by Bain; as such, prior leaders and laggards research served as a benchmark.

  These findings represent a point-in-time view of AI adoption and sentiment. As the landscape continues to evolve rapidly, they should be interpreted in the context of when this research was conducted.