Forrester study reveals 3 traits that separate AI marketing leaders from laggards

Artificial intelligence has captured unprecedented attention across business circles, with AI-focused stock valuations reaching record heights and search interest for “AI agents” surging over 900% this year. The technology has become so pervasive that it’s even providing material for late-night television comedy.

But beneath the media frenzy lies a more practical question: how are B2B marketing teams actually implementing AI, and what separates successful adopters from those struggling to see results?

Recent research from Forrester, a leading business research firm, surveyed over 1,000 B2B marketing leaders to understand the real drivers behind AI adoption in marketing departments. The findings reveal that most organizations have moved well beyond initial experimentation and are now actively deploying AI-powered technologies across various marketing functions.

More importantly, the research identifies a clear pattern among the most successful AI adopters—those marketing organizations in the top 10% based on their progress across fifteen different AI marketing applications. These leading adopters share distinct characteristics that separate them from lagging organizations, providing a roadmap for companies looking to accelerate their own AI initiatives.

3 characteristics that separate AI leaders from laggards

1. Strong partnership between marketing and IT departments

The most successful AI adopters in marketing have forged unusually close relationships between their Chief Marketing Officers (CMOs) and Chief Information Officers (CIOs). Leading adopters are nearly twice as likely to describe their CMO and CIO as strategic partners compared to organizations in the bottom 10% of AI adoption.

This partnership extends beyond occasional meetings to include established processes for ongoing communication and collaboration. In practical terms, this means marketing teams work directly with IT departments to evaluate AI tools, ensure data security compliance, and integrate new technologies with existing marketing systems.

The importance of this alignment becomes clear when considering that AI implementation often requires technical infrastructure changes, data integration across multiple systems, and ongoing maintenance that marketing teams typically cannot handle independently. Organizations where marketing and IT operate in silos frequently struggle with AI projects that stall due to technical roadblocks or security concerns.

2. Data-driven decision-making as a core operational principle

Leading AI adopters have built their marketing operations around data-driven decision-making processes that create natural foundations for AI implementation. A striking 94% of top-performing organizations report that marketing data plays a crucial role in significant business decisions, compared to just 78% of lagging adopters.

This data-centric approach manifests in several ways: marketing teams have ready access to clean, reliable data; decision-makers trust the data they receive; and processes exist for turning data insights into actionable strategies. These capabilities prove essential for AI success because artificial intelligence systems require high-quality data to function effectively.

Organizations with poor data practices—inconsistent data collection, unreliable information sources, or limited data accessibility—find that AI tools produce inconsistent or unhelpful results. Leading adopters avoid these pitfalls by establishing robust data foundations before implementing AI solutions.

3. Investment in AI readiness and governance frameworks

Successful AI adopters don’t simply purchase AI tools and hope for the best. Instead, they invest in comprehensive preparation that includes policy development, training programs, and governance structures designed to guide AI implementation.

Leading adopters are significantly more likely than lagging organizations to have established AI policies that govern how marketing teams can use artificial intelligence tools. These policies typically address data privacy, content approval processes, and guidelines for when human oversight is required.

Additionally, top-performing organizations ensure their marketing teams receive proper training on AI capabilities and limitations. This educational investment helps teams understand how to use AI tools effectively while avoiding common pitfalls like over-reliance on AI-generated content without human review.

The governance aspect extends to having clear processes for evaluating new AI tools, measuring their effectiveness, and scaling successful implementations across the organization.

The reality behind AI adoption in marketing

Despite the widespread enthusiasm for AI, the research reveals a more nuanced perspective among marketing professionals. Two-thirds of B2B marketing decision-makers believe that generative AI—the technology behind tools like ChatGPT that can create text, images, and other content—is currently overhyped. Notably, even a majority of the most successful AI adopters share this skeptical view.

This perspective reflects the reality that AI vendors have often oversold their technologies’ capabilities, leading to inflated expectations about what AI can accomplish without proper implementation and human oversight. However, skepticism about the hype doesn’t translate to dismissal of AI’s genuine potential.

The primary driver for AI adoption among B2B marketers is efficiency improvement rather than revolutionary transformation. Leading adopters are twice as likely to work in organizations experiencing growth through improved productivity compared to lagging adopters. These organizations also report higher revenue growth, suggesting that AI’s practical benefits, while perhaps less dramatic than vendor promises, deliver measurable business value.

Marketing as AI’s natural starting point

Marketing departments have emerged as particularly well-suited environments for AI adoption within larger organizations. The field’s emphasis on content creation, data analysis, and customer personalization aligns naturally with current AI capabilities.

Marketing teams regularly work with large datasets for audience segmentation, create substantial amounts of content across multiple channels, and need to personalize communications at scale—all areas where AI tools can provide immediate value. This natural fit has made marketing a strategic starting point for AI adoption in many companies, with successful marketing AI implementations often paving the way for broader organizational AI initiatives.

The research suggests that AI is beginning to transform core marketing processes, from content creation and campaign optimization to lead scoring and customer journey mapping, while disrupting traditional workflows that relied heavily on manual processes.

Practical implications for marketing leaders

For marketing leaders considering AI adoption, the research provides clear guidance. Success depends less on choosing the right AI tools and more on building the organizational foundations that enable effective AI implementation.

Organizations should prioritize strengthening collaboration between marketing and IT departments, establishing robust data management practices, and creating governance frameworks before making significant AI technology investments. The most successful adopters treat AI as part of a broader digital transformation rather than as a standalone technology solution.

The findings also suggest that realistic expectations about AI’s current capabilities lead to better outcomes than pursuing transformational changes that current technology cannot deliver. Marketing teams that focus on efficiency improvements and process optimization tend to see more consistent success than those expecting AI to revolutionize their entire approach overnight.