1. 2 out of 3 use generative AI today, citing early wins

Generative AI in marketing is no longer a question of if, but rather how it’s implemented.

While the report confirms genAI usage is widespread—63% of marketing teams now use it and more than 78% of those say it’s having a positive impact—a critical divide still exists between AI adopters and those truly embedding AI into their marketing workflows. Only 43% of adopters have a formalized AI program in place. Without one, companies may see efficiency gains, but lack the structure to truly scale AI’s impact across strategy, execution, and measurement.
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Of the adopters, leading benefits include increased productivity, improved marketing ROI, and increased job satisfaction.

Scaling marketing operations and reducing operational costs were ranked higher in the 2nd and 3rd ranks, indicating their importance, though they are not the primary focus for marketers.

2. Maturity levels are evolving and high-value AI use cases remain untapped

Only 29% of marketers self-report “advanced” AI maturity, with the majority of adopters still relying on ad-hoc or individual applications like content creation and idea generation—57% of marketers use generative AI for content creation, and 55% for idea generation. It’s also gaining traction in SEO and marketing optimization (45%) and research and analysis (49%), where AI-driven insights help shape strategy.

Adoption lags in high-value areas where AI can drive the most impact. Fewer than a third of marketers leverage AI for brand governance, hyper-personalization, workflow automation, or predictive optimization—powerful capabilities that enable AI to take on more autonomous, decision-making roles. Tapping into these areas presents a major opportunity for innovation and competitive advantage.

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3. Data privacy and output quality are key barriers to scaling AI adoption

The report identifies data privacy and AI output quality as the biggest barriers to marketing AI adoption—underscoring that as AI becomes more ingrained in marketing workflows, the conversation is shifting from “Can we use AI?” to “How do we use it responsibly and effectively?

Meanwhile, concerns about AI output quality reflect growing pressure to maintain brand integrity, accuracy, and consistency at scale. Companies working to scale AI content creation without human oversight risk undermining the very efficiencies they seek to achieve.

Leadership buy in, AI expertise, and budget constraints were also identified by respondents as ongoing hurdles, indicating organizational growing pains as AI adoption moves from experimentation to full on integration into marketing operations.

4. Most marketers don't see the ROI of AI

Despite increased marketing ROI being cited as a leading benefit of AI, 51% can't measure the ROI of their AI investments. However, another 22% plan to begin tracking AI ROI in 2025, showing that measurement is becoming a bigger priority.
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Larger marketing teams are leading the way—62% of teams with over 1,000 marketers track AI ROI, compared to just 38% of small teams (<25). The ability to measure ROI drops sharply with general-purpose AI tools. Companies using marketing-specific AI tools are 37% more likely to measure ROI than those relying on general-purpose AI (only 20% can measure).

5. As companies scale, the bar for on-brand and high-quality marketing gets higher

Bigger teams, higher stakes. Larger organizations face unique challenges as their marketing demands scale, and their tools need to address these increasing complexities, from measuring ROI effectively to maintaining brand consistency across multifaceted campaigns.

According to our data, large enterprises (>$1B) prioritize AI output quality (31%) and brand governance (33%) to protect brand integrity, manage risks, and maintain compliance. Comparatively, smaller companies are more focused on budget, leadership buy-in, and data privacy.
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The report also reveals that as the number of marketing employees grows, companies increasingly adopt AI solutions tailored for marketing. In fact, 57% of companies with over 1,000 marketing employees use domain-specific marketing tools. Conversely, the use of general AI tools like ChatGPT decreases as marketing teams expand.

6. Theres a stark CMO-team alignment gap

Perceptions of maturity and leadership commitment differ by seniority. CMOs are most likely to view leadership as “very committed” (65%), while those lower in the hierarchy, such as SVPs (40%), VPs (30%), Directors (40%), and Managers (37%), rate it lower.

Additionally, leadership is far more confident in AI maturity than the teams using it daily. 44% of CMOs say their AI maturity is “advanced” or “very advanced,” but confidence drops among those responsible for execution—only 27% of managers rate their AI usage at the same level.

7. The 7 key traits of high-maturity marketing teams

The most mature marketing organizations don't just adopt AI—they build the structures, processes, and strategies to scale it effectively.

Our research shows that companies who self report “very advanced” AI maturity consistently outperform others across 7 key components:

1. Documented Use Cases: 75% of “very advanced”companies document their AI use cases, compared to just 22% at the beginning stage.
2. Continuous Experimentation: Innovation remains a priority, with 59% of “very advanced”organizations still testing new applications.
3. Leadership Buy-in: 54% of “very advanced” organizations report active AI use among senior leaders, signaling a commitment from the top down.
4. Workflow Integration: More than half (51%) of “very advanced” companies have successfully integrated AI into their daily workflows.
5. Measurement of AI ROI: 96% of “very advanced” organizations measure the ROI of their AI investments compared to only 22% at the beginning stage.
6. Strong AI Governance: 79% of “very advanced” organizations have an AI council, 86% offer advanced AI training, and 79% provide document policy and guidelines.
7. Domain-Specific AI Adoption: 71% of “very advanced” organizations are using domain-specific AI tools vs. 21% who are only using general-purpose tools.

8. There's a shift from general-purpose to domain-specific AI

The key to closing the gap between adoption & impact 
is moving beyond generic, one-size-fits-all tools to 
domain-specific AI—models tailored to a company’s 
unique data, brand guidelines, and marketing 
workflows. This isn’t just a best practice; it’s one of 
the defining traits of high-maturity organizations.

And our data shows the shift clearly: 71% of “very advanced” teams are using domain-specific AI vs. 21% who are solely relying on general tools. Nearly all domain-specific AI users plan to expand usage in 2025 and are 37% more likely to measure ROI.

‍About the report: to gather insights for this research, we surveyed 503 marketing leaders, drawing from a combination of Jasper's database and Benchmarkit, a third-party research firm. Participants represent a broad spectrum of industries, including technology, life sciences, financial services, and more. They also span a variety of marketing functions such as demand generation, content marketing, and product marketing. The survey was designed to capture a comprehensive understanding of AI adoption, its impact on marketing teams, and the related benefits and challenges.