The Ultimate Marketer Generative AI Wishlist

The Gist

• Hype cycle impact. Generative AI’s adoption follows a two-stage hype cycle: refining technology and identifying practical use cases.

• Content-driven AI. Generative AI is changing how content is created, from ideation to production and optimization. This helps marketers streamline workflows and enhance personalization.

• AdTech challenges. Generative AI adoption in advertising lags behind other use cases due to barriers like reliance on agencies and reluctance to let AI make creative decisions.

Generative AI is reshaping marketing, and its adoption is inevitable. The question is no longer whether companies should implement it, but which use cases of generative AI in marketing will deliver real business value. The fear of missing out (FOMO) is lurking everywhere with every new technology, as reflected in the hype cycle. A common concern we hear from clients is, “Are we behind on generative AI?”

What’s crucial to understand is that there are two overlapping hype cycles, each with its own timing. One hype cycle is for the technology itself, and another is for its practical applications. 

• Technology hype cycle: The phase in which the technology is refined and optimized for real-world use.

• Use case hype cycle: The phase in which users learn which use cases to adopt and how to effectively integrate the technology into their daily workflows.

These cycles don’t move in sync, but they follow each other more closely over time. Consider electricity. While discovered in the 1830s, it took nearly a century before it became a staple in homes, powering radios, toasters and washing machines. Or consider the invention of the internet around 1969; its more widespread use began in the 1990s.

Generative AI follows a similar trajectory, yet shorter. Instead of starting with the technology, organizations should focus on high-impact customer journeys and ask, “Where can generative AI in marketing make the biggest difference?” This approach accelerates learning and adoption.

With the technology now mature, the focus should move towards identifying and prioritizing proven, high-value use cases. To provide that clarity, we surveyed 283 marketing professionals, including CEOs, consultants, marketing directors and operations managers, to assess how frequently marketers are adopting generative AI use cases.

The use cases examined were carefully selected from our Martech supergraphic, which tracks evolving trends in marketing technology. In 2024, generative AI tools accounted for 77% of the growth in the marketing technology landscape, with 2,324 generative AI solutions emerging. This is a testament to AI’s transformative role in the industry.

Yet, adoption patterns vary. While some use cases have become integral to daily workflows, others remain largely aspirational.

Table of Contents

Key Martech Functions for Generative AI

The survey results highlighted distinct patterns across the six key martech categories. These categories are content, data, social, sales, ads and management. Among these, content and data use cases dominated the top 20, while ad-related use cases struggled to gain traction.

Content Takes Center Stage

Content-related generative AI applications (orange) lead the pack, with seven of the top 20 use cases focusing on content ideation, production and optimization. Marketers are heavily relying on generative AI to streamline workflows, enhance personalization and keep up with the increasing demand for high-quality content.

Here are the top content use cases:

• Copy Ideation (50.7%): Generative AI is extensively used to brainstorm and generate fresh ideas.

• Copy Production (43.9%): Automation plays a major role in drafting content across multiple formats.

• Content Optimization & Testing (28.6%): Marketers use AI to refine content performance.

Data as a Strategic Driver

Data-related use cases (blue) for generative AI in marketing are gaining traction, with six of the top 20 use cases focused on knowledge management, competitor research and documentation. Generative AI’s ability to analyze vast amounts of data and extract actionable insights is a key driver behind its popularity.

Here are the top data use cases:

The AdTech Adoption Gap

Despite the widespread presence of advertising (red) in digital marketing, generative AI adoption in ad-related use cases remains relatively low. Surprisingly, the highest-ranked ad use case appears at position #28, with seven of the bottom 23 use cases belonging to the AdTech category. 

Several factors possibly contribute to this adoption gap. For one, many marketers rely on agencies and external partners for ad production, which limits their direct interaction with generative AI tools. Also, platforms with embedded AI like Google’s Performance Max and Facebook’s Advantage+ automate ad optimization without marketers actively realizing the role AI plays. Finally, marketers may be hesitant to delegate creative decision-making to AI.

Social and Management: Two Mixed Bags

Social and management use cases (yellow and grey) show divergent trends in adoption. While some applications, like social media analytics and content scheduling, enjoy high adoption, others remain underutilized. 

For social, analytics and scheduling tools are most frequently used. Low adoption social use cases like AI-driven community engagement and influencer management face resistance.

Generative AI in marketing is widely adopted for tasks like management transcription and summarization, significantly improving productivity. However, AI tools for talent management and recruitment remain relatively underutilized.

Sales: Selective Integration

Sales-related generative AI use cases (green) are applied selectively, mainly in areas like lead scoring and customer segmentation. Marketers appear to prefer AI for data-driven insights rather than automating the sales process entirely. This emphasizes the continued importance of human involvement.

Related Article: Two Years of Generative AI: How Has Customer Experience Delivery Changed?

Generative AI in Marketing: Usage Patterns and Frequency

The survey also shows that generative AI applications used monthly tend to be more strategic and specialized, while daily or weekly usage is driven by operational and production needs. Monthly-used applications (i.e., image and video ideation, knowledge documentation and competitor research) require periodic effort due to their complexity and resource intensity. These functions support content planning, strategic insights and high-level optimizations rather than routine execution.

By contrast, daily and weekly use cases (i.e., copy ideation, production and transcription) are embedded into regular workflows. Some use cases, like image/video ideation and lead scoring, saw early adoption but were later abandoned, likely due to challenges in ROI, integration or shifting priorities.

Related Article: How Generative AI in Content Strategy Is Changing Digital Engagement

The Generative AI Wishlist in Marketing: Top Untapped Use Cases

The survey also identifies generative AI use cases that respondents have “not yet tried.” This data offers insight into areas of interest that marketing professionals have yet to explore or implement. Let’s call this the “wishlist” for now. These use cases fall into three categories based on the level of interest. 

What Do Marketers Really Want to do With Generative AI?

Core Questions Around Generative AI in Marketing Adoption

What drives high adoption of generative AI use cases?

Seamless integration, clear ROI and minimal training drive adoption. Content creation and data-driven automation see strong adoption, while more strategic or experimental use cases require clearer justification

How can companies scale generative AI beyond experimentation?

Align generative AI with business goals, integrate it with martech and track performance. Prioritize high-value use cases, secure leadership support and upskill teams for lasting impact.