As personalization expectations grow, marketers face mounting pressure to deliver custom campaigns across regions, platforms, and audiences, but most still fall short of the goal.

A recent Adobe survey of 300 marketers and 700 consumers reveals a sharp disconnect between marketers’ ambitions and their current capabilities. While nearly all respondents (97%) believe it’s achievable to deliver personalized content at scale, the reality proves far more complex.

In this article, Gagan Mand, Director of Product Marketing and Strategy at Adobe, unpacks the study’s most compelling insights, including what’s holding marketers back and how they can move forward with the help of Generative AI.

Marketers agree on the personalization goal, but can’t reach it  

Marketers surveyed in the Adobe study are aligned, with 97% believing that creating personalized content at scale is achievable. However, 2 in 3 also reported that they find the journey toward this end goal ‘daunting.’

To clarify the path forward, the survey identified three core building blocks of effective personalization:

1. Crafting relevant content (74%)
2. Achieving targeted delivery (63%) – delivering the right message to the right person at the right time through optimal marketing channels
3. Gaining a deep understanding of customer profiles, desires, and behaviors (47%)

To make these building blocks actionable, marketers need to access high-quality, relevant data and utilize tools that transform these insights into personalized, resonant content.

The barriers to personalized content at scale, according to marketers

With operational execution identified as a key hurdle for producing personalized content at scale, what specific barriers must marketers overcome within the end-to-end process of producing, managing, delivering, and measuring content, otherwise known as the content supply chain?

From Adobe’s findings, marketers identified three key pain points:

1. Content volume is ever-increasing – Teams are overwhelmed by the sheer volume of content needed to meet personalization goals across formats, languages, and platforms.
2. Creative bottlenecks remain – Even with martech stacks in place, many organizations lack agile workflows between creative and performance marketing teams.
3. Limited use of real-time A/B testing – Although A/B and A/B/n testing are proven to improve performance, many marketers fail to leverage them to iterate quickly.

A lack of ambition isn’t what blocks the path to scalable personalization; inefficiencies in the content supply chain are. As the study shows, content overload, slow creative cycles, and underutilized testing methods are stalling well-intentioned marketing teams. What are marketers doing to overcome these inefficiencies?

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What high-performing marketers are doing differently

Top-performing marketers already use generative AI tools to overcome bottlenecks and deliver scalable personalization. In fact, 62% of respondents expect AI-powered recommendations and tailored AI content (59%) to play a dominant role in 2025.

Adobe’s end-to-end content supply chain solution, GenStudio for Performance Marketing, optimizes the process of planning, creating, managing, activating and measuring content for marketing campaigns and personalized customer experiences.

In utilizing Adobe GenStudio for Performance Marketing during generative AI-powered email testing to centralize content creation, activation, and measurement, we found:

• A 57% increase in email click-through rates using an Adobe Illustrator email in testing
• An 8.5% increase in open rates was achieved through consistent subject line testing

These marketers aren’t just producing more – they’re building more innovative feedback loops and accelerating decision-making.

Consumer perceptions of personalized content 

With evidence that AI-powered integration leads to increased consumer engagement and click-through rates, let’s consider how marketers can further strengthen their personalization strategies by leveraging insights from consumers.

Personalized promotions and sales were ranked the most critical customized content type by consumers surveyed (65%), followed by relevant product recommendations (35%), early access to new releases (22%), and content suggestions (22%).

While consumers overwhelmingly desire personalized experiences, 98% want control over how brands create customized content in terms of the data they provide.

For creating personalized content, the top data points consumers want to share include:

1. Past purchases (56%)
2. Products viewed (52%)
3. Gender (47%)
4. Age (41%)
5. Language (35%)

The key takeaway? Personalization must strike a balance between customization and consent. Marketers must use data responsibly to build trust while delivering value.

How to accelerate toward scalable personalization

The journey toward personalization at scale is a path toward achieving the ultimate goal of powerful, customized personalization that resonates with consumers. To close the gap between intent and execution, marketers can take these four steps:

1. Centralize and streamline workflows – Break down silos between creative, media, and performance teams.
2. Automate testing – Build real-time testing into campaign workflows – not just post-launch.
3. Embrace AI – Use generative AI and asset management tools to scale content production without sacrificing quality.
4. Measure and iterate – Tie creative decisions to performance data early in the process, not after launch.

Adobe’s latest study confirms what many marketers already feel: personalization at scale is essential but feels frustratingly out of reach. However, the gap can be closed with more intelligent workflows, better collaboration, and the strategic use of AI. Marketers can turn hyper-personalization into high-powered performance.

This study involved a survey of 1,000 Americans (300 marketers and 700 consumers) to explore how different groups perceive personalized content. At a 95% confidence level, the consumer study has a 4% margin of error, while the marketer study has a 6% margin of error. Because this exploratory research relied on self-reported data, it’s important to acknowledge that respondents may have biases or discrepancies between their answers and their actual experiences.

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