Get Better Adobe AI Results with Smart Metadata Strategies

As Adobe continues to extend agentic and GenAI capabilities across its martech offerings, how can leaders and operations managers make the most of those features in 2026? In the race to adopt AI, it’s important to first make time to develop clear strategies and practices.

At a high level, realizing value from Adobe’s AI offerings starts with a clear understanding of where agentic AI can fit into your existing marketing technology suite, where it can enhance existing processes, and where it can function as a martech tool in itself. Articulating this kind of roadmap allows for easier, more comprehensive planning about how AI in general and agentic AI in particular can help your team with the creation, management, and delivery of marketing assets.

Why metadata is now central to success with AI

At a more granular level, effective use of AI to accelerate content creation and process improvements requires a new look at metadata management. Metadata, the “data that describes data” has always been important for content organization and SEO. Now, because agents rely on metadata to understand the resources they’re working with, it’s increasingly valuable as a way to enable brand-compliant, AI-powered content generation, process automation, and agentic workflow orchestration.

As AI becomes embedded in more aspects of marketing (and business functions in general), organizations must focus more intently on providing accurate and consistent metadata, and associating it with virtually every function of the business. That kind of metadata framework will support GenAI practices that deliver real value.

For example, an image is a simple asset without metadata. The metadata that you associate with it describes the asset so it can be used by AI and other automation tools to generate on-brand content. For an image, basic metadata might include:

• Content type: a photograph

• Image subject: a car, a landscape, a person

• Subject qualities: a red car, a snowy landscape, a tall woman

• Audience segment: Who does this image appeal to?

If you create standardized metadata models to describe asset attributes like these, your AI tools can quickly and correctly identify content that meets a specific need. If you’re looking for winter images for a holiday campaign, a snowy landscape might be ideal, for example. Organizations that make metadata the bedrock of their AI strategy will be in the best position to get the most value from the AI resources in their martech stack.

The metadata management mindset

Metadata has been around for years, mostly operating in the background. The new need for metadata sanitation and consistent metadata practices requires a mindset shift to thinking about content at a metadata level. Just as organizations build websites to tell the world what they do, they now also need comprehensive internal metadata models that describe content, audience, and success attributes so agents and AI models know how to use that content.

To understand how this approach will shape content discovery over the longer term, think of users interacting with content by getting it from a “librarian” like an AI agent or ChatGPT, rather than going to the library and finding the content on the shelf on their own. This shift in content access means that organizations need to create content for the librarian as well as the user.

As we get closer to shopping within AI chat models, brands that have the right metadata structure in place to deliver their content to internal agents will also perform better in AI-powered search results. Brands that haven’t invested in metadata structure and strategy will risk losing visibility. This is in line with a typical seven-year cycle in which the trend is moving toward content-led design, with less emphasis on experience-led design. That also means it’s time to shift our thinking toward optimizing content creation and visibility through metadata, information architecture, and content-led strategies.

Bringing it all together in Adobe Brand Concierge

One of Adobe’s new products for the Adobe Experience Platform is Adobe Brand Concierge, which uses orchestration agents and digitized brand guidelines to generate content to answer customer questions in AI chat conversations. When your organization has properly structured, consistent metadata for your content assets, you have also essentially created the metadata for your brand. Brand Concierge can use that metadata to produce on-brand content or surface existing content that’s relevant to what your customers are looking for in real time.

Innovative AI strategies for 2026 and beyond

Metadata is now the digital bridge between AI and human strategy. This year, marketing teams that think in terms of metadata will be able to realize the most value from AI tools in their Adobe stack and in other areas of this business as well. They will also be able to raise their visibility in AI search results.

An experienced partner can help you create the foundation you need to accelerate your journey to AI success. As an Adobe Platinum Solution Partner, Rightpoint has extensive experience helping organizations structure and optimize their content supply chains to fully leverage Adobe’s always-evolving capabilities. Ready to refine your AI strategy? Contact us to learn how we can help.