From AI experimentation to AI advantage

Marketing teams are being asked to do more than ever: create for more channels, move faster, prove ROI, and protect the consistency and distinctiveness of their brand – all with the same or fewer resources.

AI can help, but not if it’s treated only as a faster way to produce more content. The next phase of AI in marketing will belong to teams that use it to improve judgment, strengthen operations, and understand how their brands are represented in AI-driven discovery.

For the past year, much of the marketing conversation around AI has centered on productivity. How much faster can we write? How many more variations can we create? Which manual steps can we automate?

Those questions matter. But they’re not enough.

The real opportunity isn’t simply to create more content. It’s to create more effective marketing.

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A common concern is that AI will flatten marketing into a stream of lookalike content. That risk is real when teams use AI only to increase output. More content doesn’t automatically mean better content, and speed without judgment can make brands less distinctive.

The better path is to use AI to support what strong marketers already do: understand audiences, identify meaningful patterns, sharpen ideas, and make informed creative choices.

That’s why the idea of evidence-based creativity is so useful. It rejects the false choice between creativity and data. Modern marketing needs both. Contentful’s research with Atlantic Insights found that nearly half of marketers identified data analysis and interpretation as a top skill needed in the profession, alongside capabilities such as digital experience design, personalization strategy, and writing for AI tools.

Used well, AI can bring more evidence into the creative process without making that process mechanical. It can help marketers analyze audience behavior, summarize research, identify content gaps, and test ideas faster. That creates more room for the work that still requires human taste, empathy, and judgment.

Laura Thornley, Director of Field Marketing, EMEA

The distinction is important. AI should be the vehicle, not the driver. It can help teams move with more confidence, but it should not replace the strategic thinking that gives marketing its value.

Advantage depends on the operating model

Individual AI use cases can create momentum, but they rarely create lasting advantage on their own. The bigger opportunity comes when AI is embedded into the operating model of marketing.

That means connecting AI to workflows, data, content structures, governance, approvals, localization, measurement, and brand standards. It also means being clear about where AI should accelerate work and where human review is essential.

Contentful’s Atlantic Insights report, which surveyed 425 marketing decision-makers, including 103 chief marketing officers, focuses on how forward-looking marketing leaders are incorporating AI into critical infrastructure rather than treating it as a side experiment. That shift matters because content creation is only one part of the challenge. In many organizations, the harder work is scaling content safely and consistently.

Teams need to manage brand voice across regions, keep messaging current, reuse content efficiently, and ensure that content can adapt across websites, apps, campaigns, and emerging channels. AI can help with that work when it’s supported by clear systems.

Structured content is especially important. When content is modeled clearly and managed centrally, teams can reuse it more effectively and make it easier for systems to understand, assemble, and deliver it accurately.

Charlie Bell, Senior Director, Solution Engineering

Contentful has described structured content as useful for both traditional search engine optimization and emerging generative engine optimization because it helps AI bots parse and surface content accurately.

This is where governance becomes practical, not theoretical. The goal is not to slow teams down. It’s to give them enough structure to move quickly without creating risk, duplication, or inconsistency.

In a tighter budget environment, that discipline matters. AI earns investment when it improves the way marketing works, not when it simply adds another layer of tools. Leaders need to measure workflow improvement, speed to market, content quality, cost control, and business outcomes. Activity alone is not enough.

Discovery is changing outside the business

The inside-out story is only half of the AI shift. While marketing teams are using AI to improve how they work, buyers are using AI to change how they discover, compare, and evaluate brands.

This isn’t just happening at the point of purchase. McKinsey found that more than 70% of AI-powered search users ask questions at the very beginning of the buying journey, using AI to understand categories, explore brands, and evaluate options before ever visiting a company website.

That changes the job of marketing. Content now has to work for people and for the systems that interpret, summarize, and reuse it. Marketers need to ask not only whether they rank but how they are represented.

Are answer engines describing the brand accurately? Are they using current messaging? Are they comparing the company fairly? Are they missing important proof points? Are they relying on outdated or incomplete information?

This is not a replacement for SEO. It’s an expansion of the discoverability conversation. Contentful’s guidance on generative engine optimization explains that generative AI is reshaping search and creating new considerations for marketers and content strategists, while Google and traditional SEO remain relevant.

The performance question is changing. It’s no longer only “Did we rank?” It’s also “What story do AI answers tell about us?”

How to run an AEO audit

A workflow for turning AI answer evidence into audit findings, ownership decisions, and a prioritized AEO action plan.

From monitoring to action

As AI discovery becomes more important, many teams will start by monitoring visibility. That’s a useful first step. It helps teams understand whether their brand appears in AI-generated answers, how often it’s mentioned, and how it compares with other companies in a category.

But visibility is only valuable if it leads to better decisions.

A team may learn that its brand is missing from a relevant answer, that a product is being described inaccurately, or that a competitor is being recommended more often. The harder question is why. Is the issue unclear positioning? Missing content? Weak evidence? Outdated product information? Inconsistent messaging? A lack of structured, authoritative content that answer engines can interpret?

This is where marketers need to move from observation to decision-making.

Palmata by Contentful was introduced as a content decision system for AI discovery that helps organizations understand, measure, and improve their presence in answer engines. It’s designed to show why AI-generated answers appear the way they do, prioritize content actions, and model the potential impact of recommended changes.

That kind of capability reflects a broader shift in marketing operations. AI discovery cannot be treated as an occasional audit or a separate reporting exercise. It needs to connect to content strategy, web strategy, competitive positioning, product marketing, and measurement.

For marketing leaders, the value is clarity.

Teams need to know where they are vulnerable, what to change first, and how those changes may improve brand representation. For technical and operations teams, the value is structure. They need systems that make content easier to manage, govern, update, and interpret at scale.

Charlie Bell, Senior Director, Solution Engineering

The teams that can do both will pull ahead

The next generation of marketing leaders won’t be defined by how much AI they use. They’ll be defined by how well they combine AI with human judgment, operational discipline, and a deep understanding of their customers.

Inside the business, AI should help marketing teams become faster, smarter, and more consistent. Outside the business, it’s already influencing how buyers discover, compare, and evaluate brands long before they visit a website.

The teams that create lasting advantage will be the ones that can connect both sides of the equation. They will use AI to strengthen how marketing works internally while also preparing their content and brand signals for the way discovery is changing externally.

That’s the real shift from AI experimentation to AI advantage. It’s not about producing more for the sake of more. Nor is it about adopting more tools. It’s about building the systems, skills, and judgment to make marketing more effective wherever audiences encounter the brand.

Laura Thornley, Director of Field Marketing, EMEA

Ready to move from AI experimentation to AI advantage? Learn how Contentful helps marketing teams build AI-ready content operations and improve their representation across AI-powered discovery.

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