B2B Marketing

AI can speed up CRO research. But which parts should you actually trust it with?

That’s the question we set out to answer in last week’s AI Native Marketer workshop, hosted by CXL President Hesh Fekry and CRO consultant Ruben de Boer, who has spent more than 15 years running experimentation programs.

The goal was to move beyond the theory and see what happens when you actually apply AI to a proven CRO research process. We built an AI-powered workflow that automates three parts of ResearchXL: heuristic review, thematic analysis, and test idea generation.

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Last week, as part of our AI Native Marketer program, we ran a live workshop on building an AI-powered AEO and GEO research pipeline.

There’s increasing pressure on marketers to keep up with AI search. Follow the right people, read the latest studies, monitor LinkedIn, subscribe to every newsletter, and spot the next tactic before everyone else does.

But here’s the problem: marketers are also expected to ship more than ever before. How are you supposed to move faster when you’re still spending hours every week manually scanning articles, case studies, and social posts just to figure out what’s actually worth paying attention to?

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A second brain is a folder of plain text files that gives your AI assistant persistent memory of your projects, your voice, and how you work, so it stops starting from zero in every new conversation.

Marketers are being asked to do more with the same headcount. One marketer with AI is increasingly expected to ship the output of three.

We ran a hands-on workshop inside our AI Native Marketer program to address this new reality: a structured knowledge base, built in the free note-taking app Obsidian and operated by Claude Code, that any AI can read at the start of every session.

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B2B marketing workflow automation works when you redesign the workflow before you touch a tool, not after.
The common failure mode is opening Claude, ChatGPT, or a workflow platform and handing over an entire task, hoping the output holds up. It rarely does.

The process for building genuine automation hasn’t changed since before AI existed: you still have to know each step of the existing workflow in detail before you can decide what an AI or automation should replace.

What gets inserted into each step has changed; the discipline of mapping the step itself hasn’t.

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The biggest limitation of large language models today isn’t intelligence. It’s memory.

Open a new chat, switch to a different project, or kick off a new workflow, and you’re back to square one. You re-explain who you are, how you work, what your goals are, your tone of voice, your systems, your active projects, and the dozens of small details that actually make an AI’s output usable instead of generic.

Multiply that across every chat, every project, and every AI agent or automation you build, and the problem compounds fast.

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Most marketers will tell you they use AI every day. What the data shows is a different story.

A few weeks ago, we launched an AI skills assessment to help marketers identify where they actually stand, whether they’re AI-assisted, AI-integrated, or truly AI-native. The assessment was built to measure real competency across five areas: workflow, production, research, analytics, and AI operations.

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AI-assisted marketers are becoming more replaceable. Marketers who build AI systems around their work are becoming more valuable, more in demand, and harder to compete with.

That’s not speculation. Over the past few months at CXL, we surveyed hundreds of B2B marketers and analyzed how AI is reshaping marketing roles, workflows, and organizational expectations.

The clearest signal from our research: becoming an AI-native marketer is where the industry is heading, and increasingly what companies are hiring for.

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