AI has moved into almost every marketing discipline. It writes copy, supports ad targeting, reviews content and helps teams understand data more quickly. Naturally, it has also begun to influence CRO.
But CRO works differently from many other fields because it relies on human behavior data, which is complex and rarely follows simple patterns. This is why human interpretation remains essential. This means that AI can speed up a lot of the work, but it cannot replace the thinking that sits behind strong experimentation.
For CRO Journal Vol. 2, we asked a group of experimentation experts to share what they see in the real world. We wanted to understand the biggest mistakes people make when they bring AI into CRO, and which use cases actually help teams scale their work.
Their answers reveal a simple truth: AI in CRO works best when it supports strategic thinking rather than tries to replace it.
AI is not a strategy (and cannot replace one)
A common misunderstanding is the belief that AI can take over the full strategic process. Some treat AI CRO tools as if they can instantly generate a roadmap or uncover the exact cause behind a conversion issue.
The issue is that while AI can process large volumes of data, it does not understand context. It lacks awareness of business goals, constraints, edge cases and the subtle reasons behind user behavior. When AI is given too much responsibility, it often produces explanations that sound convincing but don’t hold up when tested.
Experts consistently highlighted this risk. AI can summarize patterns and surface recurring behaviors, and in some cases suggest areas worth investigating. But deciding which problems matter most and how to turn insights into a solid experimentation plan still requires human judgement.
Where AI actually helps teams
Although AI cannot replace strategic thinking, it can support it in practical and valuable ways. The biggest impact of AI in CRO appears when teams use it to take over parts of the workflow that would normally require time and manual effort. This includes identifying patterns in large datasets, grouping qualitative feedback, preparing first-draft hypotheses or highlighting inconsistencies across UX research.
These are all tasks that scale well with AI. Instead of spending hours reviewing data manually, teams can let AI handle the first pass and focus their time on interpreting what the findings actually mean. This is where scaling AI in CRO becomes useful. AI speeds up the process, but humans still make the decisions.
This is also where tools like Mouseflow’s Mina AI fit in. Mina works as an AI research assistant that helps teams explore their behavior data using simple questions and quickly surface relevant patterns, friction points and user segments. It does not attempt to define strategy or explain why something is happening. It helps teams get to the insight stage faster so they can focus on interpretation and experimentation.
This approach also avoids the creativity and reasoning gap. AI is not strong at original thought, but it is excellent at organizing information that already exists. Treating it as an assistant rather than a strategist helps teams keep quality high while still moving quickly.
The biggest risk is not AI itself, but how people use it
A surprising theme that came up repeatedly was the concern about complacency. AI can make teams faster, but it can also make them rely on shortcuts. Some teams already default to asking AI for answers before thinking through the problem themselves. This often leads to surface-level conclusions and a weaker experimentation culture.
CRO requires curiosity, critical thinking and a willingness to challenge your own assumptions. If teams stop doing that because AI feels convenient, the quality of experimentation drops quickly. The fear is not that AI will replace CRO professionals. The fear is that it will replace the critical thinking that makes CRO effective in the first place.
Experts also pointed out that AI forces teams to question some of the old assumptions behind traditional CRO methods. If AI is exposing gaps in our approach, that is a sign we need to adapt, not a sign AI should be driving the entire process. CRO is evolving, and AI is only one part of that shift.
Using AI as a co-pilot, not a driver
While AI cannot run experiments on its own, it can make good CRO teams even better (and poor processes more visible). Some of the most effective use cases involve using AI to accelerate idea generation, prototype variations or challenge initial interpretations of data. By offering an alternative perspective, AI helps teams reduce blind spots and surface opportunities they might not have noticed otherwise.
This balance is critical. AI works best when it is treated as a partner in the process, not a decision-maker. Humans still choose which ideas are worth testing, how experiments should be structured and how results should be interpreted. When that ownership is clear, AI strengthens the experimentation workflow without weakening the thinking behind it.
If you want to explore these ideas further, the collection of insights from 15 CRO experts is available in CRO Journal Vol. 2, including deeper discussions about AI, experimentation practices and what the future of CRO might look like in the years to come.
