AI is not a tool for the future; it’s a tool for now.

In a short space of time, AI has become a vital part of the CRO toolkit, with teams adopting that mindset. 

That said, AI isn’t a replacement for human insight. People need to feed the right data and prompts, still need to interpret findings, and still need to apply them in a way that’s strategically aligned with broad brand goals.

AI tools in digital commerce experimentation

There’s now a universe of AI tools available, particularly for ecommerce merchandising. There’s everything from product recommendations and sort rules on PLPs to smart search optimization.  

When it comes to CRO, however, AI is particularly valuable in supporting experimentation, making it easier to design, analyze, and optimize tests. 

CRO itself is a broad umbrella that covers user testing, data analysis, funnel analysis, and optimization. Experimentation sits within that umbrella.

It’s the process of testing ideas and theories to improve website performance. The typical experimentation workflow includes several stages: analysis, hypothesis definition, building, testing, and interpreting results.

Using AI to define hypotheses

One of the most impactful ways to use AI is in the early stages of experimentation, specifically during hypothesis definition. 

Defining a clear hypothesis can be time-consuming and challenging. So using AI tools like ChatGPT or Gemini can take your data insights, funnel analysis, and research inputs to generate well-structured hypotheses.

AI can also work in reverse.

If your business has a particular strategy or focus area on your website, you can feed a potential hypothesis into the AI and ask where your data insight efforts should be concentrated.

Doing this, AI accelerates the manual work of identifying patterns and consistencies while helping you focus on the areas most likely to deliver impact.

Remember, though, that a hypothesis is an educated guess, not a guarantee. And here lies the importance of human interpretation.

Choosing the right CRO metrics with AI

AI also plays a role during the testing stage. It can help identify the most appropriate metrics to measure success.

For example, when testing changes on a PDP, like moving the “Add to Cart” button, refining product descriptions, or reducing scroll length, AI can suggest which metric best reflects the impact of those changes.

In the CRO world, much of the work involves managing stakeholder expectations and showing progress on big-picture goals, like moving the revenue needle.

But stakeholders and CRO leads often have different perspectives on what to measure. Some focus on revenue or conversion rate, while others might track add-to-cart rates. 

AI can analyze all the data, the hypothesis, the test details, and sample sizes to recommend an optimal primary metric. But again, human judgment is necessary to validate AI’s recommendation and ensure it aligns with business goals.

In this sense, AI functions as a virtual sounding board. It helps identify overlooked metrics, interprets what they could mean for the business, and suggests broader applications. 

For example, instead of tracking total revenue, focusing on add-to-bag metrics might make more sense if the test involves moving an add-to-cart button. These insights can make a test applicable across multiple pages or sections, increasing its value. 

AI also provides a safety net during experimentation. 

Feeding AI information about your test, the planned changes, and what you’re measuring can help flag risks, suggest adjustments, or indicate if your hypothesis needs refining.

It acts like a virtual colleague, helping ensure nothing is overlooked.

Supporting iteration and optimization

Experimentation rarely ends with one test. You often need to iterate, re-test, or make minor adjustments. AI can support these post-test activities.

Whether a test is a winner, loser, or inconclusive, AI can help interpret the results, suggest variations, and provide insights to drive better conversion or revenue outcomes.

This approach ensures your experimentation program continues to deliver value and evolve over time.

Top tips for using AI in experimentation

Finally, some top tips for using AI effectively in CRO and experimentation. 

1. Don’t take AI’s word as gospel

Maintain a healthy sense of skepticism and ensure human oversight. It can easily generate fake news, fabricated quotes, misinformation, and false statistics.

AI does not understand your customers, your users, your brand, or your brand’s tone of voice — the nuances driven by marketing, creative, and brand teams require human judgment. 

2. Use traditional CRO and AI-driven CRO together

Use a combination of both approaches.

Choose what is best for your testing path, your brand, and you as an optimization specialist. AI can provide shortcuts, safety nets, and sounding board moments, but it should enhance, not replace, human decision-making.

The key is to focus on doing what’s best for your customers while using AI to make experimentation more insightful and effective.

AI in CRO

AI is transforming CRO and the broader experimentation landscape, and those who embrace it thoughtfully will gain a competitive advantage.

As AI continues to evolve, it will play an increasingly central role in shaping experimentation strategies and optimizing digital commerce experiences.

Amazing things are possible in digital commerce when you look up.

If you’re looking to implement AI in your CRO practice, reach out to us.