AI Makes Website Performance Optimization Much Easier

Website performance work used to involve a lot of digging through documentation, trying to understand what a particular warning meant, figuring out which plugin was responsible, and then testing one change at a time to see whether anything actually improved.

AI has made that process much easier.

It does not matter whether the website is built with WordPress, Shopify, a custom stack, or something else. The basic process is still the same: find the performance problems, understand what is causing them, make a change, test the result, and keep going until the important problems have been dealt with.

The useful part is that you no longer have to figure out every recommendation from a performance report by yourself.

For the websites we work on, we usually start with PageSpeed Insights.

Run the website through the audit and see what comes back. You might find issues with images, JavaScript, CSS, caching, render-blocking resources, unused code, third-party scripts or something else. Some warnings are straightforward. Others are much less obvious, particularly when the website is using several plugins, a page builder or a performance plugin that is already changing how the site loads.

This is where AI becomes useful.

Instead of staring at a performance report and trying to translate every recommendation into a solution, you can take the relevant result and give it to an LLM. A screenshot is often enough for a straightforward issue, but copying the actual recommendation and the surrounding information gives the model more context.

The important part is telling it what you are actually working with.

If we are dealing with WordPress, for example, we might tell the AI that the site uses Elementor and WP Rocket. That changes the kind of recommendations it can give. There is little value in being told to implement some optimization that requires a completely different stack when the website is already built around tools that can handle the problem another way.

The same principle applies outside WordPress. Give the AI the technology that is actually running the website and ask it to work within those constraints.

Sometimes the PageSpeed recommendation is not enough to understand what is happening.

In those cases, the source code can provide another layer of information. You can copy the relevant page source and give it to the model, or inspect the source yourself and ask the AI to help identify what might be contributing to the problem.

This is particularly useful when the performance report tells you that something is wrong but does not make the underlying cause obvious.

The AI can then connect the reported problem with the code, plugins, scripts or tools involved and suggest changes that are realistic for the stack you are using.

That does not mean you should blindly implement everything it suggests.

This is still performance work, and a recommendation that looks good on paper can have unintended consequences on a real website. Disabling something might improve one metric while breaking a feature. Changing how scripts load might affect a form, tracking, navigation or another part of the page. Removing an asset because it appears unnecessary can create a completely different problem.

The useful workflow is therefore not “ask AI to fix the website.”

It is “use AI to help you understand what to test.”

Make one of the suggested changes. Clear the relevant cache. Run the test again and compare the result with what you had before.

If the problem improved without breaking anything, keep the change.

If it did not, investigate further.

If something broke, reverse the change and try another approach.

Then move to the next issue.

This process becomes especially useful on WordPress because there are usually several layers involved. You might have WordPress itself, Elementor, a theme, WP Rocket, image optimization, analytics, advertising scripts, forms and various other plugins all contributing something to the final page.

The answer is rarely just “make the website faster.”

You need to figure out which part is creating the problem and which part of your existing stack can address it.

That is where AI can save a considerable amount of time. It can take a technical performance report, the tools you are using and the relevant source code, then help turn those pieces into a list of practical things worth testing.

But the testing is still yours.

Run the audit. Find an issue. Give the AI the context. Implement a change. Clear the cache. Test again. Keep what works and move on.

Then do the same thing for the next problem.

Eventually, the performance report starts looking very different from where you began.

That is the part I like about using AI for this work. It does not need to replace the developer or turn website optimization into a one-click process. It simply removes a lot of the friction between seeing a technical problem and understanding what you could try next.

You still need to know what your website is built with. You still need to understand what changes you are making. You still need to test your work.

But you no longer have to spend hours translating every PageSpeed recommendation into a possible solution from scratch.

For performance optimization, that is a pretty useful advantage.

The process is still methodical. AI just makes the method faster.