FishingSEO
AI in SEO

7 Ways to Improve Core Web Vitals With AI

By FishingSEO••10 min read

Only 48% of mobile sites achieved good Core Web Vitals in the 2025 HTTP Archive data, even after years of optimization work (HTTP Archive, 2025). That tells you two things fast: Core Web Vitals are still hard, and most teams still need a better workflow.

AI can help, but not by “auto-fixing SEO” with one click. The useful version is simpler: AI helps you spot bottlenecks faster, group similar issues, generate fixes, and catch regressions before they hurt rankings and conversions.

Quick Summary

  • Core Web Vitals are still about LCP, INP, and CLS, measured at the 75th percentile of real visits (web.dev).
  • AI is best at analysis, prioritization, and automation, not replacing performance engineering.
  • The biggest wins usually come from images, JavaScript, third-party scripts, and unstable layouts.
  • You should use AI with field data from CrUX, Search Console, or your own RUM setup, not lab scores alone (web.dev).

What Core Web Vitals Mean in Practice

Core Web Vitals measure three parts of page experience:

  • LCP (Largest Contentful Paint): how quickly the main content appears. A good score is 2.5 seconds or less.
  • INP (Interaction to Next Paint): how responsive the page feels when users click, tap, or type. A good score is 200 ms or less.
  • CLS (Cumulative Layout Shift): how stable the layout is while the page loads and updates. A good score is 0.1 or less.

Those are Google’s current thresholds, and they are evaluated on real-user data at the 75th percentile of page views (web.dev).

One important nuance from Google: Core Web Vitals matter, but they are not the whole ranking story.

“Good stats ... don't guarantee good rankings.”
— Martin Splitt, Google Search Relations (Google Search Central)

That is why the smart play is not “chase scores.” It is improve user experience on the pages that matter most for SEO and revenue.

How AI Improves Core Web Vitals

AI works well here because performance work creates a lot of messy input:

  • Search Console exports
  • CrUX data
  • Lighthouse reports
  • Chrome DevTools traces
  • third-party script lists
  • component code
  • image inventories

Instead of reading all of that manually, you can use AI to:

  • summarize recurring problems
  • map issues to templates and page types
  • suggest code-level fixes
  • generate implementation tickets
  • create monitoring rules
  • compare before/after reports

That saves time, especially if you manage a content-heavy or template-heavy site.

1. Use AI to Prioritize the Right Pages First

The biggest mistake is fixing random URLs. AI is far more useful when you feed it exported data and ask it to rank pages by impact.

A practical workflow:

  • export poor and “needs improvement” URLs from Google Search Console
  • add traffic, conversions, or ranking data from GA4 or your SEO tool
  • ask AI to cluster URLs by template, intent, and likely root cause

This matters because Core Web Vitals usually fail at the template level, not as isolated one-off pages.

A useful prompt:

Group these URLs by page template, identify likely LCP, INP, or CLS patterns, and rank fixes by SEO and revenue impact.

Why this works:

  • AI turns a raw URL list into a fix roadmap
  • you stop wasting time on low-value pages
  • you can align technical SEO and dev work faster

If you already use AI in your content QA process, this fits nicely with the workflow in Stop Publishing AI Content Without These SEO Checks.

2. Use AI to Diagnose LCP Bottlenecks Faster

LCP is still one of the most common problems, and images are often the reason. Google notes that the LCP element is an image on about 80% of web pages (web.dev).

AI is useful when you give it:

  • a PageSpeed Insights result
  • a waterfall screenshot
  • a Chrome DevTools trace
  • the HTML of the above-the-fold section

Then ask it to identify whether the delay is mostly:

  • slow server response
  • late resource discovery
  • oversized images
  • render-blocking CSS
  • JS delaying the LCP render

Google’s LCP guidance breaks the metric into four parts: TTFB, resource load delay, resource load duration, and element render delay (web.dev). That breakdown is exactly the kind of structured problem AI handles well.

Practical AI tasks:

  • detect the likely LCP element
  • suggest preload usage for hero images or fonts
  • recommend AVIF/WebP conversion
  • flag CSS background images that hide the real bottleneck
  • identify when JS is delaying rendering after the image is already downloaded

Tip: Ask AI to explain the bottleneck in plain English for non-developers. That makes it much easier to get buy-in.

3. Use AI to Shrink Image and Media Waste at Scale

This is one of the easiest wins for publishers, blogs, and ecommerce teams.

According to the 2024 Web Almanac, the median mobile page weight was 2,311 KB in October 2024, and mobile homepages served a median 900 KB of images (HTTP Archive, 2024). That is a lot of bytes before you even talk about JavaScript.

AI can help you:

  • audit image filenames, dimensions, and formats
  • spot oversized hero images by template
  • generate image optimization rules
  • recommend responsive breakpoints
  • find decorative images that can be removed or lazy-loaded

Good use cases:

  • create rules like “convert all blog hero images over 1600px wide to AVIF”
  • detect when thumbnails are being loaded at full size
  • identify carousels that force multiple large image downloads

This is also where AI content teams often create technical debt without noticing it. If your editorial process publishes image-heavy pages fast, pair this work with 9 Ways to Use AI for Content Refreshes That Recover Rankings so performance fixes become part of refresh cycles.

4. Use AI to Cut JavaScript and Third-Party Bloat for INP

INP became an official Core Web Vital on March 12, 2024, replacing FID (Google Search Central). That changed the game because INP measures the responsiveness of interactions across the page lifecycle, not just the first input.

The 2024 Web Almanac showed how much harder that made passing CWV on mobile: 48% of sites would have had good CWV with FID, but only 43% did with INP (HTTP Archive, 2024).

AI is especially good at finding INP problems caused by:

  • heavy event handlers
  • layout thrashing
  • long tasks
  • bloated component libraries
  • third-party tags fighting for the main thread

Feed AI:

  • a performance trace
  • your bundle report
  • a list of third-party scripts
  • the code for a slow component

Then ask:

Which scripts or components are most likely causing long tasks or layout thrashing during user interactions, and what should I remove, defer, or move off the main thread?

Google’s official INP guidance recommends starting with field data, then reproducing slow interactions in the lab, then reducing input delay, processing time, and presentation delay (web.dev).

5. Use AI to Catch CLS Risks in Templates and Components

CLS is often less about “speed” and more about sloppy layout behavior.

Google’s CLS guidance says the most common causes are:

  • images without dimensions
  • ads, embeds, and iframes without dimensions
  • dynamically injected content
  • web fonts (web.dev)

AI is helpful because it can scan your templates and component files for repeat offenders. That is much faster than manually reviewing dozens of page types.

Good AI tasks:

  • detect missing width and height attributes
  • find components that inject banners above content
  • flag ad slots without reserved space
  • identify font loading patterns that trigger shifts
  • suggest skeletons or placeholders for async modules

A strong rule here: ask AI to review shared templates first. One fix in a shared component can clean up thousands of URLs.

6. Use AI to Build Better Real-User Monitoring and Alerts

Google is very clear that field data matters. The Chrome User Experience Report powers tools like PageSpeed Insights, DevTools, and Search Console, but Google also recommends setting up your own real-user monitoring for deeper diagnosis (web.dev).

That is where AI becomes operational, not just analytical.

Use AI to help you:

  • write web-vitals instrumentation
  • define alert thresholds by template
  • summarize weekly regressions
  • detect which release probably caused a drop
  • translate raw telemetry into Jira tickets

This is increasingly important because lab and field data often disagree, especially for CLS and INP.

A recent real-world example: Fotocasa used Chrome DevTools plus custom RUM with the web-vitals library to diagnose poor INP, and the improvement work contributed to 27% growth in key metrics (web.dev case study, 2025).

7. Use AI in CI to Prevent Regressions Before They Ship

The best Core Web Vitals strategy is not heroic cleanup after launch. It is blocking obvious regressions before they hit production.

AI can help by generating:

  • Lighthouse CI rules
  • performance budgets
  • pull request summaries
  • “why this change may hurt LCP/INP/CLS” comments
  • fix suggestions when a budget is exceeded

This is useful for SEO because a slow rollout can quietly damage important templates for weeks before Search Console catches up.

A simple setup looks like this:

  • run Lighthouse CI on key templates
  • compare results to budget thresholds
  • send the output to AI
  • let AI summarize the regression and propose likely fixes

This trend is getting stronger. Chrome DevTools now includes AI assistance for performance, letting you ask AI about insights and trace data directly in the Performance panel (Chrome for Developers).

Pros and Cons of Using AI for Core Web Vitals

Pros

  • Faster diagnosis: AI can summarize long reports and traces in minutes.
  • Better prioritization: it helps connect technical issues to business impact.
  • Scalable audits: useful for large sites with many templates.
  • Clearer communication: AI can translate performance issues for SEO, content, and dev teams.
  • Regression prevention: it fits well into CI and monitoring workflows.

Cons

  • It can guess wrong: AI suggestions still need developer review.
  • It depends on input quality: weak prompts and bad data produce weak output.
  • It won’t replace field testing: real-user data is still the source of truth.
  • It may miss framework-specific edge cases: especially in complex JS apps.
  • It can create false confidence: a clean summary is not the same as a verified fix.

Current Trends You Should Know

Three trends matter right now:

  1. INP is now fully central. Since March 12, 2024, responsiveness is measured with INP, not FID (Google Search Central).
  2. Mobile is still the hard mode. Only 48% of mobile sites achieved good CWV in the 2025 Web Almanac data (HTTP Archive, 2025).
  3. AI is moving into native performance tooling. Chrome DevTools now supports AI assistance for performance analysis inside the browser itself (Chrome for Developers).

That makes the near-future workflow pretty clear: real-user data + automated monitoring + AI-assisted debugging.

Practical Tips to Make This Work

  • Start with your top templates, not random pages.
  • Use AI on exports and traces, not vague descriptions.
  • Always separate field data from lab data.
  • Fix image and JavaScript issues before chasing minor score tweaks.
  • Reserve space for ads, embeds, and async modules.
  • Review third-party scripts aggressively.
  • Treat AI output as a draft for engineers, not the final answer.

If your broader AI SEO system still feels messy, it also helps to tighten your content structure and topic ownership first. That is where pieces like How to Build AI Topic Clusters in 14 Days can support the technical work without overlapping it.

Final Thoughts

AI can absolutely help you improve Core Web Vitals, but the real value is not magic automation. It is faster diagnosis, better prioritization, and fewer regressions.

If you use AI to work from real user data, simplify heavy templates, and keep performance checks inside your publishing and deployment process, you will usually get better Core Web Vitals and a cleaner SEO workflow at the same time.

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