5 practical SEO experiments with AI as a co-pilot

User intent is constantly evolving, just like our technological habits.

With the rise of artificial intelligence, the ways people search for and discover information are rapidly diversifying.

Naturally, the way we think about SEO is changing too.

But this is not a pro-AI manifesto.

Instead, I want to explore how AI can be a collaborator — not a substitute for human expertise — acting as a co-pilot to make SEO workflows more efficient and adaptable in an increasingly complex landscape.

Personally, I see AI as a telescope, not the North Star: it helps you see farther and move faster, but you’re still the one responsible for choosing the path.

With this mindset, I'll detail a series of practical SEO experiments with minimal barriers to entry, where AI acts as a generative co-pilot.

You don’t need armies of people, huge budgets, or risky tests — just focused, useful ways to achieve real results.

5 SEO experiments where AI acts as a "co-pilot"

SEO has always been about waiting for results, even for basic actions.

You publish content? You wait. Add internal links? You wait. Optimize speed? You still wait. Manually test a hypothesis? It could be a week before you see anything concrete.

So what changes when you bring AI into the picture?

Obviously, you still have to wait for performance — but here’s the difference: AI helps you ask smarter questions and structure your experiments to better anticipate results, giving you a better shot at making good, faster decisions.

With AI as an analytical partner, you can scale and accelerate your SEO work in a more proactive and detailed way, exactly in line with the latest industry trends where human–AI synergy becomes best practice. Practical SEO Experiments: AI as Co-Pilot - SEOteric Digital Marketing

If that sounds good — let’s dive in!

1. Validating ideas before wasting dev time

Time and budgets are limited.

That’s why validating hypotheses meant to improve user experience and SEO performance before involving stakeholders or the dev team is always a wise move. Nobody wants to invest in something that won't deliver.

To avoid bogging down developers prematurely, I ran an A/B testing process with Claude 3.5 Sonnet, which offers:

• advanced reasoning
• structured output
• extended memory

I compared the existing navigation bar with a new version I considered more efficient, integrating relevant site, product, and content data.

Claude analyzed both versions, highlighting strengths, weaknesses, and the estimated impact on engagement and conversions — even before a single hour of development was booked.

Disclosure: I work at Designmodo, the company involved in the experiment.

Claude also generated additional recommendations, helping me refine the proposal before handing it over to the team.

After implementation, I saw a significant increase in engagement and conversion rate — validating the choice made.

2. Content optimization experiment

We all have pages or sites we trust. They're high-quality, serve user intent, and maybe even performed well in the past. But sometimes, they stop delivering.

Why? User intent may shift, competitors offer better formats (lists, tables, comparisons), or a new algorithm changes the game.

Traditionally, I would manually audit the content and SERPs, then adjust based on ranking.

This time, I let AI assist. I used Gemini and Deep Research to:

• analyze the top pages for the target keyword
• predict content formats with the highest chance of success
• identify factors influencing performance
• discover patterns worth replicating

The filtered data was shared with the content team.

With AI, I could expand and refine the analysis, making the final article more readable, better structured, and more aligned with user expectations.

Impressions increased within two weeks, and after two months, some of the updated content even appeared in AI Overviews.

Could we do this analysis manually? Sure, but with AI as the pilot, reverse engineering becomes much faster and more targeted. Industry case studies show such optimizations can lead to spectacular traffic increases, with one example being a +2,300% monthly traffic boost from AI recommendations for brands that tested and systematically iterated these approaches. AI SEO Case Study: 2,300% Monthly AI Traffic Increase By ...

3. Experimenting with new page indexing speed

Understanding how fast different platforms index or display content is essential for prioritizing pages that need attention.

I ran an experiment comparing how quickly traditional engines and generative AI platforms discover new content. For details on why your site might not appear in Google, see Site-ul tău nu apare în Google? Află ce lipsește și cum rezolvi.

On a test site, I published 10 types of pages simultaneously, without submitting them manually for indexing.

Meanwhile, I shared the links on various social media platforms.

• Bing indexed first (38 minutes).
• ChatGPT started answering with 2 of the pages after about 2 hours.
• Perplexity showed 6 pages within 3 hours.
• Google Search Console showed 8 indexed pages after 6 hours, but the site: operator only returned 5.
• By the next day, all 10 were visible in Google’s results.

For the second phase, I used ChatGPT’s Reason feature to analyze which structures and page types yielded faster results, prioritizing fixes for the slower ones.

The fact that AI can correlate structure, page type, and cross-platform visibility in a rapid workflow is a major advantage for indexing strategy. Recent stats show that over 47% of SEO specialists already use AI to increase indexing efficiency and prioritization.

The result? Massive time saved and smarter prioritization.

4. Experiments for crawlability and prioritization

Sometimes, a high-potential page underperforms simply because it isn’t accessible to crawlers.

Manual log analysis is tedious and error-prone, especially at scale.

That’s why I used ChatGPT's Advanced Data Analysis as a pilot to scan for patterns and quickly identify issues like:

• orphan pages (no bot visits)
• pages consuming excessive crawl budget
• loading delays
• unusual fluctuations in crawling activity

I quickly got a prioritized list of issues and could channel the time saved straight into fixing them — exactly what a "co-pilot" should deliver.

5. The content velocity index experiment

Everyone knows that publishing fresh content helps maintain visibility.

But how often do competitors publish? I wanted to find out if their higher content velocity was affecting our performance.

I used Gemini to scrape & summarize the main competitors’ blog publishing dates.

Then I calculated publishing frequency by topic and compared it to our own history.

Gemini gave a clear benchmark for content velocity, helping to identify real gaps and reallocate internal resources.

Final perspectives & takeaways

The above experiments showed that AI-powered platforms amplify the speed, personalization, and refinement of the SEO process — but only when combined with human experience and critical decision-making.

Current studies and data show that over 80% of consumers use AI-generated results for about 40% of searches, and over 52% of marketers make their workflow more efficient with AI, doubling their speed and reaction to new trends. 30+ AI SEO Statistics You Should Know in 2025 But risks remain: AI can miss nuances, amplify errors, or generate content without depth or contextual relevance.

AI should be treated as a catalyst for innovation and analysis, not a substitute for strategy or professional human intuition. Best practices still apply: constant auditing, hypothesis-driven experimentation, and cross-functional collaboration for scalable, sustainable results. Practical SEO Experiments: AI as Co-Pilot - SEOteric Digital Marketing

Use AI with these principles in mind — carefully, experimentally, paying attention to possible errors — and you’ll maximize your chances for superior results.