Imagine asking an AI the same question twice and getting two different answers. Now imagine asking it ten times and getting ten different recommendations. That is exactly what recent SparkToro research explored. The results were surprising. And they tell us a lot about how artificial intelligence really works.
TLDR: SparkToro found that AI recommendations can change almost every time you ask the same question. Even small wording tweaks lead to different results. This shows that AI personalization is fluid, not fixed. For marketers and users, it means visibility depends on context, behavior, and constant variation.
What SparkToro Studied
SparkToro wanted to understand how stable AI recommendations really are. Many people assume AI tools are consistent. Ask a question. Get an answer. Repeat. Same output.
But that is not what happened.
Researchers asked popular AI tools for recommendations. Things like:
- Best marketing podcasts
- Top SEO software
- Great email newsletters
They repeated the same queries multiple times. Sometimes back to back. Sometimes slightly reworded. Sometimes from different accounts.
The results shifted constantly.
Some brands appeared once and never again. Others moved up and down the list. Occasionally, entirely new names appeared from nowhere.
This was not random noise. It was a pattern.
Why AI Recommendations Change So Often
To understand this, we need to understand how modern AI works.
AI models like ChatGPT or Gemini do not store a fixed ranking list. They do not hold a stable “Top 10 SEO Tools” document somewhere.
Instead, they predict words.
They calculate probability. Word by word. Sentence by sentence.
That means every answer is generated in real time. Even if two prompts look identical, subtle internal variations can change the output.
Here are a few reasons this happens:
- Probability sampling: Many AI systems intentionally introduce slight randomness. This avoids repetitive answers.
- Context windows: Previous prompts affect future ones. Even earlier questions in the same session matter.
- Model updates: AI systems are updated over time. Small tweaks change outputs.
- Personal signals: Location, usage history, and behavioral data may influence responses.
So when SparkToro repeated queries, they were not retrieving stored lists. They were generating new ones each time.
This Is Personalization in Action
At first glance, changing answers feel unstable. Almost unreliable.
But there is another way to look at it.
This is personalization happening in real time.
Traditional search engines use rankings. They crawl pages. They index them. They sort them using fixed algorithms.
AI works differently.
It blends training data, probabilities, context, and interaction history. It creates a fresh answer for you.
Not for everyone.
For you.
That means two people asking the same question may see different recommendations. Even seconds apart.
This is not a bug. It is a feature.
What the Research Reveals About Visibility
For marketers, this research matters a lot.
Why?
Because it means AI visibility is not fixed.
In traditional SEO, you fight for position one. Once you get there, you defend it.
With AI recommendations, there may not be a permanent position one.
You might appear today. Disappear tomorrow. Return next week.
SparkToro’s findings highlight something important:
- AI mentions fluctuate.
- Brand inclusion varies.
- Exposure is dynamic.
- Consistency is not guaranteed.
This makes tracking harder. But it also creates opportunity.
If recommendations constantly rotate, more brands have a chance to show up.
Small Prompt Changes, Big Output Changes
One of the most fascinating discoveries was this:
Tiny wording changes dramatically altered results.
For example:
- “Best SEO tools”
- “Top SEO platforms”
- “Most popular SEO software”
These sound almost identical.
But AI may interpret them slightly differently.
One phrase might emphasize popularity. Another quality. Another affordability.
The outcome shifts.
This tells us something powerful.
Language precision matters more than ever.
In an AI-driven world, wording is strategy.
Is AI Truly Personalizing, or Just Being Random?
It is fair to ask: is this personalization or randomness?
The answer is somewhere in between.
AI systems often use controlled randomness. This is called temperature sampling. Higher temperature means more variety. Lower temperature means more predictable outputs.
Some platforms prioritize stable results. Others favor diversity.
But personalization also plays a role.
Factors that may influence responses include:
- Your search history
- Your location
- Your device
- Your previous interactions
- Your account settings
The tricky part?
Most users cannot see these layers.
That makes AI feel magical. Or mysterious. Or unpredictable.
What This Means for Brands and Creators
If AI answers change often, what should brands do?
Here are practical takeaways.
1. Build Broad Authority
Do not optimize for one exact keyword phrase. Cover topics deeply. Answer related questions. Create comprehensive resources.
The broader your footprint, the more likely AI will notice you.
2. Be Mentioned in Trusted Sources
AI models learn from large datasets. That includes articles, reviews, forums, and expert content.
The more your brand is discussed in credible places, the better.
3. Encourage Real Conversations
AI pulls from authentic discussions. Podcasts. Social media. Communities. Reviews.
Visibility is no longer just about web pages. It is about conversations.
4. Accept Fluctuation
You will not appear in every answer. No one will.
Monitor trends. Not single outputs.
A Shift From Rankings to Probability
For decades, digital marketing has revolved around rankings. Position one. Page one. Top three.
AI changes that.
Now the game is probability.
What is the probability your brand appears in a generated answer?
That probability depends on:
- Training data presence
- Context of the prompt
- Semantic relevance
- User signals
- Randomization factors
This is a more fluid environment.
Less rigid. More dynamic.
What It Reveals About the Future of Search
SparkToro’s research is not just about AI tools today. It hints at the future.
Search is becoming conversational.
Answers are becoming synthesized.
Results are becoming personalized in subtle ways.
Instead of ten blue links, users get curated summaries.
Instead of fixed lists, they get generated guidance.
This changes user behavior.
It also changes power dynamics.
Brands cannot rely solely on traditional search engine optimization.
They must think in layers:
- Search visibility
- AI inclusion
- Brand authority
- Audience trust
The Human Angle
There is also something very human about these findings.
Real people do not give identical recommendations every time either.
Ask a friend for podcast suggestions today. Then ask again next month.
The list might change.
It depends on mood. Memory. Context.
AI behaves similarly.
It reflects patterns, not static databases.
That makes it flexible. But also less predictable.
Final Thoughts
SparkToro’s research shows something simple yet profound.
AI recommendations are not carved in stone.
They are generated on the fly. Influenced by wording. Context. Probability. Personal signals.
This reveals a new kind of personalization.
One that is dynamic.
One that shifts constantly.
For users, it means answers may vary.
For brands, it means visibility is fluid.
The goal is no longer to win a single ranking.
The goal is to become part of the broader conversation AI draws from.
Because in a world where every query creates a slightly different reality, the brands that show up consistently across variations will win.
Not by controlling the algorithm.
But by being impossible to ignore.
