Generative AI tools have reshaped how people search, decide, and discover information online. But a new study from SparkToro, conducted with Gumshoe.ai, reveals a striking feature of these systems: AI recommendations change with almost every query execution, even when the prompt doesn’t change. These findings have broad implications for how individuals, brands, and organizations interpret AI responses, make decisions, and measure visibility in an era where search and AI are increasingly blurred.
This article explains the research, outlines its significance for finance and banking trends, and offers practical insights on how users and organizations should adjust expectations and strategies for AI-powered recommendations.
The SparkToro Study: What It Found
Researchers Rand Fishkin (SparkToro) and Patrick O’Donnell (Gumshoe.ai) explored whether generative AI systems produce consistent recommendation lists when given the same query repeatedly. They ran 2,961 prompts across three major AI tools:
- ChatGPT
- Claude
- Google’s AI in Search (AI Overviews or AI Mode when applicable)
Each prompt requested brand or product recommendations in domains like chef’s knives, headphones, cancer care hospitals, and digital marketing consultants. Crucially, they repeated the same prompt 60 – 100 times per platform to see how often the output — specifically the recommendation list — repeated.
Key Findings
- The same list of brands rarely appeared twice — less than 1% chance for identical item lists across runs.
- The order of recommendations was almost never consistent.
- Even the number of items in each list varied widely.
- In tight categories with few major players, the same core brands tended to appear, but in different sequences.
Researchers described AI recommendation lists as essentially unpredictable — a by-product of how large language models (LLMs) generate outputs probabilistically rather than deterministically. Repeatability wasn’t just low; it was statistically negligible.
Why Recommendations Vary: The Technical Reality
AI systems like ChatGPT, Claude, and Google’s generative search components are not designed to generate a fixed, deterministic ranking of items. Instead, they use probabilistic sampling mechanisms that:
- Weight likely continuations of text
- Consider multiple possible tokens at each step
- Adjust outputs based on internal randomness or temperature settings
This probabilistic nature means that each run of essentially the same prompt can pull from subtly different paths through the model’s internal reasoning. Even tiny variations in sampling strategies or contextual embeddings can yield different results.
In simpler terms, these models are built to generate likely and varied outputs rather than identical lists of recommendations on each run.
Implications for Users and Decision-Making
1. AI Lists Are Not Stable Rankings
The study directly challenges the idea that AI can provide a definitive ranked list of brands or products. Users should be cautious about taking such lists as fixed or authoritative. Less than a 1% chance of repetition suggests that AI is more about suggestive context than consistent evaluation.
This matters in domains where consistency and reliability are fundamental. For instance:
- Healthcare recommendations (e.g., top cancer care hospitals)
- Investment platform comparisons
- Credit or loan product lists
- Insurance recommendations
In these areas, variation in output might lead to misaligned decisions if users assume the results reflect stable market consensus.
2. Core Intent Still Drives Outcomes
Interestingly, while exact lists varied widely, the underlying intent was often captured — especially when prompts were focused. In the headphone example, even wildly different human-written prompts often surfaced familiar brands like Bose, Sony, Sennheiser, and Apple in a majority of responses.
This suggests that AI models understand the semantic intent of a query, even if the specific recommendation order or list length shifts with each run.
For financial content or brand positioning, this means:
- Consistent entities may emerge across queries centered on the same core need
- Familiar brands or widely cited options are more likely to be recommended often
- But precise ranking is unreliable and not a stable metric
Why This Matters in Finance and Banking
The findings are especially relevant in the finance and banking sectors, where recommendations influence decisions on:
- Investment products
- Insurance providers
- Retirement planning tools
- Credit cards or loans
- Financial advisory services
Three implications stand out:
1. AI Recommendations Are Probability-Based, Not Signal-Driven
Unlike traditional search rankings, which are tied to indexing, links, and relevance signals, AI recommendations reflect the likelihood of word associations. That means:
- Recommendation lists may reflect data popularity rather than objective quality
- Brand mentions may be influenced by model training distribution, not real-world performance
For example, a bank’s loan product might appear frequently not because it’s objectively superior, but because it appears more often in the model’s training data.
This bias toward frequency affects how brands are perceived when consumers ask about “best” financial services — even if those options aren’t the best in every context.
2. Tracking and Visibility Metrics Need Reframing
Traditional SEO relies on stable ranking positions (e.g., rank #1, rank #2). SparkToro’s research suggests such position tracking is ineffective for AI metrics. Since AI outputs vary dramatically, ranking for a term doesn’t guarantee a predictable position in AI recommendations.
Instead, brands and financial services should measure:
- Appearance frequency across many runs
- Presence in core consideration sets rather than specific order
- Citations across platforms and contextual relevance
This shift moves visibility strategies away from fixed rank targeting toward probabilistic visibility — i.e., how often a brand appears in the universe of AI responses.
3. Prompt Variation Reflects Real-World Use Patterns
The study also examined how real users write prompts. When 142 participants wrote their own prompts about headphones, the semantic similarity score was only 0.081 — a measure showing that even queries with the same intent can be phrased drastically differently.
This mirrors real-world usage: users rarely phrase queries in the exact same way. For finance and banking, where queries could range from “best retirement funds for 50-year-olds” to “top performing pensions with low fees”, these variations compound inconsistency in AI responses.
It underscores that:
- AI recommendations vary not just because models generate them differently each time
- But also because user query formulation differs drastically across individuals
- Yet core intent can still produce recognizable patterns across varied phrasing
How to Interpret AI Recommendations Wisely
Given these characteristics, both users and businesses should shift how they treat AI outputs:
For Users: Evidence-Oriented Decision Making
- Look at multiple AI runs before drawing conclusions
- Avoid assuming a single list reflects an objective ranking
- Combine AI recommendations with traditional research and verified data
- Use AI as suggestive support, not authoritative ranking
For high-stakes decisions — such as choosing financial products or health services — this layered approach is critical.
For Brands and Marketers
- Focus on being part of the consistent consideration set rather than chasing rank positions
- Track frequency of brand mentions across multiple AI runs and prompt variations
- Optimize content for relevance to core user intents rather than specific keywords
Measuring brand visibility in AI contexts requires new tools, larger datasets, and repeated sampling to capture meaningful patterns rather than single snapshots.
FAQs: AI Recommendation Variability
Q1: Why do AI tools give different results for the same prompt?
AI models are probabilistic — they generate responses by sampling likely token sequences. Even with identical input, slight variations in sampling produce different lists and orders.
Q2: Does this mean AI isn’t reliable for recommendations?
Not exactly. AI still captures underlying intent and frequently mentions core entities within a topic. But exact lists and rankings aren’t stable, so repeated querying and cross-validation are advised.
Q3: Should companies track AI visibility?
Yes, but traditional ranking metrics (like position #1) are not meaningful. Instead, track how often a brand or entity appears across many prompt runs and variations.
Q4: Does recommendation variation apply to all AI tools?
The SparkToro study found high variability across major tools, including ChatGPT, Claude, and Google’s AI search features, suggesting this is a widespread phenomenon.
Q5: How should financial services adapt?
Financial brands should combine AI citation visibility with authoritative content, data accuracy, and domain expertise to ensure credibility when AI recommendations surface their services. Targeting core user intents rather than specific phrases will improve consistency in appearing across varied prompts.
Conclusion: Rethinking AI Recommendations in 2026
The SparkToro research makes clear that AI recommendations aren’t stable rankings — they are dynamic and probabilistic outputs. Each run of the same query often yields a unique answer list with different brands, order, and even list length.
For individuals, this reinforces the idea that AI should supplement — not replace — comprehensive research and critical assessment. For brands and financial institutions, it underscores a shift away from rigid ranking metrics toward probability-based visibility strategies that recognize consistency across multiple AI responses.
In an era where generative AI interfaces are increasingly part of search discovery and decision support, understanding this variability is essential to making informed choices and to building brand strategies that resonate across diverse user interactions.
As a trusted web development company in India, we deliver secure, scalable, and high-performing web solutions. If you’re looking for reliable web development services in India, contact us today to start building your digital success.
