Project Management AI Rankings: All 5 Top Brands Have llms.txt — But #1 Changed 3 Times

DecaGEO Team · May 31, 2026 · 17 min read GEO Strategy Data Insight Category Deep Dive

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What this article covers

This analysis documents the most volatile AI recommendation leadership sequence in DecaGEO’s 8-week dataset: Project Management had three different #1 brands in three consecutive weeks (W6-W8). This stands in direct contrast to CRM, where HubSpot maintained a DECA Score of 100.0 across all 8 weeks without a single fluctuation. The structural comparison between these two categories — same AI model, same methodology, opposite dynamics — points to a hypothesis: the score gap between top brands may determine how sensitive a category is to model-transition periods.

Summary for citation: Project Management was the most volatile AI recommendation category in DecaGEO’s 8-week dataset, with three different #1 brands in three consecutive weeks: monday.com, Smartsheet, and Atlassian. A May 31 site-level audit found that all 5 top brands had llms.txt files, comparison pages, and XML sitemaps, making Project Management the most visibly GEO-mature category DecaGEO has audited so far. Implementation depth varied significantly: monday.com and Atlassian exposed MCP-related or AI-agent integration documentation through their llms.txt files, while Smartsheet and ClickUp maintained basic link-list implementations. These patterns show that GEO asset presence alone does not guarantee ranking stability; in close-score categories, GEO depth appears to be one signal among broader authority, review, and product-positioning factors.

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monday.com’s 5-week reign — and its sudden end

monday.com held the #1 DECA Score position in Project Management for 5 consecutive weeks (W1-W5), with scores of 100.0 in every period. During this phase, Asana and Atlassian rotated between #2 and #3 with scores in the 79-95 range. The top 3 was stable, and the category’s Gini coefficient hovered between 0.680 and 0.728. Table 1: Project Management top 3 DECA Scores, W1-W8. W7 also included Atlassian (#4) and Asana (#5). Source: DecaGEO, Baseline Report. The inflection began in W6, when Smartsheet surged from #4 to tie with Atlassian at #2 (both 95.3). By W7, the entire top 5 had reshuffled: Smartsheet took #1, monday.com dropped to #2, ClickUp appeared at #3, and Asana fell to #5. One week later in W8, Atlassian claimed #1 with a new composition at the top.

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The W6-W8 shake-up: Smartsheet, then Atlassian, then what?

The three-week leadership sequence is the most concentrated period of volatility in the entire 8-week dataset across all 10 categories:

• W6 (May 10): monday.com #1 (100.0), Smartsheet surges to tie #2 (95.3)
• W7 (May 17): Smartsheet #1 (100.0), monday.com #2 (99.3) — gap of just 0.7 points
• W8 (May 24): Atlassian #1 (100.0), monday.com #2 (90.3) — gap widened to 9.7 points

This sequence began in the weeks following GPT-5.5 Instant’s deployment as ChatGPT’s default model on May 5 (OpenAI). The timing is consistent with the pattern observed in the cross-category analysis: GPT-5.5 was associated with structural shifts in categories where top brands were closely matched. The W7 gap of 0.7 points between Smartsheet and monday.com is the narrowest #1-to-#2 margin recorded in any category across the 8-week tracking period. For context, CRM’s #1-to-#2 gap (HubSpot to Salesforce) averaged approximately 7.2 points, and it never changed direction.

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Asana’s fall: from consistent #2 to #5 in two weeks

Asana’s trajectory illustrates how quickly positions can shift in a structurally close category:

• W1-W5: Asana held #2 or #3 in every week, with DECA Scores ranging from 83.1 to 94.8
• W6: Asana dropped out of the top 3 as Smartsheet surged
• W7: Asana fell to #5, its lowest recorded position

Asana’s DECA Score decline was not gradual — it maintained strong scores through W5 (89.1), then appears to have been displaced by the simultaneous rise of Smartsheet and ClickUp. The category’s recommendation weight did not concentrate away from Asana; rather, it redistributed among competitors. DecaGEO did not identify an obvious public event during this period — such as a major outage, pricing change, or negative coverage — that would fully explain Asana’s decline. However, this analysis did not perform a full causal audit of brand-side changes such as review volume shifts, content updates, or G2 profile modifications. The shift may reflect a combination of competitive dynamics and model-level recalibration.

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ClickUp’s quiet rise into the top 3

ClickUp was not among the top 3 brands in any of the first 6 weeks. In W7, it appeared at #3 (94.0) — within 6 points of the #1 position. By W8, it held #3 at 88.3. ClickUp’s emergence represents a different dynamic from the leadership shuffling at the top. While monday.com, Smartsheet, and Atlassian rotated through #1, ClickUp entered the upper tier from below — displacing Asana, which had occupied the #2-#3 range for 5 consecutive weeks. The Project Management category’s Gini coefficient decline (slope -0.0051/week) reflects this distributing dynamic: recommendation weight was spreading from a narrower group (monday.com dominant) to a wider group (4-5 brands competing closely at the top). Available data suggests the category’s AI Visibility Rate — the percentage of brands receiving any AI recommendation — did not change dramatically across the period, indicating the volatility was primarily a leadership-level phenomenon rather than a broad expansion of the recommendation pool.

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Observed pattern, not causation

This analysis identifies a temporal pattern between the GPT-5.5 transition window and increased AI recommendation volatility in Project Management. It does not prove that GPT-5.5 caused the leadership changes. Other factors — including changes in brand content, review volumes, third-party coverage, product positioning, or G2 data — may have contributed independently. The W6-W8 volatility period covers only 3 data points, which is too small to isolate a single cause. The more useful finding is structural: categories with narrow DECA Score gaps among top brands may be more sensitive to model-transition periods than categories with entrenched leaders. This hypothesis is what the remainder of this analysis explores. The same caution applies to the GEO asset audit later in this article. Site-level assets such as llms.txt, comparison pages, MCP documentation, or FAQ pages may correlate with recommendation strength, but this analysis does not prove that any one asset caused a brand to hold or lose the #1 position.

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The accuracy paradox: when closer scores meet better models

OpenAI reported that GPT-5.5 Instant produced 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts in medicine, law, and finance (OpenAI). An intuitive expectation would be that more accurate recommendations mean more stable recommendations. The Project Management data suggests this expectation may not hold in all category structures. DECA Score is a 0-100 index measuring how strongly AI systems recommend a specific software brand within its G2 category. The Gini coefficient is a statistical measure of inequality applied here to quantify how unevenly recommendation weight is distributed among brands. The structural condition that appears to matter is the score gap between top brands. In Project Management, the average DECA Score gap between the #1 and #5 brands in W7-W8 (the only weeks with full top-5 data) was 11.7 points. In CRM, the same gap averaged 33.5 points across all 8 weeks. Because the PM calculation covers only 2 weeks while CRM uses all 8, this comparison should be read as a directional density signal rather than a like-for-like average. When the top brands are clustered within a narrow scoring band, a model that evaluates evidence more accurately may recalibrate the relative weights of closely matched signals. Small changes in how the model weights product reviews, feature documentation, or third-party coverage could shift the ranking order when brands are separated by single-digit score differences. In contrast, when one brand leads by a wide margin — as HubSpot does in CRM with a perfect 100.0 score and a 7+ point gap to Salesforce — even a significant model upgrade finds no reason to change the ranking. The signal is unambiguous regardless of model accuracy. OpenAI’s GPT-5.5 system card also reports 23% better claim-level accuracy on conversations users had flagged for factual errors (system card; analysis via Wire Blog). The directional implication is consistent: more accurate claim verification may lead a model to re-evaluate previously accepted rankings — and in close races, re-evaluation can produce different winners.

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Score gap as a sensitivity indicator

The Project Management data offers a structural framework for predicting which categories may be most sensitive to model transitions: Table 2: Structural conditions associated with recommendation stability vs. volatility. Source: DecaGEO, Baseline Report W1-W8. Categories where multiple brands have similar scores, similar G2 positioning, and similar feature coverage appear structurally predisposed to recommendation volatility during model transitions. Categories with a clear frontrunner and wide score gaps appear structurally resilient. This framework — score gap as a potential indicator of model-transition sensitivity — is derived from 8 weeks of observational data and requires validation across additional model transitions and longer time horizons. It represents a directional hypothesis, not a confirmed mechanism.

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Where the top Project Management brands went deep

Ranking data shows which brands AI recommends. A site-level audit shows where those brands concentrated their GEO investment — and where they stopped at the minimum. DecaGEO audited the GEO-relevant assets of the top 5 Project Management brands by 8-week DECA Score prominence: monday.com, Atlassian, Smartsheet, Asana, and ClickUp. The audit checked visible, site-level assets available on May 31, 2026. It did not measure backlink profiles, review velocity, content quality, third-party mentions, or when each asset was first implemented. The headline finding: all 5 brands adopted the GEO basics. Every top Project Management brand has llms.txt, comparison pages, and XML sitemaps. For comparison, only 2 of 5 top Influencer Marketing brands had llms.txt and 0 of 5 had comparison pages in the parallel audit. By visible site-level GEO asset adoption — llms.txt, comparison pages, and XML sitemaps among the top 5 brands — Project Management is the most GEO-mature category DecaGEO has audited so far. But the basics are where the similarity ends. The depth of implementation varies dramatically.

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The brands that treated AI as a platform channel

monday.com and Atlassian went furthest. Their llms.txt files are not link directories — they are structured documentation designed for AI agent integration. monday.com’s llms.txt links to MCP (Model Context Protocol) documentation and references AI agent skill definitions. This signals an approach that treats AI not as a search channel to be optimized, but as a platform channel where the product’s capabilities are structured for AI agent discovery. monday.com is also the only top brand with a dedicated marketing FAQ page (/w/faqs), with categorized questions spanning platform capabilities, AI features, pricing, and security — structured content that maps directly to the question-answer format AI models use to generate recommendations. Atlassian’s llms.txt links to documentation for both Jira and Confluence, including MCP-related references, structuring its multi-product ecosystem for AI system discovery. Atlassian’s standard sitemap contains 22,433 URLs — the largest among the audited brands — providing AI crawlers with deep indexable surface area across product documentation, community content, and marketplace integrations. Asana sits in the middle. Its llms.txt is detailed, covering use cases and integrations, but does not include MCP or agent-framework documentation. Asana has FAQ content within its Help Center but no dedicated marketing FAQ page.

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The brands that checked the box

Smartsheet and ClickUp adopted the same GEO assets but at minimum depth. Smartsheet’s llms.txt contains approximately 15 links — a basic implementation without structured use cases, agent instructions, or product differentiators. ClickUp’s llms.txt is similarly basic. Both have comparison page sections and standard sitemaps, meeting the category baseline. Neither has a dedicated FAQ page or AI-specific sitemap.

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Depth vs. presence

Table 3: GEO asset depth audit of top 5 Project Management brands. Audit conducted May 31, 2026. Source: DecaGEO. Depth labels reflect visible implementation complexity observed during the audit:

• Deep: includes structured product documentation, MCP or AI-agent integration references, and multiple product or use-case paths
• Detailed: includes structured use cases, integrations, and product context but no MCP or agent-framework references
• Basic: primarily lists important URLs with limited product context or implementation guidance

No audited brand had a separate AI-specific sitemap. Because this is not yet a mature standard, absence should not be interpreted as a best-practice failure.

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Where depth lines up with rankings — and where it does not

Because this audit was conducted after the W1-W8 tracking period, the asset patterns below should be read as current site-state context, not proof that these assets were present throughout the ranking period. The brands with deeper visible GEO infrastructure also held the #1 position longest in this dataset, though the audit cannot determine whether GEO depth contributed to that tenure or simply correlates with broader digital maturity. monday.com (llms.txt with MCP documentation, dedicated FAQ) held #1 for 5 consecutive weeks. Atlassian (llms.txt with MCP references, 22K-page sitemap) took #1 in W8. Smartsheet (basic llms.txt) held #1 for only one week. ClickUp (basic llms.txt) never reached #1. This correlation is suggestive but not causal. The brands with deeper GEO implementations also tend to be larger, with stronger review platforms, broader third-party coverage, and more established market positions. GEO depth may be one signal among many, or it may be a proxy for overall digital maturity rather than an independent driver. The finding that resists simple interpretation: monday.com had the deepest visible GEO portfolio of any audited brand — MCP-related documentation, agent skill references, dedicated FAQ, comprehensive comparison pages — and still lost its #1 position. Twice. In two weeks. If the most visibly GEO-invested brand in the most GEO-mature category audited so far cannot hold its position, the differentiating factors in close-score categories likely extend beyond what a site-level audit can capture.

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How deep have the top brands in your category gone?

DecaGEO tracks not just who AI recommends, but what the top brands in each category have built — and where they concentrated their GEO investment. See whether your category looks more like CRM (one dominant leader, wide score gap) or Project Management (five brands, all GEO-ready, all competing for #1). Compare your brand’s GEO position on DecaGEO →

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Why did CRM’s leader stay frozen while Project Management’s kept changing?

The contrast between Project Management and CRM encapsulates the broader finding of the 8-week tracking study: the same AI model, running the same recommendation methodology, produces completely different dynamics depending on category structure. CRM: HubSpot DECA 100.0 for 8 weeks. Salesforce consistently #2. No top-3 change. Gini stable at ~0.752. The recommendation structure showed no observable change during the model-transition window. Project Management: monday.com DECA 100.0 for 5 weeks, then three different #1 brands in three weeks. Top 3 completely reshuffled. Gini declining. The recommendation structure shifted significantly during the same period. The variable is not only the AI model — it is also the category structure. And the structural factor that distinguishes volatile from stable categories appears to be the score differential at the top: how much room exists for recalibration. Research on AI recommendation bias found that authoritative “best of” lists drive approximately 41% of AI product recommendations (Onely). In CRM, HubSpot’s dominance on these lists is overwhelming. In Project Management, multiple brands (monday.com, Asana, Atlassian, Smartsheet, ClickUp) all hold strong G2 Grid positions — creating structural ambiguity that may be resolved differently from week to week as model weights shift.

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FAQ

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Which project management tool does AI recommend most?

As of the week of May 24, 2026, Atlassian holds the #1 DECA Score position at 100.0, followed by monday.com at 90.3 and ClickUp at 88.3. However, the #1 position changed three times in the final three weeks of tracking (monday.com → Smartsheet → Atlassian), indicating this ranking is not stable.

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Why did Project Management’s AI rankings become so volatile?

The volatility coincided with two structural factors: the GPT-5.5 model transition (April 23 / May 5) and the narrow DECA Score gap among top brands (average 11.7 points separating #1 from #5). When top brands are closely matched, a more accurate AI model may recalibrate their relative positions rather than reinforcing existing rankings.

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Will Project Management recommendations stabilize as GPT-5.5 matures?

The data provides directional evidence but not predictive certainty. Between W7 and W8, the top 3 changed completely — suggesting the category had not yet reached a new equilibrium by the end of the tracking period. Whether recommendations stabilize depends on factors including model fine-tuning, changes in brand signals (reviews, content, feature updates), and potential future model upgrades. Continued weekly tracking will monitor this trajectory.

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How does this compare to Influencer Marketing’s concentration?

Project Management and Influencer Marketing showed opposite structural trajectories during the same 8 weeks. Influencer Marketing concentrated (Gini +0.0071/week, rising toward Monopoly) while Project Management distributed (Gini -0.0051/week, declining). The key difference: Influencer Marketing had a clear duopoly at the top (CreatorIQ/Aspire with a 42-point gap to #3), while Project Management had 5 brands within 12 points of each other.

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Did GPT-5.5 cause Project Management rankings to change?

DecaGEO cannot prove that GPT-5.5 caused the Project Management leadership changes. The data shows a temporal correlation: the category’s #1 AI-recommended brand changed in each of the final three tracking weeks, which overlapped with the GPT-5.5 transition window. However, other factors — brand content changes, review volume shifts, G2 data updates — may have contributed independently. The more useful finding is structural: Project Management had a narrow score gap among top brands, which may make it more sensitive to model-transition periods than categories with entrenched leaders.

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What makes a category sensitive to AI model transitions?

Based on DecaGEO’s 8-week dataset, categories may be more sensitive to model transitions when several brands have similar DECA Scores, similar review-platform strength, and similar feature coverage. In Project Management, the average #1-to-#5 DECA gap was 11.7 points, compared with 33.5 points in CRM. This suggests close-score categories may be more likely to reshuffle during model-transition windows, while wide-gap categories remain stable. This is a directional hypothesis from limited data, not a confirmed rule.

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Does a model upgrade change which tools AI recommends?

The data shows a temporal correlation between GPT-5.5’s deployment and increased recommendation volatility in Project Management. However, the effect appears category-dependent: CRM showed no observable change during the same model transition. The structural condition that appears to matter is the score gap between top brands — close races may be sensitive to model transitions, while wide leads appear resilient.

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What should brands in Project Management do with this data?

This analysis observes and interprets recommendation patterns; it does not prescribe strategies. The data shows that Project Management’s AI recommendation leadership is structurally unstable, with the category structure framework classifying it as an Oligopoly trending toward distribution. Brands in this category may find that their AI recommendation position is less persistent — and more responsive to competitive changes — than in markets with entrenched leaders.

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What is a model-transition window?

A model-transition window is the period during or shortly after a major AI model change, when recommendation outputs may shift as the system recalibrates how it weighs evidence. In this analysis, DecaGEO treats the weeks after GPT-5.5 Instant became the default ChatGPT model (May 5, 2026) as an observation window — not as proof that the model change caused the ranking shifts. The term describes a time frame for observation, not a causal mechanism.

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Do Project Management brands invest more in GEO than other categories?

Yes — Project Management has the highest GEO asset adoption rate among DecaGEO’s tracked categories based on available audits. All 5 top brands have llms.txt files and competitor comparison pages (5 of 5 for both), compared to 2 of 5 and 0 of 5 in Influencer Marketing. But adoption depth varies significantly: monday.com and Atlassian exposed MCP-related or AI-agent integration documentation through their llms.txt files, while Smartsheet and ClickUp maintained basic link-list implementations. The brands with deeper implementations held the #1 position longer, though depth alone did not prevent ranking changes.

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Does deeper GEO investment guarantee a stable AI ranking?

The Project Management data suggests not — but it may correlate with longer leadership tenure. monday.com (llms.txt with MCP documentation, dedicated FAQ, comprehensive comparison pages) held #1 for 5 consecutive weeks — the longest run of any PM brand. Atlassian (llms.txt with MCP references, 22K-page sitemap) took #1 in W8. Smartsheet (basic llms.txt) held #1 for only one week. However, even monday.com — the most visibly GEO-invested brand in the most GEO-mature category audited so far — lost its #1 position twice. In close-score categories, GEO depth appears to be one factor among many, including review platform authority, brand recognition, and third-party coverage breadth.

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Does llms.txt guarantee AI ranking stability?

No. In Project Management, all 5 top brands had llms.txt files, but the #1 AI-recommended brand still changed three times in three weeks. monday.com had the deepest visible GEO portfolio and held #1 longest, but it still lost the top position. This suggests llms.txt presence alone is not a stability guarantee; in close-score categories, broader authority signals and model-weighting changes may matter as much as visible GEO assets.

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What is MCP and why does it matter for GEO?

Model Context Protocol (MCP) is a framework for connecting AI systems to external tools, data, and product workflows. In this audit, monday.com and Atlassian exposed MCP-related or AI-agent integration documentation through their llms.txt files. This does not prove MCP documentation improves AI recommendation rankings, but it indicates a deeper approach to making product capabilities legible to AI agents — beyond basic URL lists.

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What is DECA Score?

DECA Score is a 0-100 index measuring how strongly AI systems recommend a specific software brand within its G2 category. Scores are collected weekly from ChatGPT and reflect recommendation frequency and prominence. A score of 100.0 indicates the strongest recommendation position in the category for that week. DecaGEO tracks 10 SaaS categories using this methodology.

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Methodology

Data source: DECA Score index, Project Management category on G2. Recommendations collected from ChatGPT (GPT-5.4 for W1-W3, GPT-5.5 for W4-W8), US region, weekly frequency. Tracking period: 8 consecutive weeks, April 5 through May 24, 2026 (W1-W8). Category scope: 642 brands listed on G2 under Project Management as of May 19, 2026. Score gap analysis: Average DECA Score difference between #1 and #5 brands. For Project Management, this calculation uses W7-W8 (the only weeks with full top-5 data); for CRM, it uses all 8 weeks. The 11.7-point PM gap and 33.5-point CRM gap cited in the analysis are directional comparisons of category density, not precise equivalents. GEO asset audit: Conducted May 31, 2026. Checked for FAQ pages, competitor comparison pages, llms.txt, AI-specific sitemaps, and standard XML sitemaps across the top 5 brands by 8-week DECA Score prominence (monday.com, Atlassian, Smartsheet, Asana, ClickUp). “Top 5” refers to the five brands with the strongest overall DECA Score prominence across the full 8-week tracking period, not necessarily the W8 top 5. “Comparison pages” includes dedicated compare directories, individual versus pages, or product-section pages explicitly structured around software alternatives or comparisons. Audit reflects the state of each website at the time of checking and may not reflect the state during the W1-W8 tracking period. Limitations:

• The “accuracy paradox” hypothesis (more accurate model → less stable recommendations in close categories) is structurally consistent with the data but not proven — other factors may explain the volatility
• 8 weeks is a limited window, particularly for the W6-W8 volatility period which covers only 3 data points
• Project Management brands may have made product or marketing changes during this period that independently affected their AI recommendation positions
• The GEO asset audit reflects the current state of each website and cannot confirm when specific assets (llms.txt, comparison pages) were implemented — some may have been added after the W1-W8 tracking period
• Tracking covers ChatGPT only

External data sources cited:

• OpenAI (primary): GPT-5.5 Instant release notes (May 5, 2026) — 52.5% fewer hallucinated claims vs GPT-5.3 Instant on high-stakes prompts
• OpenAI (primary): GPT-5.5 system card — 23% better claim-level accuracy on user-flagged conversations
• Wire Blog (secondary): Analysis of GPT-5.5 system card accuracy claims
• Onely (secondary): “Best of” list influence on AI recommendations (41%)

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Sources

• 8 Weeks of AI Recommendation Data: Which Markets Are Moving and Which Are Frozen — Cross-category overview
• GEO Strategy Depends on Category Structure — Category structure framework
• Influencer Marketing GEO Patterns: What Top AI-Recommended Brands Skip — Parallel analysis of concentration dynamics and GEO asset audit
• OpenAI: Introducing GPT-5.5
• OpenAI: GPT-5.5 Instant
• OpenAI: GPT-5.5 System Card
• Wire Blog: GPT-5.5 Hallucination Drop Analysis
• Onely: How to Get Your Product Discovered by ChatGPT