TL;DR — AI has already formed a SWOT-like assessment of your brand based on thousands of sources, and you need to know what it says before competitors use it against you. Go to Strategy > Brand Perception to see the positive and negative attribute clusters AI assigns to your brand, then verify each one in Visibility > Responses by reading the actual framing language. Check Insights > Sentiment Analysis filtered by topic to quantify whether each attribute is helping or hurting you. Pro tip: cross-reference AI-identified weaknesses with your search intelligence data — if the same weakness shows up in both AI responses and organic search queries, it is a strategically critical issue to address.
The Question
“What are the strengths and weaknesses AI associates with my brand?”AI models do not just mention your brand — they evaluate it. When someone asks “is [Brand] a good choice for X?”, the AI draws on its absorbed understanding of your brand’s reputation to produce a recommendation or comparison. That recommendation reflects an implicit SWOT: AI has formed opinions about what your brand does well and where it falls short. Unlike a customer survey or NPS score, AI brand perception aggregates signals from thousands of sources — reviews, analyst reports, community discussions, press coverage, and your own content. The result is a composite assessment that may contain both accurate observations and outdated or inaccurate ones. Understanding what AI considers your strengths and weaknesses gives you both a reputation audit and an optimization roadmap. You might also be wondering:
- “Is AI accurately representing our product’s real strengths, or amplifying outdated praise?”
- “Which competitor strengths is AI citing that I can realistically challenge?”
- “What weakness does AI associate with my brand that I can directly address with content?”
Where to Go in Qwairy
Start here: Strategy > Brand Perception
Navigate to Strategy > Brand Perception — this is where structured strength and weakness signals are extracted from AI responses.
Review the Theme extraction panel with attention to both positive attribute clusters (what AI says you do well) and negative or cautionary clusters (what AI flags as limitations, risks, or weaknesses). The attribute strength scores show how firmly each signal is embedded.
Read the evidence: Visibility > Responses
Cross-reference with Visibility > Responses to read the verbatim text supporting each attribute.
For each strength or weakness identified in Brand Perception, find 3–5 responses that contain that attribute. Read how AI contextualizes it — a “weakness” mentioned briefly is very different from one that anchors the entire response.
Quantify the tone: Insights > Sentiment Analysis
Open Insights > Sentiment Analysis and filter by the topic or tag associated with each strength or weakness.
This lets you move from “AI mentions X about my brand” to “AI talks about X positively or negatively”. A strength that is framed with caveats may not be functioning as an asset in AI recommendations.
Connect to actions: MCP Integration
If your team uses the Qwairy MCP integration, connect it to your content workflow tools.
Use the brand perception data as input to your content calendar: weaknesses that AI currently associates with your brand become content gap targets; strengths become pillars to amplify and protect.
What to Look For
Brand Perception — Strength and Weakness Extraction
The Brand Perception view extracts attribute clusters from all collected responses and scores their prominence. Positive-valence attributes represent perceived strengths; negative-valence or cautionary attributes represent perceived weaknesses.| Element | What it tells you |
|---|---|
| High-strength positive attributes | What AI consistently praises — your de facto AI reputation strengths |
| High-strength negative attributes | What AI consistently flags — your de facto AI reputation weaknesses |
| Low-strength positive attributes | Strengths you claim that AI has not absorbed — content gaps |
| Absence of expected strengths | Positioning pillars missing entirely from AI descriptions — major gaps |
| Comparative framing | Whether strengths and weaknesses are stated absolutely or relative to competitors |
Responses — Verbatim Attribute Evidence
Reading the actual response text for each identified attribute is essential. Brand Perception gives you the signal; Responses let you understand how it is being communicated to end users.| Element | What it tells you |
|---|---|
| Strength framing | ”Industry-leading X” vs. “offers decent X” — both are positive but very different in impact |
| Weakness framing | ”Lacks Y” vs. “Y is limited compared to competitors” — the latter implies a comparison you may want to contest |
| Qualifier language | Phrases like “reportedly”, “some users say”, or “has been criticized for” signal uncertain or contested claims |
Pro Tip: Cross-reference AI-identified weaknesses with your Insights > Query Fan-Out data. If the same weakness appears in AI responses AND in the organic search queries driving traffic to competitor comparison pages, it is a strategically critical issue — users are searching for it and AI is reinforcing it.
Filters That Help
| Filter | How to use it for this question |
|---|---|
| Topic / Tag | Isolate strength/weakness analysis to a specific product area, use case, or market segment |
| Provider | Some providers consistently surface different weaknesses — identifying provider-specific weakness signals helps you prioritize which audience is receiving the most damaging narrative |
| Period | Measure whether a weakness is persistent or improving — a weakness that appeared 6 months ago but is fading is less urgent than one that is strengthening |
How to Interpret the Results
Good result
Your top 3–4 strengths in Brand Perception match the attributes you invest in and want to own. Each strength scores above 60 and appears in the majority of relevant responses. Identified weaknesses are either: (a) not material to your target buyers, (b) historically accurate but already addressed by your product, or (c) minor and losing strength over time. No competitor is specifically cited as stronger in the areas where you claim leadership.Needs attention
A core product strength that your team knows is real (confirmed by customer feedback, case studies, analyst reports) is not appearing in AI’s strength extraction. This means the content supporting that strength is not being retrieved by AI models. A weakness that was accurate 2+ years ago continues to dominate responses even after the underlying issue was resolved — outdated content or reviews are still being retrieved. A weakness is framed in comparative terms where a specific competitor is named as the superior option.Example
Scenario: An event management platform wants to understand what AI says their product does well and poorly before heading into their annual planning cycle and partner pitch season.
- Open Strategy > Brand Perception with no filters applied. Review the attribute list sorted by strength score. Top positive attributes: “event registration” (score: 84), “attendee analytics” (score: 71), “ticketing flexibility” (score: 66). Top negative attributes: “virtual event experience” (score: 63), “venue management features” (score: 52), “customer support responsiveness” (score: 45).
- Open Visibility > Responses and search for responses mentioning “virtual events”. Read 10 responses. Seven of them contain language such as “the platform’s virtual capabilities trail behind dedicated virtual event tools” or “virtual features were added during COVID and have not been significantly updated”. One response cites a comparison article from 22 months ago.
- Open Insights > Sentiment Analysis and filter by Tag = “Virtual Events”. Sentiment for virtual-event-related prompts scores 34 out of 100 — significantly below the overall brand average of 64.
- Cross-reference: the virtual event weakness reflects an outdated perception. The platform shipped a major hybrid event overhaul six months ago with breakout rooms, live polling, and virtual networking. This is a high-priority content gap: AI is still drawing from pre-overhaul reviews and comparison articles.
- Repeat for “customer support responsiveness”. Sentiment score: 41. The responses reveal that AI is referencing a G2 review cluster and a Reddit thread from two years ago, before the company expanded its support team and launched a 24/7 live chat. Action: publish case studies documenting the improved support experience, solicit updated reviews on G2 and Capterra, and pitch the hybrid event relaunch to event industry publications that AI models index heavily.
Go Further
How SWOT extraction works
Read the Brand Perception documentation to understand how Qwairy extracts strengths, weaknesses, and themes from AI responses
Strengths/weaknesses over time
Track how AI-identified strengths and weaknesses evolve over time using the answer-details data source in Looker Studio
Share the SWOT snapshot with your brand team
Create a shared view of your AI-extracted SWOT analysis for your brand or product marketing team
Related Questions
What does AI actually say about my brand?
Read the full AI responses before analyzing the aggregated strength/weakness picture.
What content should I create to improve AI visibility?
Turn AI-identified gaps into a concrete content production roadmap.
Is the AI perception of my brand aligned with our actual positioning?
Extend the strength/weakness analysis into a full positioning alignment assessment.
How do I correct misinformation that AI is spreading about my brand?
Address AI-identified weaknesses that are factually incorrect or outdated.