AI visibility tools: Are they worth it?
- 13 hours ago
- 5 min read
Artificial intelligence (AI) visibility tools are a hot topic in many marketing conversations these days, with the likes of Profound and AthenaHQ promising to track how often brands appear in AI-generated answers. But are they worth the cost or effort?
Key takeaways
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Our two views start from the same uncertainty: AI-driven discovery is evolving faster than our ability to measure commercial impact with confidence.
 Rhys More cautious
Here’s the problem. AI visibility tools are trying to measure something we can’t yet prove actually matters. With traditional search engine optimisation (SEO), there’s a clear chain. Rankings lead to clicks. Clicks lead to conversions. You can argue about attribution models, but the path exists. With AI visibility, that path doesn’t. There’s no solid evidence that being mentioned in an AI answer drives pipeline or revenue, especially in long B2B buying cycles. There’s no clear path to ROI Right now, visibility scores are proxies, not outcomes. No platform can show a reliable link between an AI citation and commercial impact. Until that attribution exists, it’s hard to justify these tools as anything more than directional indicators. Urgency shapes how these tools are positioned A lot of these tools are marketed by amplifying a fear of invisibility. The fear of falling behind. The fear that AI is already replacing search. That urgency sounds convincing, but fear isn’t proof. Visibility doesn’t automatically lead to choice. Visibility scores lack shared meaning AI visibility tools often report scores, rankings or share-of-voice metrics, but there is no agreed standard for what ‘good’ actually looks like. A visibility score of 30 or 70 may signal relative presence, but it doesn’t map cleanly to awareness, trust or preference. Without benchmarks tied to outcomes, teams risk optimising toward numbers that feel directional but offer limited guidance for decision-making. The data doesn’t explain much Tools like Google Analytics can show what happens after someone clicks, including traffic from generative platforms. What they can’t show is how often a brand is mentioned, summarised or cited inside AI generated answers when no click occurs. That gap is what AI visibility tools are attempting to measure, but it remains difficult to interpret or connect to action. |  Nat More pragmatic
People are using generative AI to research markets, compare options and form early opinions. Discovery is fragmenting. Even if Google Search still matters, brand narratives are increasingly being summarised by systems marketers don’t control. Discovery is already shifting AI tools are becoming a front door to information. If your brand doesn’t show up in those summaries, you’re not part of the conversation, regardless of how strong your SEO or paid media might be. Flying blind is a risk in itself Without any visibility tracking, teams don’t know if their brand is being cited, ignored or misrepresented. They don’t know whether competitors dominate AI summaries for key topics. No PR team would run a campaign without media monitoring. Letting AI shape perception with no tracking at all creates a similar risk. Imperfect data beats no data The case here is not broad adoption, but limited experimentation with clear expectations and review. Yes, the data is messy. But if you’re investing in generative engine optimisation (GEO) at all, clearer content, stronger authority and better structure, you need some way to see whether those efforts correlate with increased presence in AI outputs. Manually checking ChatGPT, Gemini and Perplexity over and over isn’t realistic. The tools don’t fix the problem, but they automate something you’d otherwise do badly and inconsistently. We’ve seen this pattern before Early SEO metrics were crude. People tracked rankings long before they understood how position translated into revenue. Attribution came later. Teams who started measuring early had baselines. Those who waited had nothing to compare against when the channel matured. The downside of doing nothing is bigger Spending a few thousand dollars a year on tools that under-deliver is frustrating. Being invisible in AI-mediated discovery if it becomes dominant is far more costly. From this perspective, AI visibility tools aren’t about perfect optimisation. They’re about building capability before you urgently need it.
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Verdict: Buy
Scepticism about AI visibility tools is reasonable. At present, visibility in AI-generated responses can’t be reliably linked to commercial outcomes.
This is less a judgement on the tools and more a question of risk. As AI systems play a growing role in early discovery, organisations must decide whether operating without any visibility into that layer is acceptable.
In practical terms, AI visibility tools are unlikely to justify significant spend or operational effort today. But modest investment can be reasonable where the cost of having no visibility at all outweighs the imperfections of the data.
When AI visibility tools matter most
AI visibility tools are likely to matter more in categories where AI-driven discovery sits closer to conversion, such as retail, ecommerce, travel and product-led services. In these cases, heavier investment in AIEO or GEO tools can be easier to justify.
In longer cycle or more indirect categories, including utilities, superannuation and some B2B sectors, the value is more about brand accuracy, trust and narrative control. That often supports a lighter touch approach focused on monitoring rather than aggressive optimisation.
Appendix: Popular AI visibility tools (reference only)
Platform | What it tracks | Supported AI platforms | Strengths (from independent reviews) | Weaknesses (from independent reviews) |
From ~$600+/mo (enterprise pricing) | Brand visibility in AI-generated answers, citations, prompt-level responses, AI crawler interactions, and where content appears in AI outputs. | ChatGPT, Google AI Mode/Overviews, Gemini, Claude, Copilot, Grok, Meta AI, DeepSeek | Broad AI platform coverage, strong competitive tracking, real-time monitoring | Higher cost, complexity can be a barrier for smaller teams |
~$450-$1,300/mo | Brand visibility, citations and mentions in AI generated answers, with prompt level and competitor tracking. | ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews
| Clear brand narrative visibility, strong competitor comparison, intuitive interface
| Limited attribution, pricing may be high for smaller teams
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~$300–$750+/mo (incl. base plan) | Brand visibility and performance in AI-generated answers, including visibility scores, competitor research, and prompt tracking. | Responses from generative AI systems such as ChatGPT and Google AI Mode, with coverage implied across platforms in toolkit descriptions. | Combines AI visibility with established SEO workflows | AI visibility features less mature than specialist tools |
~$400–$800+/mo (usage-based) | Brand mentions, citation frequency, and related signals from AI-generated responses and monitors visibility across multiple generative AI engines. | Major generative AI engines including ChatGPT, Gemini, and Claude. | Cross-platform monitoring, hands-on team support | Credit system can escalate costs, some features still evolving, limited attribution |
~$50–$1,000+/mo (credit-based) | Brand visibility and citation frequency across AI search results, providing visibility scores and citation tracking data. | Major AI engines such as ChatGPT, Perplexity, Google AI Overviews, and others (detailed lists vary by source). | Sentiment and citation tracking, relatively simple interface | Pricing can be unpredictable at scale, limited enterprise-grade documentation |
