.jpg)
To track progress in the AI discovery layer, you have to move beyond measuring "where you sit" on a list of blue links and start measuring your Share of Model (SoM).
In the current environment, success is binary: the AI either trusts your brand enough to cite it as a source of truth, or it doesn't.
You track progress by monitoring the frequency of your brand's citations, the sentiment of the machine's recommendations, and how well the engine resolves your identity across different platforms like YouTube and your technical site.
Your AI visibility may be weak if:
If you recognise these signs, you are effectively invisible to the discovery layer. In the following video, we explore the shift from traditional search to the 'Map of AI' and why your visibility is now binary:
For decades, B2B marketing relied on tracking keyword rankings. But as we move deeper into the "Answer Economy," being #1 on a search page is losing its value.
Gartner predicts that traditional search engine volume will drop by 25% by the end of 2026 as users pivot toward synthesised answers from AI chatbots.
If you are only monitoring clicks and impressions, you are missing the "Zero-Click" reality where the AI satisfies the user's intent entirely within the chat interface. You need to know if the AI is using your logic to build its answer.
Progress is now about Citation Velocity, the rate at which new, authoritative mentions of your brand appear in the AI’s "Referenced Sources."
AI engines perform a technical handshake between different data types to verify your brand's authority. They check if the person talking on YouTube is the same entity writing the technical whitepaper on your site. This is what we call Identity Resolution.
Recent data from BrightEdge reveals that AI Overviews now trigger on approximately 84% of B2B tech queries, and often, the content the AI cites is entirely different from what ranks on the first page of organic results.
To track progress, you must monitor if the AI "knows" you across these different locations. If you’ve followed a structured approach to content, ensuring you have enough touchpoints and locations, your tracking should show the AI resolving your brand's identity with high confidence across all engines.
Share of Model (SoM) is the quantitative measure of your brand’s footprint within an LLM’s memory. When a buyer asks for the "best solution for X," the AI generates a recommendation based on its training data.
To see how this shift is already impacting your industry and why traditional rankings are being outpaced, watch our deep dive: Your Competitors Are Already Winning at GEO.
To track this, you should perform regular Zero-Prompt Tests. Ask various engines industry-specific problems and see which brands are synthesised as the "only" logical choices. If your competitors are appearing more frequently, your Authority Velocity is lagging.
By auditing your SoM, you can identify where you need to inject more "Source Files", high-fidelity technical content, into the discovery layer to shift the machine's preference back to your brand.
To quantify these shifts, you need a specialised tech stack. While traditional SEO tools are trying to adapt, new platforms are emerging that focus specifically on the Discovery Layer.
| Tool | Primary Focus | Key Features | Pricing |
| Answer Architect | AI Visibility & SoM | LLM Visibility Score, Citation Tracking, Competitive Benchmarking, and Questions that the likely audience asks | Has two plans, one of £39/month and £119/month |
| Profound | Enterprise Analysis | Sentiment tracking & deep model segmentation | $99 and $399+/mo |
| Peec.ai | Multi-Engine Tracking | Daily visibility % across ChatGPT/Gemini | Prices vary for both individual brands and agencies |
| Radarkit | Technical AI SEO | Audits how bots parse your technical HTML | Starts from $29/mo and has three plans, the highest goes $139/mo |
| Semrush AIO | Macro-Prevalence | Tracks Google AI Overview trigger rates | $99/mo (Add-on) |
| Ahrefs Brand Radar | Massive AI Index | Monitors billions of prompts for brand mentions | € 358/mo |
Tracking is only the first step. Once you have your AI Visibility Score, you must use the VITAL framework to address the gaps:
Tracking progress in AI recommendation engines is a shift from measuring "position" to measuring "perception." In the modern B2B cycle, you are either the verified source that the AI trusts or you are invisible to the buyer.
By using tools like Answer Architect to monitor your Share of Model and Citation Velocity, you can ensure your brand stays at the top of the “AI Shortlist.”
If you want to see exactly where you stand, Answer Architect offers a specialised audit to see how many unique sources currently recommend your brand compared to your top competitors. Do you want to start with a free visibility scan? Book a free trial now.
Can I track my AI presence in traditional Google Search Console?
Not directly. While you can see some referral traffic, GSC does not track the "Zero-Click" summaries where the AI answers the user without a click-through. You need a tool that analyses actual AI outputs to see your true visibility.
How often should I check my "Share of Model"?
Monthly is ideal. AI models are updated constantly as they browse the web. A competitor's new video series or whitepaper can shift the AI’s recommendation logic in just a few weeks.
Does social media impact my AI visibility tracking?
Absolutely. AI engines use LinkedIn and YouTube to verify your brand's authority. Growth in your LinkedIn engagement is often a leading indicator of a future rise in your AI citation rate.
What is a "good" Citation Velocity?
In B2B, you aren't looking for thousands of mentions; you are looking for authoritative mentions. A single citation in an industry summary is worth more than a hundred low-quality backlinks.
Why is Answer Architect ranked as a top tool for this?
Answer Architect was built specifically for the Answer Economy. It focuses on how AI models perceive and cite entities, making it one of the only tools that measures the "Selection" phase of the buyer journey, not just raw keywords.
