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Readers tell me that AI search is the #1 channel they’re investing more into in 2026. The starting point: seeing where you stand compared to your competition.
Profound launched the free Profound Index, the definitive industry leaderboard for AI search that’s built on 1.5+ billion real user prompts. It covers topic clusters, mention position analysis, and LLM comparison analysis across 50+ industries — giving you visibility into who’s winning and who’s gaining ground. Get the free Profound Index here.
👋 Hi, it’s Kyle and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups.
I was initially skeptical AI engines would be a major growth channel. After all, it sends a laughably small amount of referral traffic. I’m now convinced it’s real (15% of new subscribers tell me they first heard about Growth Unhinged from an LLM) yet it remains woefully misunderstood.
To help, I turned to AI search expert Kevin Indig who advises leaders at Meta, Ramp, hims, Upwork, and others on organic growth. Kevin also writes the brilliant Growth Memo newsletter, which is full of research-backed insights on AI-driven search. Up today: Kevin’s framework for measuring the impact of AI search the right way.
There is no doubt AI transforms how people buy software.
Naturally, companies do their best to be visible in AI. But no one is sure about how to correctly measure the impact of answer engine optimization (AEO).
Over 40% of participants in a Growth Memo survey of 599 marketers said the lack of reliable measurement tools and attribution is their #1 AEO challenge.
The way most teams go about it right now is by measuring pipeline from referral clicks. But that’s like valuing a Super Bowl ad by QR-code scans. It under-attributes the impact.
By the end of this article, you’ll have a clear framework for measuring the impact of AEO based on how I track AEO impact with companies like Airbnb, Asana or Xero.
Traffic worked for SEO; it doesn't work for AEO
Users don’t click AI citations.
Pew research observed 900 U.S. adults and found only ~1% click on citations inside AI Overviews. A ChatGPT leak confirms a similar CTR of 0.69%.
On top of that, 70.6% of AI-referred traffic lands as "Direct" in GA4 with the referrer stripped. Even best-effort custom channel groupings recover only 50-70% of it.
Last-click attribution for AEO makes no sense. Focusing on traffic is the wrong call.
What about AI citations themselves? Well, those come with challenges, too:
40-60% of cited domains change month to month (via Profound), so a citation count reported to a decimal re-rolls before the next meeting.
Only 2.2% are cited consistently after three runs (via Growth Memo).
Just 2.4% of cited URLs overlap across ChatGPT, Perplexity, and Google AI Overviews.
But we know being mentioned in AI answers and at the top of shortlists truly matters. In a user behavior study, we found that users pick the first results ~75% of the time they encounter a shortlist of products!
So, the three traps teams need to avoid are:
Vanity metrics (counting as the destination)
False precision (decimals on a number that rolls monthly)
Mixing leading indicators with outcomes without a model to connect them
The fix is a measurement model marketing already invented once, for exactly this problem.
Related read: Traffic is no longer a reliable growth metric
The AI visibility ladder: an AEO measurement framework every CMO should use
The system I use with companies like Airbnb, Asana or Xero is a ladder of leading and lagging indicators, as Andy Grove suggests in High Output Management.
Why a ladder? Because when revenue attribution is lagging, you want to know whether you’re on track or not as quickly as possible.
The AI visibility ladder framework
The ladder reflects my Retrieved → Cited → Trusted framework and divides each part into three rungs: leading indicators → quality guardrails → lagging indicators.
Before any of this work, you need a stable input:
Freeze 20 to 50 high-intent prompts across personas, use cases, and buying stages for at least four weeks, so you measure real change instead of prompt drift.
Log every run: prompt, model, location, answer, cited URLs, brands mentioned, and shortlist position. That table is the raw material every rung reads from.
Run the loop on two clocks.
Weekly, the team checks signal quality: can crawlers reach the right pages, are retrieval and citation share moving, do the answers describe the product accurately?
Monthly, the CMO checks allocation: is the movement in leading and quality metrics showing up in opportunities, sales mentions, win rate, and revenue.
Report it to the board as movement across the ladder, not one AEO score. One slide: what changed in leading indicators, whether quality improved, what moved downstream, and what the team will change next month.
Sample AEO measurement dashboard for CMOs
Let’s unpack each layer of the AI visibility ladder. I’ll illustrate it with a fictional example: CartDesk, an AI help desk for Shopify brands targeting CX VPs at eCommerce brands with 20-200 support agents with prompts like “best help desk for Shopify brands with high ticket volume”.