The Approach · A sub-framework of VCT
Known, Recommended, Chosen
You don’t get booked by chance: you get booked by being Visible, Connected, Trusted. This is how Visible works in the age of AI search.
Known, Recommended, Chosen describes how AI models decide who to surface as a speaker: whether they can correctly identify who you are, whether trusted sources validate you enough to name you with confidence, and whether you match the actual situation someone is in. It’s how the Visible pillar of VCT works.
The problem this solves
Most advice on AI visibility treats it as a content problem: publish more, post more, hope the algorithm notices. That’s the same hope-as-a-strategy thinking that’s always failed speakers waiting on the next referral. AI visibility isn’t one big fuzzy goal. It’s three distinct, sequential gates. Miss one, and the others don’t matter.
Stage 1: Known
AI has to correctly identify who you are before anything else happens. Not a vague sense that you exist, an unambiguous answer: this person, this topic, this body of work.
This is a consistency problem. Every framework, every credential, every claim you make needs one dedicated, consistent home online, described the same way everywhere. If your bio says one thing on your site, another on LinkedIn, and a third on a bureau listing, a model has to guess which one is real. Uncertain models hedge. Hedging models don’t recommend anyone by name.
Example: a speaker with three different one-line descriptions across their website, LinkedIn, and a podcast appearance. An AI asked about their specialty gives three different, partial answers, none of them wrong, none of them confident.
Stage 2: Recommended
Being known isn’t being trusted. AI has to see proof that didn’t come from you, before it’s willing to put your name in an answer.
This is where other people’s words do the work your own website can’t: being quoted, appearing on someone else’s podcast, getting named in an article you didn’t write yourself. It’s the same thing that’s always earned trust, real proof from real sources, just with higher stakes now, because AI isn’t showing ten options. It’s naming two or three. Independent research backs this up: a 2024 study out of Princeton and Georgia Tech found that content backed by citations and named sources gets recommended by AI models measurably more often than content without them.
Example: two speakers with equally strong websites. One has been quoted in three industry publications and cited on a peer’s podcast notes. AI recommends the second one, not because their site is better, but because independent sources already vouched for them.
Stage 3: Chosen
Being recommended in general isn’t being chosen for this. The final gate is relevance to the actual situation someone’s in, not just the topic they typed.
Buyers don’t search for “keynote speaker” as an abstract category. They search because something happened: a merger, a morale problem, a sales kickoff nobody’s excited about. Content built around real triggers and real questions (the kind that show up in your own inbox and client calls) outperforms content built around guessed keywords, because it’s already speaking to the situation, not just the subject.
Example: an event planner typing “speaker for a leadership team going through a reorg” gets a different, more specific answer than one typing “leadership keynote speaker.” Whoever built content around the first, more specific situation is the one who gets named.
Known, Recommended, Chosen: the three stages
01
Known
AI correctly identifies who you are and what you do, without ambiguity.
In practice: one consistent description, everywhere it appears.
02
Recommended
Trusted third-party sources validate you enough that AI is confident naming you, not just aware you exist.
In practice: citations, podcasts, industry publications — proof earned off your own site.
03
Chosen
You match the actual situation someone is in, not just a topic, but the specific reason they’re searching.
In practice: content built around real triggers and questions, not generic keywords.
Where this fits
Known, Recommended, Chosen is how the Visible pillar of VCT (Visible, Connected, Trusted) actually works for AI-era discovery.
The Pyramid of Understanding underpins Connected and Trusted, the relationship and credibility side of the work. This framework underpins Visible: how AI finds you in the first place.
Where do you stand?
That’s Known, Recommended, Chosen, in three questions.
Answering “no” to any of these is common, not fatal. It’s also exactly what an AI Visibility Audit is built to pinpoint.
