u/Which_Work6245

One of my biggest weekly dilemmas: do I engage with the crappy AI-generated comments on posts? 

LinkedIn's algo says I should engage in the comments. 

But my remaining shred of self-respect says don't talk to this person's (very badly deployed) AI as if it's a real person. 

It's a constant internal struggle.

How are you guys looking at tracking visibility for different stakeholders?

We’ve been building out intent clusters based on jobs to be done. 

We start with a matrix - as in image (this is for a CRM)

for a CRM

Then put that into Claude.

Short version of prompt:

“For each row (cluster), generate up to 25 prompts that a real B2B buyer would type into an AI assistant during their purchase research. Use the angles in each cell to ensure the prompts reflect the full range of perspectives across the row.”

Interested to hear how others are doing this.

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see full prompts and system - google ‘demand genius intent clusters’.

LLMs tailor responses to each stakeholder’s needs and criteria. 

e.g

CFO asks about cost efficiency and compliance of brand
vs
VP of Engineering asks about API flexibility and developer experience?

How do you make sure your AEO strategy accounts for both?

Internally, we’ve set out a ‘Criteria Alignment’ strategy and define it as - how often we come up alongside the specific attributes that matter to each stakeholder in a buying group.

Our tactics go along the lines of:

1. Identify 5 specific attributes per persona that most influence their evaluation. e.g
   • CFO’s - total cost of ownership, implementation risk, and compliance coverage.
   • VP of Engineering’s - API extensibility, developer documentation quality, and how well the product integrates with existing tech stack.
2. Track how often brand appears for those attributes
   • particularly in awareness and consideration-stage prompts
3. Compare against competitors on the same attributes

How do you go about doing this?

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If you’re interested in a more in depth write up google ‘demand genius criteria alignment’.

Everyone's trying to boost AI visibility.

Optimising for BOFU clicks and trying to get recommended when someone searches "best x tool alternatives" in the LLMs.

But how much effort is going toward the TOFU/MOFU stage.

Framing the questions and requirements.

Isn't it more about defining 'x' around your product/service.

So by the time it gets to 'best tool for x' you'll show up?

How are people going about this?

I started thinking about this after we did our AI in Fintech report .
~91% of content we analysed was current, 2% outdated, and just 7% evergreen.

That 91% will soon be outdated, if not maintained.

Our study showed a clear shift towards topical, "always on" content.

Volume is a hack for AEO in the short term. Churn out enough listicles/topical TOFU pieces and you'll see those AI referral metrics tick up.

But the content "surface area" that needs to be maintained.

LLMs are looking to give clear answers to questions.

If your content library is full of contradictions (because you updated your product positioning and now that content from 2022 conflicts with your 2025 messaging) LLMs will just move on to someone else to get the answer they need.

I think the brands blindly doing this are going to really pay a price.

if you want to read the report - search 'demand-genius fintech report'

You know your content is working, but can't prove it to the CEO?

This is the typical scenario that plays out in the boardrooms: "We've generated 50 content-influenced deals in Q1"

And then the marketing team gets blank stares...

What are you doing in this situation? How are you showing your content is working beyond the MQL stage?

We're looking at how is content influencing these:

1. Velocity: Do deals close faster? ↳ (e.g. 10 days quicker than cold outbound)
2. Value: Is the contract value higher? ↳ (e.g. £4k more per deal)
3. Rate: Are they more likely to close? ↳ (e.g. 3% higher win rate)

We ran AI agents on their entire content library.

We looked at what we call information gain. Whether a page adds net new understanding to a problem space or just restates what already exists.

We have our AI categorise as one of four levels:

‣ Level 0: No information gain
The kind of content that summarises or paraphrases what's already out there. Nothing a model couldn't piece together from a hundred other sources.

‣ Level 1: Interpretive gain
Takes known ideas and reframes them through a different lens. A fresh angle, but not original research.

‣ Level 2: Empirical gain
Brings original data, benchmarks, or measurements that aren't widely documented elsewhere.

‣ Level 3: Conceptual gain
Introduces a genuinely new mental model, framework, or way of reasoning. This should be rare.

60 out of 61 pages sat at Level 0, with one lonely page scraping into Level 1 and nothing at Level 2 or 3.

I'd bet a lot of content libraries would produce similar results.

We've spent years building content the same way.

Google rewarded high quality summaries. The goal was to direct users to the summary that best matched the query intent.

LLMs work differently, they are literally summarisation machines. They reward content that enhances their answer, and that requires information gain.

If your content is a repackaged version of what everyone else has published, an LLM has no reason to draw on you. You're just restating what it already knows.

We recently did a study on what we've called 'Dark AI'.

Only 16% of AI prompts in complex buyer journeys produce brand citations.

We simulated AI interactions across the full buyer journey, in 14 different B2B categories, to try and understand how AI responses evolve across journeys.

We had the distinct sense that existing advice is suspiciously SEO-like. Is this because SEO-style optimization is effective, or because leading SEO brands and voices are defining AEO?

Turns out the latter.

We found LLMs cite brands just 16% of the time, and ONLY in conversion-stage queries.

Meaning that by treating citations as the AEO north star, one fails to influence the 84% of interactions.

We're calling "Dark AI".

Some of the most interesting things we learnt:

1️⃣ AEO is an iceberg, not a funnel. What you currently see - citations and referral traffic - are just the tip. The majority of AI influence happens in awareness and consideration-stage conversations that never generate a click.

2️⃣ LLMs act like personal shoppers, not directories. They tailor recommendations to each buyer's role, constraints, and trade-offs. Broad claims of category dominance actually weaken your position. You want to communicate fit, not dominance.

3️⃣ BOFU visibility is decided earlier. LLMs converge on a small set of viable brands as queries become more decision-oriented. Once that happens, the shortlist barely changes between runs.

If you want to read it just Google 'demand-genius dark ai report'.

Agree or Disagree?

I reckon we'll start seeing those with less content start doing better than those churning out loads of mid blog posts.

There's a lot of people churning out listicles and barely relevant content to expand their "surface area" for citations.

I reckon we'll start seeing this backfire.

By chasing referrals as if a query = a keyword, you’re hurting your ability to shape the TOFU and MOFU conversations beneath the surface where requirements are formed and decisions are made.

You’re diluting the LLMs understanding of who you are, who you’re for, and when / how to recommend you by trying to rank for as many terms as possible.

Instead, we're trying to

• do less but more targeted content with better strength and relevancy
• focus on the prevalence of our “narrative” in the hundreds of conversations beneath every MOFU / BOFU prompt

SEO was a channel.
AEO is an active market participant.

Brands that win AI Search will be the ones that recognise this difference and stop treating LLMs like they’re just a new search engine to optimize.

We should be thinking about LLM enablement, not AI Engine Optimization.

It’s about teaching the LLM who you are, who you’re for, and when / how to recommend you. Not chasing direct referrals that are unlikely to come.

Here are the key metrics I'm looking at.

• How content is influencing our positioning, as LLMs see it?
• Where the inconsistencies, gaps and quick wins are that can improve this?
• How positively / negatively do LLMs see our brand for specific use cases?
• What questions, based on actual engagement data, are coming up at each stage of our buyer journey?
• How prevalent is our brand narrative in broader industry conversations where requirements are actually defined?

How are you looking at it?

Do 3,000-word blog posts still work or are they dead? Because honestly they remind me of school when I'd stuff long sentences to fill out a word count.

I feel word count as a north star content metric is bollocks. Always has been.

There's a famous Mark Twain quote: "I'm sorry I wrote such a long letter, I didn't have time to write a short one."

That's the same trap.

You have a simple point to make, but you pad and meander through three paragraphs of fluff just because some SEO data said long-form content ranks better.

Instead, focus on "value per sentence"

As a reader, I don't care if your blog post is 500 words or 5,000 words. I'm skimming it anyway...

I only care about one thing: how much of what you wrote is actually valuable?

If there's 3,000 insightful words to be spoken on a topic, publish 3,000. If there's 2,000, publish 2,000.

(LLMs are getting too intelligent for these hacks anyway. If you chase hacks, you'll constantly play whack-a-mole against the smartest engineers on the planet)

Curious when was the last time you actually read a 3,000 word post from start to finish?

The word "rank" has no place in AEO. Two reasons.

(1) Less than 16% of AI queries in B2B categories lead to a "search". Even then, it doesn't "search" for an answer to the original query.

We ran hundreds of awareness, consideration, and conversion stage prompts multiple times across 14 B2B categories*.

Guess how often AI invoked retrieval? (ie. "searched")

Awareness: 0%.
Consideration: 0%.
Conversion: 48%.

Total: 16%.

In the real world, conversion is a much smaller part of a complex journey, so that's a pretty big overestimate.

Even when AI does "search" it doesn't search for the original query. It breaks it down into a series of queries to capture the information it needs to compile an answer.

Meaning if you prompt: "Why do people keep talking about AI as if it's the same as search?" it won't "search" for an answer to that question.

It'll search for answers to 20 sub-queries you don't see and didn't optimize for.

2 - The order in which an LLM response cites brands has ZERO bearing on which brand "won" that response.

"Don't use Salesforce, use Hubspot". Congratulations Salesforce, you ranked #1!!!

This is of course a bit of an extreme example, but you get the point.

Despite this a lot of "AEO experts" (who have usually recently rebranded from SEO experts) frame all their advice in SEO frameworks and terminology.

It makes sense. It's what they know, it's not intentionally misleading. But marketers need to realise that they're getting very bad advice from a lot (not all) "AEO experts".

*Source: Demand-Genius Dark AI report

The typical enterprise B2B brand is cited in just 3% of AI overviews that it's relevant for.

3%.

Walker Sands dropped some great research this week. Here's the specific breakdown they published for a few industries:

Cybersecurity: 4.2%
Fintech: 3.8%
HR Tech: 2.8%

To be clear, this is AI overviews - slightly different to conversational AI with ChatGPT, Claude, etc.

But our own research*, which also studied across 14 B2B categories (feels a bit like they copied us there!) wasn't any more encouraging.

Just 16% of responses across a B2B buyer journey produced a citation in ChatGPT.

This data should be alarming to anyone adopting the popular AI Search "playbook" of chasing metrics like Share of Voice and Citations.

They are, quite simply, not the right metric. They are SEO metrics. They treat AI as though it is a channel just like Search.

It's not. Search is a directory. AI is a thought partner that your buyers engage with at every stage of their journey.

When they're understanding the problem.
When they're building requirements.
When they're building a shortlist.
When they're comparing providers.

Citations ONLY occur towards the end of that journey (we tested it - 0% citation rate on awareness and consideration-stage queries).

We know that B2B categories are won and lost in those earlier stages. Yet if you're reading this, I will bet your AEO strategy ignores them entirely.

-- It's time B2B brands got better advice on how to win AI search. --

If your category is won at the point of transaction - think shampoo, loo roll, toothpaste - then AI Search is simple. Be visible in that moment.

If it's won in the messy, complex, 12-month long buyer journey before purchase, you need a different approach. One built around influence, not visibility.

*Demand-Genius Dark AI report