Why Does AI Find a Page but Not Cite It?

Being retrieved is only an intermediate step. A page can be discovered during query fan-out and still fail to appear in the final answer if another source better matches the intent, offers more distinctive information, presents a cleaner answer passage, has stronger corroboration or provides fresher and more defensible evidence. The episode therefore treats GEO as a sequence: match the intent, make the answer easy to retrieve, establish trust, and give the system a reason to select and cite the source.

Editorial note on study figures

Percentages and study findings quoted on this page are presented as statements made by the speakers during the recorded conversation. This episode page preserves the discussion rather than independently re-running or re-validating the underlying studies.

The episode is hosted through Buzzsprout and distributed through the NeuralAdX GEO & AI Search Strategy Podcast. This NeuralAdX page provides the crawlable episode summary, chapter structure, related resources and cleaned transcript.

Hello, and we’d like to say hello today to the wonderful Rhiannon Cecil, who is Head of Content at Digital Authority Partners, a Chicago digital marketing agency founded in 2016.

Rhiannon has spent more than 10 years in content, starting in public relations and building brand messaging and communication strategy. She moved into agency SEO at one of the largest digital marketing agencies in the United States, working on Fortune 500 manufacturing and imaging accounts.

She began experimenting with AI in 2022 and has carried this experience into her current role.

Digital Authority Partners is a MarCom Gold winner for Best SEO Agency and an Inc. 5000 company. She runs DAP’s original research programme on AI search, designed to eliminate the guesswork surrounding what works in search today.

The programme runs controlled studies at scale to find out which sources these systems retrieve, which brands they name, and what businesses can do to change either one.

Two of those studies are already published: the AI Content Fingerprint and the AI Visibility Gap, with a new study on brand mentions launching soon.

I mean, Jesus Christ, wow. Now that’s a résumé.

So thank you so much for joining us here today, Rhiannon.

With that, we’re moving into the query we discussed, which we thought would be useful for our viewers, the majority of whom will be business owners with this particular question:

Why does AI find a page but not cite it?

So, Rhiannon, if that question came to you from a client, it’s obviously a very common problem with AI systems today and all the different platforms that are available.

What would be your interpretation, and what process would you use to answer that client and bring about a resolution to the problem?

I think the most important thing to look at when it comes to a page being findable but not citable is obviously the content on that page.

The first thing I always ask is: are you summarising what everybody else is already saying?

If the answer is yes, then AI has no reason to cite the page.

The other thing I look at is how old the information is. We all know now that LLMs reward recency.

One of our studies actually found that, within a three-week period, only 33% of those citations remained.

So you need to keep giving them new information in new formats in order to get cited from that page.

What I think is happening with older content is that the LLMs are using it in their training. They’re browsing all of these pages and internalising that knowledge, so they don’t need to cite a page anymore.

I think that’s a very good possibility of what’s happening.

Essentially, if you are being found but not cited, you need to give LLMs a reason to cite you that they cannot find anywhere else.

That might be a downloadable asset, for example. LLMs always want to help. That’s what they’re programmed to do.

If somebody asks a question about SEO, for example, you’ve got a better chance of being cited if you have a checklist.

The AI can say, "Here’s the answer, and if you want, here’s a checklist for you."

So that’s how I would look at the process: recency, whether there is anything unique on the page, and obviously the structure of the page.

Page formats are very important. They can almost even the odds before you’ve even put content on the page.

In another study we conducted, we looked at what types of pages were getting cited.

The majority were reference and encyclopedia pages, and they were cited about 75% of the time in our study.

After that was editorial content at 58%.

So if a client is investing a lot of money in service pages and heavily buyer-intent-focused pages, I would also advise them to look further into their editorial and informational content.

So straight off the bat, when you have that situation, one of the things we’ve got to think about is search intent, isn’t it?

It’s the particular prompt you’re trying to surface for on behalf of the client.

Is there a particular process you use to establish that throughout the article you’re creating to surface for that prompt?

Is there a way you narrow things down to make sure you’re hitting that search intent precisely?

Yes.

If you look at the old days of SEO, you had a primary keyword and a collection of contextual keywords that helped you get your point across.

Just before LLMs started becoming very important in search, there was a version of that which became translatable into optimising for GEO.

Obviously, when people are asking LLMs questions, it’s a conversation rather than simply a query.

What we do is try to answer as many of those subqueries as we can on a specific page.

Regardless of where the conversation goes with the user, there’s potentially an answer for it.

A simple but useful example would be if you were advertising a forklift.

A common prompt might be: "Is there one that fits in a 10-foot aisle?"

You can have two sentences addressing that which specifically answer the kind of questions people are likely to ask.

Essentially, we try to answer as many questions, proposed in as many different ways as possible, within a piece of content so that it retains its value.

I think in GEO terms they call that generative answer coverage.

From the initial H1 title, you move down through the H2s and propose questions that sometimes step slightly outside the immediate intent to cover semantically related areas.

That gives you a greater chance when the AI engines perform query fan-out that your content will be retrieved and considered during the synthesis process.

What you’re describing is exactly the same type of thing that we do.

As you pointed out, it’s partly led by previous SEO tactics, but now you’re making sure there is significantly more expanded coverage because AI engines can perform so many query fan-outs.

Sometimes there might be 50, 60 or 70 different areas being explored.

There are therefore many potential areas to cover, and you want the article to address as many relevant ones as possible to give yourself the optimal chance of surfacing.

Once the search intent has been established, we move into retrieval.

The AI engine has performed its query fan-out and has a large number of potential sources available.

Is there a particular way that you structure content to make it more retrievable or make it stand out to the AI systems so that they select the content?

One thing we tested that is working quite well is what we call the TL;DR — "too long; didn’t read."

That little box at the top works really well.

Everything changes very quickly in the world of AI, but for now we’ve found that it works particularly well for metrics and data.

If you give the system the opportunity to parse the most important, citable information first, it appears more likely to cite you.

That’s something that has worked really well for us and something we’re certainly going to continue doing for now.

Then obviously you have your page structure.

In our study, what we found was that how you build a page was a predictor of citation 71% of the time.

So it is about proper heading structure.

Structured data and schema markup are obviously very important, and we’re seeing that increasingly in AI search.

Outbound links to authoritative sources have also become more important.

Again, there are parallels with traditional SEO.

Something else that works really well is being careful about how you position the answers to the questions your H2s are posing.

The answer needs to be immediate so that, when the AI is parsing the paragraph, it can clearly see that question-and-answer relationship without defaulting to the vastly overused FAQ section structure.

What you’re saying about the TL;DR is interesting.

I learned about that a little while ago because there was a study which showed that implementing a TL;DR could improve the likelihood of AI citing your content.

I don’t recall the organisation behind the study, but it demonstrated that effect.

So I can verify that what you’re saying has also been demonstrated in research, and I’ve implemented it myself.

We’ve both independently come across that particular piece of information and incorporated it.

Absolutely. It’s good to know we’re on the same track.

We’re all on an expedition trying to figure this out.

No doubt sometimes we’ll test the same thing independently, and as soon as we see that it works, it becomes part of the artillery for future campaigns.

What you were describing about having a question followed by a direct answer also fits into passage-level retrieval.

You’re structuring the page so that it isn’t simply machine-readable; it’s also optimal for the AI to extract that particular section.

It might be a small paragraph, but it directly answers the question.

You have the question above it, then the direct answer, potentially followed by a citation or evidence verifying that answer and providing citation fidelity.

The AI system can then identify it as something it can utilise to answer the user’s query.

So again, we’re in alignment on that.

The next thing that comes to mind is this:

We’ve established search intent. We’ve looked at retrievability. Then we come to the all-important question with AI engines:

How do we get them to trust us?

I’m intrigued by this because people have different approaches to how they perceive trust and how they implement tactics around it.

What is your approach?

I think you’re right. A lot of people are approaching the trust component of AI slightly differently.

We’ve seen various things.

One thing that really stands out, which I think is fairly well covered and which one of our own studies confirmed, is that the majority of citations recommending a brand by name are third-party citations.

Our study came in at 89% third-party editorial.

That’s an interesting number to work with.

I think LLMs care more about what other people say about you than what you say about yourself.

Because of that, the recurrence of PR on the digital stage is going to become increasingly important in terms of building trust.

A lot of people are also talking about the importance of author bios and having them visible on the page.

What we’ve found, or what I’ve found, is that author bios are very important, but not necessarily at page level.

They don’t have to be visible on every page of your website.

A large part of our content study examined author bios.

We looked at how detailed they were, how many credentials they displayed, whether they linked through to another page, and we manually analysed all of that.

Pages cited in the study ranged from having no author bio at all to simply saying "staff writer."

Where author bios become important is in your overall entity authority.

They are very important and should be detailed, but at an individual page level, I don’t think they are as important.

Instead, I would look at PR, having conversations with people and participating in roundup articles.

There’s a lot of that happening in GEO today.

For example: "10 best healthcare agencies."

I mention you, you mention me, and everybody tries to become involved in those roundups.

Comparison content is another important area.

Pitching journalists with things such as "this agency versus this agency" or "this tool versus this tool" directly mirrors questions people ask LLMs.

If I were advising a client right now, I would say to invest around 80% of their GEO budget into digital PR and 20% into their own website optimisation.

The percentages are obviously based on your own accumulated knowledge, but regarding third parties, I completely agree because that is exactly what I’ve found myself.

It makes sense.

The people creating the directives behind these systems understand that companies are naturally going to talk about themselves positively on their own websites.

They’re obviously going to tell everybody they’re the best thing going.

But it’s significantly more difficult to get a third party to say the same thing.

So the system can acknowledge what the domain owner says, consider it and look at the evidence, while placing considerable value on third-party information because those sources do not necessarily have a vested interest in praising the company.

That said, something else I’ve found through experimentation is that first-party evidence can also go a long way.

But it has to be worthy evidence. The key word is evidence.

For example, I’ve conducted our AI Citation Benchmark, which compares NeuralAdX Ltd against five leading GEO service agencies.

Every month I update it.

So consistently, every month, the AI is receiving new knowledge graph enrichment from us, and it’s unique data.

Because you’ve got that consistency, the AI engine can keep returning and seeing that we’re carrying out measurable work over a longitudinal period of time.

We also have our "Proof Generative Engine Optimisation Works" page, where we conduct live AI retrieval testing for certain queries within our industry to see how well we surface.

On 19 September, that testing reached one year.

The AI engines can therefore see an entire year of testing on the website.

From a trust perspective, there’s a body of evidence showing that this isn’t simply a spam website.

The amount of work involved demonstrates that somebody has to be taking the subject seriously.

So first-party evidence definitely has value because I’ve seen positive results from it.

For certain prompts we consistently perform very strongly, primarily because of that evidence.

The AI actually cites our AI Answer Visibility and Share of Voice Benchmark, our AI Citation Benchmark and our proof page.

As you said earlier, systems may show a preference for third-party citations, but because our first-party evidence is strong and we use third-party tracking software, AI systems are still choosing to cite content from our own domain.

I’ve found that to be important.

The problem is the time and resources required to maintain it.

I’m often doing the benchmarking at weekends or in the evenings.

It sounds like a great idea initially, but then you realise you have to repeat the process every month.

It can require 20 or 25 hours of work every month.

But that’s one of the ways you can build that kind of authority.

Another major component of trust is topical authority.

I’m sure you have a lot of knowledge about how important that has become with AI engines.

Absolutely.

It’s been interesting watching the evolution from internal linking being a predominant metric of topical authority to something closer to asking how many unique ideas you can provide per hundred words, or however you want to measure it.

I think that’s very important.

What you’re saying about first-party data also goes hand in hand with digital PR.

Nobody is going to pick up your work unless you’re studying these things yourself.

That’s something we’ve found and one of the reasons we’ve invested so much time into our studies.

We’re actually tracking prompts.

We’ve got a team of people looking at things and recording what they’re seeing.

We were in the middle of one of our studies when GPT started showing paid ads, and everybody on the team immediately noticed it.

We all knew something like that was coming, but seeing it happening in real time was pretty incredible.

So the formula of having first-party data, publishing it and then getting it picked up by third parties is going to go a long way in GEO over the next year.

For us, because GEO is what we eat, sleep and breathe, the time investment is something we simply have to accept.

But there are also ways to advise clients.

A lot of clients are already sitting on data that they could potentially use and don’t even realise it.

It’s then about interpreting that data, drawing useful inferences and turning it into something citable.

I think that’s going to become very important going forward.

Do you also find there’s a great need, when dealing with clients, to convey that we need to be calm and methodical and implement accurate, factual information?

It takes time because clients naturally want immediate results.

But with AI, it doesn’t really work that way.

As you were saying, you need to identify the data and evidence they already possess but may not realise they can utilise.

Then you have to harness it and know exactly where to implement it to achieve the greatest effect.

This requires thinking.

It isn’t simply about rushing out 2,000-word blog posts two or three times a day.

You have to calmly structure the information, implement it at the right point and then execute it.

Is that something you also find — that when you explain this to clients, sometimes they simply don’t want to hear it?

Yes.

Like everything, AI becoming so dominant has huge advantages, but it also has very marked disadvantages.

What we’re seeing is that clients often want to produce multiple blog posts at scale all the time.

What I always say is that you may be focused on GEO, but there’s absolutely no reason to destroy your SEO in pursuit of what is most likely to be a very fleeting citation.

There’s this idea of: "If we just publish 400 blog posts, we’re going to get a citation somewhere."

Maybe you will, but Google could penalise you and there are a whole collection of other potential problems.

Again, it comes back to the question: are you simply summarising what everybody else is saying?

Because you can do that a hundred times and the outcome will be exactly the same.

Obviously, we do have clients who see the vision.

They understand that it takes time and they would rather do it properly.

I think it’s always best to caution clients and say exactly what you’ve just said: let’s take this carefully and position it in the right way.

Whatever you publish needs to be defensible.

You can’t simply publish something in the hope of getting a citation.

You need to be able to show your methodology, particularly when you’re talking about first-party research.

I’m very direct with clients.

If something isn’t going to work, I’ll tell them: this isn’t going to work if you do it that way.

Everybody wants to get involved in what they see as the beginning of this journey, but it doesn’t work through sheer volume.

We are also effectively tenants within an AI Overview, a GPT summary or whatever interface we’re discussing.

We can push citations now, but what I think we would prefer to achieve is brand mentions.

Being recommended by name is another level altogether.

And then you want content that withstands the churn.

You want to be one of those URLs that survives over time.

Those are the important things.

Yes. That is almost the definition of top-level GEO because that’s one of the hardest things to achieve.

It’s easier to obtain a domain citation.

The next stage is brand visibility — when you’re actually being named.

That’s arguably one of the most important outcomes.

[Brief connection interruption.]

Coming back to brand visibility, when the company’s name is being mentioned, that is top-tier GEO.

But we could go one stage further and describe consistency over time as elite GEO.

It’s not easy to get mentioned by an AI platform, but it is possible to appear briefly for a week or two because of the recency bias we discussed earlier.

Maintaining citation stability and brand-mention stability is considerably more difficult.

That’s something we’re working towards.

Interestingly, I’m seeing this with our own live AI retrieval testing.

For the first nine or ten months, on particular queries, I was surfacing first across four engines above everyone else.

Now we’re getting into months 11 and 12 and some of those results are beginning to dip.

That’s the reality.

Even when you have strong evidence, another agency may publish another study that potentially supersedes yours, and suddenly they’re commanding the attention of the AI engine.

That’s where you understand that genuinely elite GEO requires effort, a protracted approach and precision.

One way I describe it to clients is this:

When somebody says, "Let’s produce ten blog posts a day," I tell them to imagine that they’re dealing with a judge in court.

You can’t simply say, "He said this, she said that."

You have to provide evidence.

You need facts, CCTV, witness statements — something that validates what you’re claiming.

A judge isn’t simply going to accept whatever you say.

You have to be factual and precise.

When I explain it that way, clients often recalibrate and realise what we’re dealing with.

In ordinary life, people can sometimes talk their way around things or distort a story slightly, but when you’re standing before a judge, you know that isn’t going to work.

That’s the way I try to convey it.

But a lot of clients still simply want results.

They’re saying: "I’m giving you X amount of money. Give me this result."

Unfortunately, it doesn’t work that way.

We can work towards the result, but I don’t want to mislead somebody and tell them it can happen immediately because that’s simply not how these systems work.

I think that’s one of the difficult circumstances we have to navigate with clients.

Absolutely.

I’ve seen it a few times.

If an agency says it can guarantee you ten citations within the first three months or something similar, I think we all know that’s nonsense.

I compare it to traditional PR.

PR also took time.

You had to build those relationships.

This is another form of that.

We have to convince LLMs that we, or our clients, are entities they can recognise and trust without constantly having to use retrieval-augmented generation and investigate numerous external signals.

It’s exactly what you said.

The LLMs are coming back to you because they know your first-party data is good quality and that you update it regularly.

Once they recognise you as an entity, your work certainly isn’t finished, but you’ve overcome the first hurdle.

That’s how I position it.

We can go after citations. Citations are great.

It’s good to know we’re visible and building recognition.

We’re seeing a lot of this now with clickless search.

Someone may see a citation and then visit your website two hours later.

But what we’re really after is the bigger picture.

As you said, elite GEO means we ultimately want your name appearing when somebody searches for something.

We want the AI to say, "Try these guys."

I can’t remember which study it was, but it essentially claimed that people trust the first result from an LLM in a way that they don’t necessarily trust the first result on a Google SERP.

Once they’ve narrowed down their questions, people often don’t look much further.

That’s something we aim for with clients.

The objective is to keep working until we’ve achieved that level of recognition.

Here’s my view of where things may go in the future.

The importance attached to appearing within AI answers could go through the roof.

Look at Google now.

When you perform a normal Google search, you’re increasingly presented with an AI Overview, and you have the option to move into an AI-style conversational answer.

The natural progression could eventually be towards a predominantly AI-led experience.

If that happens, the traditional ten blue links become significantly less important.

Then the question becomes: are you being mentioned by the AI?

When you enter a prompt, an AI engine might recommend two companies, sometimes one, perhaps four or five, and occasionally ten.

Let’s say the average is somewhere around four or five.

For a high-intent commercial query, imagine how valuable it becomes to be one of those companies when thousands of businesses may want to win that same query.

Then multiply that by a future in which traditional search results have become less prominent.

Brand mentions within AI platforms could become one of the primary ways customers discover companies.

I don’t think a lot of businesses fully appreciate the potential importance of that yet.

Then take it one stage further and look at AI agents.

They’re increasingly able to go into websites and transact or purchase things with less direct human involvement.

Yes.

So GEO also becomes important in making your website one that an AI system chooses to interact with.

It could select your website, connect with your API and purchase access to information or services.

That could become incredibly valuable.

That’s where I see GEO becoming even more important further down the line.

Businesses may suddenly realise the significance of it.

At that point, practitioners who have genuinely been working with GEO — experimenting, testing, discovering what doesn’t work as well as what does — will have built up useful baseline knowledge.

But you have to go through that troubleshooting process first.

That’s similar to what we were discussing earlier.

Absolutely.

Now is the time to experiment.

It really is an opportunity to make mistakes.

Anybody working on GEO needs to look at it that way.

Every mistake is a lesson, and we need to learn as much as we can before AI Mode becomes the default experience.

I think, as you say, that is where things appear to be heading.

Look at what the SERPs look like now.

There are fewer and fewer queries that don’t trigger an AI Overview.

We still find some occasionally.

But now, as soon as you enter the AI Overview, you can interact with it.

You no longer necessarily have to switch separately to AI Mode to begin a conversation with the LLM.

Google is already preparing users for that experience.

Google’s index may increasingly become an underlying index rather than something people directly navigate.

I’m sure people will still navigate traditional SERPs for some years, but eventually that behaviour could decline significantly.

The more high-quality information we can give these LLMs while these systems continue developing, the better positioned we may be.

And I think citations — and this is a big thing to say, so if I turn out to be wrong, I’m sorry — could become less important over time.

LLMs are already showing that they’re increasingly comfortable answering certain queries without citations.

Even with some YMYL queries involving areas such as healthcare and finance, they may name a brand without necessarily citing a webpage.

I think brand mentions could therefore become the real currency that we’re working with going forward.

It’s interesting that you’re saying that because there’s the idea of confident content.

That’s where AI engines have accumulated such a large amount of information about a particular subject that they no longer need to employ RAG to retrieve the information externally.

They’re confident they can provide the information directly to the user and still be accurate.

In its own way, that’s slightly concerning for business owners.

The system is no longer necessarily looking outward and retrieving your website to bring you into the conversation.

One thing that mitigates that to some degree is hallucination-risk mitigation.

AI engines still have the problem that they can hallucinate.

So there’s an incentive to surface answers supported by citations because that provides another level of verification.

If an answer is backed by reliable citations, the developers of those systems have greater confidence that the information is grounded.

That dynamic still exists.

But we’re talking about the level of capability AI has today.

With recursive improvement, we might reach a point where systems make considerably fewer mistakes.

If they no longer require the same degree of hallucination-risk mitigation, they may become increasingly comfortable generating answers without retrieving and citing external material.

Then God knows what we do.

I also think it depends on the type of query.

For many "what is" and "how-to" queries, I’m not seeing much RAG happening anymore unless it’s something particularly obscure.

"What is SEO?", "What is GEO?", "How do I optimise my website?" — those kinds of questions are increasingly things LLMs appear comfortable answering from their existing knowledge.

So I think it comes down to optimising for queries they can’t easily answer on their own or providing genuine additional value to the answer.

Something we’ve experimented with is a value-based click strategy.

You give people something that encourages them to click through from the AI Overview itself.

A lot of that comes down to gated assets, studies, white papers, checklists, e-books and similar resources.

As long as the LLM is confident that the asset genuinely contains what you say it contains, you’re giving people an additional reason to click through.

And because LLMs are programmed to be helpful, they’ll often provide that option.

Yes, and I think that encapsulates it.

It’s interesting because I did a podcast around the question, "Does generative engine optimisation actually work?", explaining my own journey.

One of the biggest things I’ve discovered is knowledge graph enrichment.

Again, it comes down to giving the AI new information that it hasn’t previously been exposed to.

With the confident-content scenario we’ve just discussed, the AI already believes it knows enough about the subject.

But if you introduce genuinely new information and new evidence, particularly if it’s backed by third-party software or perhaps an independent third-party website, the AI has a reason to investigate it and potentially synthesise it into the answer.

But knowledge graph enrichment requires work because the content needs to be individual and unique.

You can’t simply repurpose what’s already out there.

You have to go on an expedition to find new knowledge and essentially enrich the knowledge graph.

Absolutely.

That’s another way of getting yourself noticed by AI engines.

But again, it’s all effort.

It is.

I think information gain is really going to be the differentiator.

That’s essentially what we’re discussing with studies, knowledge graphs and new research.

It’s the nature of the beast that producing verifiable information these LLMs don’t already have is going to become increasingly time-consuming.

I think that’s going to be the challenge going forward.

The fact that we’re already experimenting and publishing what we’re finding puts us in good stead for the future, when things become more difficult and everybody has done the obvious research and tried the obvious unique angles.

We’re going to have to be increasingly creative.

That’s interesting because there’s so much discussion now about whether AI can be creative.

But if you genuinely want to produce something rankable and valuable, you need to think of something an AI hasn’t already thought of.

You need to focus on that and report on it.

I think that’s exciting.

You’ve really got to stay on your toes.

I think the same thing.

The frustration comes back to clients.

Imagine it’s a construction client.

You might say: "Look, you need to record a video of an installation. You need to talk about what you’re doing. You need to give me photographs."

Then I’ve got all of that evidence.

I can put it onto a website page, support it with transcripts and connect it to the company’s other projects and areas of expertise.

Now you’ve created an entire ecosystem of fresh, evidence-based information from the real world.

But the client is running a business every day and has countless other things to do.

They might understandably think: "Do I really have time to do this?"

Ultimately, though, I think businesses are increasingly going to have to do it.

I think this is going to become an industry in itself, although many people don’t fully realise that yet.

There may eventually be little choice because AI engines increasingly need those kinds of evidence signals before recognising and surfacing businesses.

I think that’s right.

It’s that and proximity.

If you happen to be close by, then that’s great.

But outside of that, exactly as you said, everybody is going to have to put in the work — which is good for you.

Yes, and there’s also the importance of truth.

That’s one of the things I like about AI engines.

I think it’s becoming harder to rely purely on marketing spiel.

You can still get away with it to some degree today.

I see companies making very large claims about the volume of work they’ve supposedly completed, and sometimes when you calculate what that would actually require day after day, the numbers seem questionable.

Yet those companies can still get cited.

So AI certainly isn’t foolproof yet.

But in many other areas, I’m seeing systems getting better at identifying stronger information.

As these systems continue improving recursively, we may eventually reach a point where they can distinguish much more effectively between marketing claims and genuine evidence.

A lot of empty marketing content could become significantly less influential.

What remains valuable would be precise, accurate, honest data and information.

That excites me.

There’s so much content online where it’s difficult to know what is genuine, what is paid for and what is simply marketing.

If AI systems can increasingly reward factual, evidenced information, I think that’s a positive direction.

It’s the great leveller.

It definitely is.

I think we’ll bring things to a pause there because we’ve done around 45 minutes, and I’m sure you have lots to get on with.

Rhiannon, sincerely, thank you so much for coming on.

I really value your expertise.

It’s been fascinating hearing about your experiences in South Africa, your work representing clients in America, and the testing and research you’ve been doing.

It helps me, and hopefully it helps everybody listening and absorbing this information.

So thank you so much. I really appreciate it.

Thank you for having me.

It’s been really great. I’ve learned a lot as well.

Let’s see where all of this goes.

Thanks very much.

No problem at all.

Thank you so much indeed.

God bless. I’ll see you soon.

Take care. Bye.

Rhiannon Cecil:

Bye.

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