Everyone is talking about AI lately, so we pulled together some clips from recent Tech Forum sessions all about how to make sure your books get discovered in the age of AI.
(Scroll down for the transcript.)
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Further reading/listening
- Get discovered: How to use your metadata to conquer AI search webinar
- Get discovered: Adapt your marketing for the AI search era webinar
- AI use across the North American book industry 2026 report
- How the Canadian book industry is approaching AI webinar
Transcript
Ainsley Sparkes: Hello and welcome to the BookNet Canada podcast. I’m Ainsley Sparkes, your host for this episode.
There’s a lot of talk about AI everywhere you look lately. It reminds me of how much we were all talking about ebooks in the early 2000s and how much learning we were all doing about EPUB and selling digital books. How were ebooks going to change the publishing landscape. And I remember attending BookNet’s conference, Tech Forum, years before I worked here to listen to the experts they’d gathered on stage to share what they’d learned and provide some guidance.
And now, some 20 years later, Tech Forum is still providing those experts and guidance, albeit online, about how AI might change how publishers operate. In September 2026, the Tech Forum team ran two webinars both about how to get discovered by AI. Tricia McCraney, Head of Account Management for North America at Virtusales Biblio, lead the session about how to use your metadata to conquer AI search, and Ariel Hudnall, Managing Director of Serif and long-time literary marketer, talked about adapting your marketing for the AI search era. I’ve pulled out clips from their presentations to give you a glimpse of the content in each.
Before we get to them, though, if you want to know more about how the book industry is using AI, you can read the newly released results from the AI use across the North American book industry survey. BookNet Canada and the Book Industry Study Group partnered to take the temperature of AI adoption, use, and concerns in the book industry. It’s an interesting read, and Tech Forum will be running a webinar that shares the Canada-specific data on October 27 you can sign up at the link in the show notes if you’d like to learn more about that.
Ok, let’s hear what Tricia McCraney had to say about metadata and AI.
Tricia McCraney: So, as many of you know, who are on this call, the book industry is always facing some kind of crisis. It’s been reshaped roughly every decade, and each…and I’m starting…when I’m thinking about that, I’m going back to the 1970s. I wasn’t working in the industry then, but that’s sort of a good starting point if we think about how the modern book industry came to be. And each new change or shift across those decades has really one thing at the heart of it, and that is that discovery or the way that our readers find books has moved further from the physical book, from that tactile experience of holding the book in your hands, and deeper into the information that we provide or the data.
Today, AI is in place, and it’s…really, this is not the first time that we’ve had machines influencing the supply chain. So, the lesson for us is that what do we need to do? We need to keep on doing what we do well. And across those 25 years, the best practice has really been the same, and that is to maintain complete, comprehensive, structured data that’s consistent and accurate. And there’s a little more focus on what data we’re providing now. So, really, what we need to be thinking about is this lesson that it’s important to own your data and also supply it early. Owning it means supplying it early. If you’re the first one to supply it, that’s the best case scenario.
So, just to set the stage, there are broadly three types of metadata. And this classification here that I’m talking about really comes from our friends in the library and information science world. They love taxonomies, maybe even more than we do as publishers. But there’s descriptive metadata, structural metadata, and administrative metadata.
Descriptive metadata tells us what the thing is. So, what is our product? There’s a title, a subtitle, there are contributors. It describes subjects, categories, classifications, and the language that the book is published in, for example.
Structural metadata is really how the parts of the thing relate to each other. So, this is where we start getting into chapter-level data, page count or extent of the product, the table of contents, EPUB navigation, and even some accessibility features. Some of this data is data that we are supplying for the first time or newly supplying or recently supplying.
Administrative data is metadata that describes how the thing, your book, is managed. So, this is where we’re now…this is entirely new. So, we have the core descriptive stuff, the structural stuff that we’ve been starting to supply and are doing to some degree or starting to, and the administrative data, which is where we’re talking about rights, including for AI, how our data can be used by AI, if at all. It’s managing stuff like technical specifications. So, things like some of the accessibility features I mentioned before, but also things like the actual spec of the book, the physical spec of the book, the production information about the book.
And then the last thing, when we’re talking about administrative data, is the provenance. And that’s what I’ll talk about a little bit more throughout this presentation is where is the ownership in the details of the book. So, we’re almost going from a more rights-based and production-based view than we have before.
As you can imagine, the descriptive metadata is where we’ve put all our focus in the past. It’s kind of the attention-getter. Structural metadata, just to sum it up, is what makes our navigation and accessibility work. And the administrative stuff is where that AI piece of describing any AI attribution lives, as well as the terms of AI acting on our titles.
Okay. So, what does this mean in the real world? If we think about metadata and search for our end user, our readers, the biggest thing is that, now, the search box is an answer engine. So, the people who we want to buy our books, who were typically searching for them in retailer search engines before, are now chatting with search engines. So, the behavior has changed. If we talk about SEO versus AI in search engines, we need to start thinking about putting full sentences and phrases in our keywords, not just individual words. And I know phrasing has been a thing before, but it’s actually more of a question that we’re answering than just putting in data that helps someone get to the end result.
Matching and ranking that happened in SEO — so, where your search terms returned a list of links or possible results — has now moved to semantic retrieval. And that means not just matching and ranking, but AI is making a decision about what to display back to someone using a search engine. So, instead of that list of links, the users, us as readers or our end users, are getting a synthesised answer.
So, practically, what that means is that if someone is asking what should I read next or what would my relative who’s a 12-year-old who likes graphic novels like to read next, they are now typing that full question into a search bar, not just typing in the keywords that might form that question. And the search functions on retail sites now have AI layers acting on them. And so, the results that come back are not always just a synthesised answer, but you might have seen the experience. I haven’t seen this on our major book retailers yet, but you might have had the experience in other sites where results come back and they sometimes prompt you to refine the results that come back. They ask you more, just like if you’re chatting with an LLM, Claude, or one of your language models. Just in the same way, it might just ask you more questions to refine those results. So, your data needs to be able to respond to these questions and not just serve up individual data points like categories and classifications.
Now, what happens in search or what’s happening in search right now is a little bit different than what’s happening in the supply chain. For suppliers — so, those recipients of your metadata — their systems are really looking at everything. So, suppliers will mine your data, and that’s always been the case. They’re already looking at your data and extracting whatever you send, even if we can’t see it pushed through onto a product page. They’re using that data in meaningful ways. But the way that categorisation is happening with suppliers is shifting. So, they now want — and in some cases, need — to know more about how your books are made.
And what’s important for us to know as suppliers of metadata is that any records that don’t have enough data, so that are sparse or thin, can get interpreted poorly. They basically read badly in AI, and then we have the risk of AI hallucinating or making something up. And the same is true if data is contradictory.
So, in this current area, in this world that we live in now, your metadata is being interpreted and not just displayed. That’s kind of the takeaway here. Supply chain systems used to take your data and pass it through, and you would kind of evaluate what you sent based on what you saw on individual product pages and different sites. But now, that data is being evaluated against itself, your own records and other records. And the systems and suppliers are making decisions about what the book is and who it’s for.
Ainsley: That’s a brief clip from Tricia’s presentation, but I highly recommend going to watch the whole thing as she provides really great practical tips for how to do your metadata right for AI discovery. And speaking of AI discovery, Ariel Hudnall joined us for a complementary session about AI discovery from a marketing perspective. Here’s a bit of that session.
Ariel Hudnall: So, discoverability has changed a lot in the last three years or so, but particularly with the advent of AI search. There’s a number of acronyms we use here, which is AEO, GEO, AIO, and, of course, our old favourite, SEO. But AI in particular has disrupted how we discover things, and it’s affecting you already, whether you know it or not, and whether you’re using AI or not.
So, does this mean everyone has to use AI? Is that really the solution here? Well, we can go ahead and do a thought experiment.
So, Bynder, a marketing survey company, which I’ve linked to in my slides, which you will get after this presentation, conducted a survey. They asked a thousand people to look at two articles. One was AI-generated and one was written by a human. Forty-five percent of these participants couldn’t identify the AI. Fifty-five percent could. And interestingly, because AI use is more rampant in the U.S., U.S. Americans were 10% more likely to identify that AI. So, people are, as consumers, really learning to spot AI. And what does that mean?
So, at first glance, the survey results are fairly interesting. Fifty-six percent of the participants preferred the AI-written article. So, does that mean the AI-written content was better? We can’t really make the judgment from the survey, but what we do know is that this result was only when the participants were not told which article was AI.
When asked about how they feel about content they suspect to be written by AI, the respondents were much clearer. Fifty-two percent will become less engaged if they suspect AI was used. So, we’re going from 56% preferring the AI written article to 52% saying, basically, if I clock AI use, I’m 52% more likely to distrust it and move away from it.
So, the key word here is suspect, because maybe some of you have also had this experience. You might not be using AI at all, but you’re getting comments, this is AI-generated. So, suspect is the key word here.
So, where are the trust gaps when you’re thinking about using generative AI? I put these in order of better trust to least trust. So, clarity is the one place where currently AI purportedly excels more than humans, and that’s in organization, outlining, and structure. So, basically, 82% of a different study with the International Journal of Science, Strategic Management and Technology found that AI-organised content was clearer, easier to understand, helped them get to their answers more quickly. So, excels in this particular space.
However, in terms of actual trust — so, do they believe what the AI is telling them — only 50% of participants did. So, that kind of goes back to that 52% when they know it’s AI, they’re less likely to believe the outcome.
For engagement and particularly CTAs, which is calls to action, human content performed better. Seventy-eight percent of human content was more engaging, more likely to result in a click-through. But it was 52% for AI. So, it works, but only marginally. And I think, really important here is this is one data set by itself. When we’re thinking about this in the context of, for instance, an ad where you only have a 3% click-through rate to begin with, AI’s additional barrier changes that number, too. So, they correlate in many ways. And you should expect that, in some cases, it may improve your chances, but in many cases, it may not.
The worst performances for AI came down to authenticity and emotional depth, which is unsurprising. When it comes to being authentic, human content wins. Less than perfect is better than AI. Typos, better than AI. For emotional depth, also incredibly important. As you’ve probably guessed, AI’s emotions are fairly shallow in the sense of it can perform happiness to an average, but it doesn’t understand give me happiness with a bittersweet undertone or something like that. So, emotional depth can be in particular a hard thing for the AI to replicate in a way that consumers find natural.
There is one more stat that I think is actually more important, and that’s how customers are using AI to understand what to buy. So, in fact, through a study with Reuters, 54% of users that have been recommended a product by AI, because AI is crawling everything, are more likely to convert them to the product that it suggested. And additionally to that, they were 53% more likely to spend more time on the website where that product was being sold.
So, if you’re being indexed and surfaced by a chatbot to say, this is the book to get on this particular topic, not only is that consumer more likely to click through, you have a less of a barrier to entry. They’re also going to maybe spend a little bit more time on your website, learning more about you and what you publish. So, thinking about product pages and are those related books at the bottom really related? Is there a way that you can, for instance, join the mailing list on every product page? That kind of thing. So, thinking about that kind of stuff can really help with further conversion.
But ultimately, what all of these stats are saying together is that the trying to be human with AI is the issue. Consumers like AI to have that impartiality, even if it’s biased and sycophantic. When they feel like AI is being unbiased, they’re more likely to respond positively.
Ainsley: Ariel’s entire presentation is worth watching to get of sense of how AI is not just changing search, but also ecommerce and consumer behaviours and what to find out what publishers can do to make sure their books get seen.
Thanks for listening to this month’s episode! Before I go, I would like to take a moment to acknowledge that BookNet Canada’s operatons are remote and our colleagues contribute their work from the traditional territories of the Mississaugas of the Credit, the Anishinaabe, the Haudenosaunee, the Wyandot, the Mi’kmaq, the Ojibwa of Fort William First Nation, the Three Fires Confederacy of First Nations (which includes the Ojibwa, the Odawa, and the Potawatomie), the Métis, as well as the unceded and ancestral territory of the Musqueam, Squamish, or Tsleil-Waututh peoples, the original nations and peoples of the lands we now call Beeton, Guelph, Halifax, Thunder Bay, Toronto, Vancouver, Vaughan, and Windsor.
We encourage you to visit the native-land.ca website to learn more about the people whose lands you are listening from today. Moreover, BookNet Canada endorses the calls to action from the Truth and Reconciliation Commission of Canada and supports an ongoing shift from gatekeeping to space-making in the book industry.
We would also like to acknowledge the Government of Canada for their financial support through the Canada Book Fund, and of course, thanks to you for listening.
