What happens when you feed years of messy content into AI? In this episode, Bill Swallow and Alan Pringle dig into the content debt crisis, including increased system costs, neglected localization, and the fallout of “just use AI” mandates. They share practical insights to help organizations get back on track.

Alan Pringle: Is your content updated? Does it reflect the latest information? Is it created for all the different locales that your company serves? Is it in different languages? That is another pile of debt that when you start looking at AI, all the problems will be very brutally magnified, and you’re going to have to address them to really have a large language model that works at all.

Related links:

Balancing automation, accuracy, and authenticity: AI in localization (podcast)
Forbes: AI Costs More Than The People It Replaced
Taming AI: Using AI for content conversion at scale (podcast)

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Transcript:

Disclaimer: This is a machine-generated transcript with edits.

Introduction with ambient background music

Christine Cuellar: From Scriptorium, this is Content Operations, a show that delivers industry-leading insights for global organizations.

Bill Swallow: In the end, you have a unified experience so that people aren’t relearning how to engage with your content in every context you produce it.

Sarah O’Keefe: Change is perceived as being risky; you have to convince me that making the change is less risky than not making the change.

Alan Pringle: And at some point, you are going to have tools, technology, and processes that no longer support your needs, so if you think about that ahead of time, you’re going to be much better off.

End of introduction

Bill Swallow: Hi everybody, I’m Bill Swallow.

Alan Pringle: And I’m Alan Pringle.

BS: And today’s episode is going to focus more on the content debt crisis, AI edition.

AP: And it’s also going to be the complaint edition, surprise, surprise, because there’s a lot of things in the AI world right now that are still making me cranky. And I am sure we will talk about them at some point.

BS: Yeah. So with the rise of AI, I think it’s kind of holding a microscope to a lot of the technical debt that we’ve been seeing over the years in content operations in general, whether you have outdated authoring formats or content that’s not being updated on a regular basis, new delivery formats not necessarily meeting the needs of the users, and so forth. And all of that is kind of compiling or snowballing into a bigger problem once you start feeding all of this stuff into AI.

AP: Right. And it’s interesting to me how everyone’s talking about AI as being this productivity tool. In a lot of ways it is, but that’s not what the focus of this is. In a way, it is also sort of a consultant for you. As you just mentioned, Bill, when you start looking at AI and delivering it, treating it as a delivery endpoint for your content, a distribution endpoint, you are going to start to discover that your processes on the back end for creating and distributing your content are not what they should be. So it’s kind of like this consultant saying, Hey, you need to do better over here. And that is where a lot of this debt is coming from, from my point of view.

BS: Mm-hmm. The unfortunate part of that consultant is that it’s not offering advice on how to fix it, but it certainly is pointing out the issues.

AP: It’s like, “This is screwed up. Full stop.” So, I mean, part of why we’re here is to talk about some of those kinds of debt. And let’s just start with one technical debt. And I’m saying technical in the sense of the way that you perhaps use software to put together your content. Let’s kind of focus on the content operations world.

BS: Mm-hmm.

AP: For example, if you are delivering content via unstructured desktop authoring tools, of which there are many, and you can templatize things and make your content seem more consistent, but there’s a problem with a lot of desktop published or content generated from the desktop publishing world. It’s more focused on look and feel and fit and finish, particularly if you’re delivering for PDF. And yes, people are still doing that. So there’s a lot of time and effort spent on that look and feel, that fit and finish. And frankly, that time should have been invested in adding intelligence to the content to explain, you know, things under the covers about what user is this for? What is the model of this particular thing? All of that kind of metadata, that kind of categorization. Desktop publishing, at least from my point of view, doesn’t really do a great job of helping you catalog that kind of stuff. So that’s a problem.

BS: No. It is a problem. Also, with desktop publishing, you can kind of confuse AI a bit if you’re using desktop publishing inconsistently. So if you’re using formatting tools to override formatting to make things look like headings or make certain paragraphs look like children of another paragraph, doing those manual finesses is great for print because, as you know, most people will look at that and understand the hierarchy of information, understand what’s going on with the content that they’re reading. But anything digital isn’t necessarily going to pick that up.

AP: Right. Yeah.

BS: Especially if you’re looking at something like bare bones HTML, if you’re using CSS to override the size and prominence of a standard paragraph as opposed to using a heading, that is not necessarily going to be picked up as a heading, even though a reader would actually see that as a heading while looking at the HTML page.

AP: Right. What a human reader can figure out from formatting cues, if those cues aren’t set up in a way that a large language model, a computer, can understand, there’s that huge disconnect, and that’s where that debt starts piling up. And then another angle here in this content creation world, if you are using multiple different tools to create your content, there’s a good chance that content under the covers is not going to be processed the same by a large language model. So there’s another deficiency right there on top of that. And this is very common, for example, if you have had mergers, acquisitions, and you’ve basically created a larger company from many different companies. And they all still have, especially in legacy content, things created the “old way,” and all the old ways start to pile up and cause problems because your large language model can’t properly basically figure out what is the heading in this particular chunk of docs versus what it is over here. So it can’t parse it as well. And again, this all goes all the way back to the way that you created that content. And it’s a clue, hey, you need you need to fix this. And I I think beyond that more technical, the way that you create it, there also there are also issues with the content itself.

BS: Mm-hmm.

AP: Is it updated? Does it reflect the latest information? Is it created for all the different locales that your company serves? Is it in different languages? That is another pile of debt that when you start looking at AI, it’s gonna be all the problems in regard to that are also gonna be just very brutally magnified, and you’re going to have to address them to really have a large language model that works at all.

BS: Yeah, because not only do you have the technical debt on the source content side and on the published content side, but you also now have technical debt growing on the AI side because you need to spend more time and energy refining the how that model works with your content in order to achieve the correct results.

AP: Right. So you’re having to do a lot of overrides and we I don’t know if overrides is the right word, but you’re having to do a lot of additional processing and figuring out so it will parse things cor...