Content Operations

In this podcast, Sarah O’Keefe and Carlos Evia discuss their upcoming book: The Uninvited Author: AI, Intent, and the Future of Content.

Carlos Evia: One of the things that we really want to invite people who end up reading this book to do is engage critically with AI tools across the text cycle. It’s not magic. It’s a collection of ugly computers in water-chugging data centers that work primarily on predictability approaches. And I truly think that we need to be critical in our engagement with AI as authors and as readers of content.

Related links:

Digital sovereignty in the age of AI

From ad hoc to autonomous: The AI content ops maturity model

Subscribe to our newsletter for updates on Sarah O’Keefe and Carlos Evia’s book: The Uninvited Author

LinkedIn:

Sarah O’Keefe

Carlos Evia

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

Sarah O’Keefe: Hey everyone, this is Sarah O’Keefe. I’m here with Carlos Evia. Hi, Carlos.

Carlos Evia: Hello!

SO: So, Carlos, for those of you who don’t know, is the co-author of this book that we’re working on and also professor at Virginia Tech. His official title, his official titles are Associate Dean of Strategic Initiatives at Virginia Tech, and he is also the CTO of the College of Liberal Arts and Human Sciences. I think, Carlos, that means you’re the computer guy in the humanities world.

CE: That means that people come and complain to me about curriculum and also about their computers. So yes.

SO: Okay, cool. And so with that, Carlos and I have written this book. And in this first episode to this special series, we wanted to give you an overview of what it is that we’ve done, or what it is we hope we’ve done, we are trying to achieve here. And probably the easiest way to do that is actually to break down the official book titles. So what is our official book title?

CE: The Uninvited Author: AI, Intent, and the Future of Content. And I don’t know if I got it right from memory.

SO: No, I think that’s right, only because I wrote it down. So I’ve been staring at it nonstop for, you know, four to six months now, but still. 

CE: Yeah. yeah. I see it now. Ha ha ha.

SO: You know, can we remember our own book title at this point in the writing process? And we should tell you that we’re recording this as we’re approaching sort of ninety percent done, which means that we still have approximately ninety percent of the work to do. And we’re a little punchy.

CE: My co-author today woke up being very optimistic about progress on the books. But I agree, 90% sounds accurate. Yes, I’m not gonna argue.

SO: Yes, you’re well, your co-author is slightly delusional, so we’ll just set that aside and move on. All right, so let’s talk about this. The main title is the uninvited author. So who is the uninvited author? What are we talking about here?

CE: Well, the uninvited author, surprise, is the AI author. We have seen how AI has been taking over the generation of content at different types of content, content for marketing purposes, content for technical purposes, content in video, in text, in audio. And in some of those cases, it’s creeping into people’s workflow without an invitation. And now, and we know people, I mean, across the different facets of content work that now are, on purpose, inviting AI agents to contribute or to take over and start driving the car of the content operations. But particularly for the type of content that we do in our little corner of technical communication, AI, authors kind of started coming as uninvited guests that will appear when a user will use a chatbot, say going to ChatGPT or going to Claude asking how do I do this instead of going to the published official manual of the name of the appliance or name, the software application. 

And what we realized is that what was happening is that as the company, I write my documentation, I put it on the website, I put it on a book if I’m in 1985, and I give that to my users. And I hope that that’s what they use. I am the author. I bring it to you. But now when a user prompts a chat button and says, how do I turn on this machine? How do I save a file in this software application? 

Now there’s a new author that is going to give information that might be accurate, that might reflect what I wrote, or might be inaccurate. It might be, like you said, delulu and inventing some other content. So that is the uninvited author. An author that is already here and is adding to what other humans have created or written or flat-out making up stuff out of nowhere that didn’t exist and that was not created by humans before.

SO: And I think that this this theme of loss of control for the humans returns in maybe every page of this every page of this book. So we have the AI that is now delivering… well, it was delivering content, right? And when you had a search engine, you would ask it a question and it would return answers, but in general, those answers were, “Here’s what I found in this document,” or “Here’s a collection of links,” or whatever. The what’s new is that the AI is synthesizing answers from its sources, and we don’t know what those sources are necessarily. Sometimes they’re cited, sometimes they’re not. 

I asked the chatbot something the other day, and it said it was something very straightforward like how old is somebody, like famous person. And it gave me an answer, and I said, cite your sources. And it said, this is just general knowledge. I’m sorry. How is this general knowledge? It it would not and then it said, but you know, it got kind of passive-aggressive. It was like, well, if you insist, I can give you a couple of websites that list this kind of information. but essentially it was like, how dare you question my knowledge of just the general universe? Well, I I use computers for a living, so I question everything.

All right, so the uninvited author is the AI, and now the chatbot is authoring content, accurately or not, whether we like it or not. So our subtitle then is AI Intent and the Future of Content. When we look at AI in the context of content, what are we looking at here? How does AI get involved in content operations, in the content process?

CE: Well, we have been chatting, ha ha, we have been chatting about chatbots so far, but the chatbot, it’s really only scratching the surface of what AI already is for content operations. I don’t even want to think about right now, let’s save it for the next section of the title of what it’s going to do or what it could do.

CE: And the chatbot is the easiest representation of it. And yesterday I was teaching, I’m teaching this semester a senior seminar in communication on the topic of what AI is doing to the profession of communication, for good or for bad. And something that my students tell me is that they use ChatGPT or a similar chatbot as a replacement for the Google search box. So they go there and they go ahead and ask and prompt without prompting, ask, they query things that up to two or three years ago, people would just ask Uncle Google.

SO: Now, in their defense, it’s getting very difficult to actually find the search box. 

CE: If you go to Google, yeah, you don’t know what’s Google, and you don’t know what’s Gemini. They’re all married and combined together. And I think that that is the very interesting part. But the chatbot in that box that resembles the Google Omni box is only one component of what we mean by AI. AI comes in many other flavors that I think my co-author in this book even has a fancy table inserted on page number here about the different forms of AI engagement in content operations. And at the end of that spectrum, we have agentic approaches to AI in which you, as the developer or you as the author of the content, are going to be sending out these AI agents to be taking care of processes or supporting processes that human beings are doing throughout the life cycle of your content. 

So I want to emphasize that what we’re talking about here is not just ChatGPT, not just Gemini or even Claude, but many in-house developed and maintained in a little or not so little computer in one organization implementation of not just a large language model, but many other machine learning and data mining approaches that could create something that smells or looks like artificial intelligence beyond basic automation. So AI means many things, and we have more than one section in the book that tries to address that AI is many things, even though for the majority of people who probably are listening to this AI is ChatGPT in that box.

SO: And one of I think one of the really tricky parts about this is that, as content people, we see AI in lots of different places in our in our job roles. So what I mean by that is that mean, we’ll start at the end. The content consumer, it used to be get your content on the website, do some search engine optimization, keywords, whatever, make sure that people find your content. Or you might be privileged that your content is ships with the product as online help or a companion document. Again, if it’s nineteen eighty five, but you know, it’s the supporting content that goes with the content, or you’re working on knowledge bases, that type of thing. So as a content creator, I’m thinking about what does it mean

SO: To get my content in front of the end customer. And if that is now mediated by a chatbot, how do I make sure that it survives the trip through the chatbot so that the content, the instructions, the information still reaches that end user instead of them getting, you know, something potentially inaccurate? So that’s the front end, right? There’s also the issue that LLMs and chatbots have the ability to create what we call synthetic content. Content that doesn’t previously exist, but when I ask the chatbot for it, it gets generated. There are lots of examples of this, but machine translation is one. Hey, can you give this to me in a different language, please? And it says okay, and it magically machine translates it and gives you what you’re asking for. Make it shorter, make it longer, give me more information, give me less information. Write it in a different style. Those are all cases where the chatbot is intervening and putting its own layer on top of that. And then as you work your way backwards, there’s authoring assistance, you know, fix my grammar, refactor my sentences, that kind of thing. And as a content creator, the availability of AI across the entire what you know, the text cycle.

The process of creating and editing and managing and delivering content, it affects every single one of those pieces. And we have to really think carefully about how and where and whether or not to integrate that into content operations. What are appropriate and inappropriate ways of doing that?

CE: I think you said something very interesting when you refer to a magical process. The assumption is that a magical process happens and then voila, you have something that the AI agent or the AI chatbot generated or assisted in the generation. If we go back to that separation of the backend or the front end. And that’s where I think that one of the things that we really want to invite people who end up reading this book is to engage critically with AI tools across the text cycle. And we haven’t even talked about the concept of the text cycle, which is a major component of the book, because it might be that you, as an author or you as a content reader, as a consumer of content, prompt a chatbot for information on how to do this, either to write something or to read something and solve a problem. But it’s not magic. It’s a collection of ugly computers in water-chugging data centers that work primarily on predictability approaches. And I truly think that we need to be critical in our engagement with AI as authors and as readers of content and not just about technical content. If you’re doomscrolling on Instagram, and let’s not even mention TikTok because I don’t use TikTok, I’m very old. And you see these AI sloppy videos from dozens of people fighting in like every Mortal Combat. Let’s ask why…

SO: You seem to know a lot about this for someone that doesn’t use TikTok.

CE: I doomscroll a lot. You’re like, what is this, and what is happening behind the scenes? How do we think critically about the concept of AI as it becomes an invited or uninvited component in communication processes? And it’s not magic. It does things fast. Sometimes it does it in an accurate way. The models are getting very interesting, and they tend to be way more accurate than they were just a year ago. But I think that’s an invitation that engage with this critically. And I want to peddle this book as an approach for people in our profession, particularly in technical communication, in content operations, to read this and start thinking in terms that allowed him to expand that idea of this is the oracle. I will post a question, and I hope I get something good in return, and my life can be better.

SO: Yeah, I think one of the most useful things you can do with AI is sit down and ask it a question about something that you are an actual expert on. That might be a hobby in your life. It might be that you have an interest in, you know, eighteenth century literature. It could be a lot of things, but pick something and ask the AI a question about something that you truly know a lot about. And pay attention to the results you get, and you know it’ll say, would you like me to give you more information about something? And just kind of follow that trail for for a bit. What I found in doing that is that nearly always the first answer, the high-level answer, the sort of overview is pretty accurate. And the farther I chase it, the worse it gets. As I ask it for more and more detail, the details degenerate. It’s still very confident. But it starts just going completely off the rails. Now, then you try the same thing with a topic you don’t really know anything about and you’re like, well, this looks pretty accurate. So essentially, it will get you to a sort of mediocre level of expertise, but if you need to go deeper than that, you run into all sorts of problems. So intent. We have intent in the title. What is intent, and why do we care?

CE: I’ve been thinking about this for a very long time, and to a degree, this is something that I wanted to write even back in the early 2000s, not about AI, but in the context of single sourcing of content and how we were embracing, in the early 2000s, approaches for reusing pieces of content that will appear in different deliverables and will adapt on the fly to the needs of different audiences or different versions of a product or something like that. I started thinking about in the tradition of, I’m gonna say a bad word that Sarah doesn’t like: hermeneutics, which is the study of interpretations. 

SO: Boo.

CE: And it’s not Herman Munster. Hermeneutics. As an author, as a human author, I write something, and I imprint in that something my intentions, my intent. I want to say something. If you compare it to the rhetorical tradition, that’s your purpose. You have a purpose when you send something out to the world. You have an intention when you put something out in the world. And at the other end of the spectrum of the communication model, there will be somebody who, up to a few years ago, it was most likely a human being and not a machine, who was going to receive that message that you were sending with your intentions and was going to make an interpretation of said message based on their environment, on their cultural background, on their languages, and extract something that will make sense for them.

So that is what we’re talking about here when we mention intentions or intent, because surprising nobody, if we go back to that communication process that today includes the human author, the human reader, and the possibility of a human, sorry, not human, of an AI author or co-author in an AI reader or assistant to the human reader, well, those AI players bring their own intentions. And you mentioned this when you were talking about where is the information that they have coming from? And there are many layers of the training data, models that go out and perform web searches on the fly, models that are looking at your memory and your previous interactions with them, all of those components create the intentions of these AI authors or readers. So we think that intent or intention is a very complicated topic when it comes to AI in content operations, because it can really take things on a completely different path of what that traditional approach of a human talking or a human writing to another human is conveying. But at the same time, also, intention is a good thing from the author’s perspective, because if I imprint my intention and I set some guardrails on my communication processes, that might be my only hope, my only way to battle the production of AI slop. 

That is completely taken over by the intentions of the “AI authors.” And I am still not super comfortable calling the AI authors, even though in the book we call them the AI authors. And that’s what we mean by intentions. It’s going to that very basic fundamental concept of I write something, I publish a video, I publish a picture that I took; I have a purpose, I have an intention. What is my intent in publishing that? And what happens when that message gets to a human receiver and that human receiver makes the interpretation out of it? That cycle is truly affected by AI players at all points in the cycle.

SO: Yeah. And then, you know, the last chunk of this is the future of content, which is, you know, pretty much what we might expect. I think I’m gonna wrap it up here because this is getting too long already. We have a lot to say, and much of it we’ve captured, but we’re trying very, very hard to provide a roadmap to, you know, as Carlos is saying, to engage critically with AI, with the tools that are now available to us as authors, as content creators, as content consumers, and to understand, you know, what does it mean to use AI? What kind of biases does AI introduce? We’ve seen a lot of discussions about, it uses too many em dashes and, you know, now I can’t use em dashes anymore because people see that as a proxy for AI-written content. 

But what else is it introducing? What kinds of biases are in the training data? What are the ethics of using data that is largely English-centric, biased towards things that are available online, not just biased towards if it’s not available online, then the AI can’t consume it, right? Which is why they’re buying up books and shredding them and scanning them wholesale, which is a whole other thing. 

CE: Mm-hmm.

SO: But the digital footprint of what an AI has ingested is different from reality. It’s the AI’s reality. And then you have to ask the question of how far does that deviate from actual reality? And does it do so in predictable ways that we can potentially remediate? So we’d have a fair amount of discussion about algorithms and bias and what it means to work in an environment like this and, you know, are there ways to remediate it? Are there ways to bring this under control? And then finally talked, you know, a fair amount about what it means to live in a world where your chatbot has access to, you know, truly a stupendous amount of information, which means it’s at your fingertips and you can potentially automate and agentify these workflows. what does that mean? Our last sort of section is currently called the next 30 years. We’ve debated changing it to the next 30 months, or I feel like the next 30 days is the most accurate way to portray it. But we are trying to step back and take that longer view of where is this going and what are the implications of all of what’s going on here? So with that, we are mostly done.

CE: Optimistic.

SO: Our goal, our plan is that this book will be available by the end of October, which is to say for LavaCon. if you want to get more information about it, I would say follow this podcast series or sign up for our newsletter. We’ll be sending out updates that way. And I expect there will be some fun special offers as we launch. We expect this thing to be in the vicinity of probably 300 to 350 pages. And we’ve tried hard to make it an actually fun and entertaining read as opposed to something that you have to slog through. And I hope that you will find information in there that you currently cannot get from your chatbot

CE: And it’s not written by AI, it’s written by us in painful writing sessions and lots of online meetings and what have you.

SO: We have suffered greatly, and I would really like for people to read this stuff, and I don’t know about agree with, but at least engage with and have that conversation. I am actually much more interested in finding out what other people think about all these developments than I am in asking the chatbot. So so I hope it’s useful and I look forward to being done.

CE: Me too.

SO: That’s the most exciting part of all of this. We’re gonna do a couple of these podcasts to talk through some of the pieces and parts that are there. I would like to state for the record that we are not going to generate synthetic podcasts off the book content, although we totally could. 

CE: Tempting, tempting.

SO: Not very tempting, but it is possible. Given the shape that my voice is in this week, it’s actually, yeah, the listeners might have appreciated that. But I think that’s it. We’ll see you on the next one. And Carlos, thanks, and I’ll talk to you in our next torture meeting.

CE: Thank you very much.

SO: Okay, bye.

Conclusion with ambient background music

CC: Thank you for listening to Content Operations by Scriptorium. For more information, visit Scriptorium.com or check the show notes for relevant links.

The post The Uninvited Author: AI, intent, and the future of content appeared first on Scriptorium.

What are the limits of automation and AI in content systems? In this episode, Sarah O’Keefe and Bill Swallow dive into traditional formatting, workflow challenges, and what happens when AI is introduced.

The limit of automation here is not really the automation. It’s the people who say, “I don’t like the way this looks, and I’m gonna find a way to fix it. I know all sorts of ways of bending this stuff to my will.” If you’re generating content at scale, you can’t afford to do any of this stuff.

Related links:

Digital sovereignty in the age of AI

From ad hoc to autonomous: The AI content ops maturity model

Subscribe to our newsletter for updates on Sarah O’Keefe and Carlos Evia’s book: The Uninvited Author

LinkedIn:

Sarah O’Keefe

Bill Swallow

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

BS: Hi, I’m Bill Swallow.

SO: And I’m Sarah O’Keefe.

BS: And today we’re gonna talk a bit about automation, and more specifically what the limits are for automation.

SO: Yeah, they let us out of our cages. And so here we are. And I think that in this world of  I’m not going to get through the first sentence without saying AI. In this world of AI, we are suddenly facing, you know, increasing automation across every facet of everything that you could imagine. And I think it’s important to take a step back and talk a little bit about limits and what automation cannot do and maybe more importantly, why it cannot do certain things.

So before we get to, you know, the AI in the room,  Bill, what are the limits when we talk about generalized publishing in the pre-AI world where we’ve done a ton of formatting automation and we talk about all those kinds of things, what are some of the limits that we’ve run up against in terms of automation?

BS: Yeah, automation is something that we’ve been doing quite a bit of  for the past, what, twenty years, I think, or more. I’ve only been around for thirteen years. I’m thirteen years young,  so…

SO: Yeah,  this is lies.

BS: But no, a lot of our work around automation is around publishing.  That is, I think first and foremost, the most common case.  And with automation and publishing, you can save a lot of time and save a lot of effort, but a lot goes into building that capability because you can’t really work around edge cases or you know, fancy hand-cobbled formatting,  hand page breaks,  all of that thing. You have to kind of rely on the software to understand the content that you’re feeding it and it’s going to produce something that you know is a finished product: a PDF, a web page, what have you.  And you know, if you deviate from the structures and conventions that it’s expecting, then all bets are off as to whether or not your automation will be successful.

SO: Right. And so, from our point of view, doing lots and lots and lots of structured content work,  i setting aside the tools and the technologies, what structured content really does is limit variability. Right? It limits the variability of the information or of the markup really going into your system, which then means in turn that the system can be automated and can automatically output all the outputs, all the deliverables, all the file formats that you need. And so  when you have weird edge cases, and I am of course the worst offender in terms of actually finding ways to bend the software to my will,  but only when I’m an author, right? As a system configuration, I’m, yeah, I think you should all follow the rules. Yeah. 

So here we are, and we basically say we’re gonna limit the variability of the input and standardize the input and therefore we will get standardized output. Cool. But as you said, that only works if you don’t have so what is a weird edge case or what are some of the things you’ve run into that just bollocks up automation?

BS: One good case is working with content that maybe has valid structures in place. So they’re not, you know, the content itself validates against a validator. So there’s nothing wrong with it. But they’re using, let’s say, different in this case, DITA elements in a let’s say creative way. You know, so they’re you know  I was on vacation last week, so that wasn’t me. 

SO: I see you were looking over my shoulder last week again. Yeah, that’s true. Well,  what I actually ran into was I had a DITA map; it was valid, it validated, and then I used Oxygen’s validator, which you know really goes a little bit deeper and looks at things. Everything was fine, but it crashed. And eventually what I realized or what I found after some digging was that somebody who was definitely me had inserted a draft comment and the draft comment was in a table, maybe under the title, but before the table group kind of thing. It was in an unusual location and it just the processor just died. It just laid down and gave up.  now I don’t know exactly whether that was because of the, you know, the core processing or something that we did in the the  plugin that I was running. It doesn’t really matter. The point is it was it was valid. But it didn’t pass the processor because the processor was like,  Why would anybody put a draft comment in this location? This is dumb. And then I, you know, felt berated by the processor and I moved it and then it all worked.

BS: Interesting. Yeah. I would have enjoyed being there for that. 

SO: Yeah. Mm hmm.  So anyway, we fixed it, and it was fine, and you know, and off we go. Let’s talk about pagination!

BS: Pagination’s a fun one. 

SO: Pagination’s my favorite.  I get very upset with bad line breaks or bad page breaks.

BS: Yeah. And you know, we can build in rules that say, you know, only, you know, to control widows and orphans, that type of thing. You know, tell it, you know, break a table leaving X many cells. If you don’t have X many cells to break, then move the entire table to the next page, all that fun stuff. But if you have, you know, specific places where you need to break to a new page for whatever reason, that always can’t be necessarily automated, depending on what the rules are for producing your output. You know, the processor’s not going to know that, you know, you may arbitrarily want to break a page at this particular location. So you know, in many cases we cobble together a little tag that says, you know, essentially break the page here,  and the processor knows to you know, when it sees that to say, okay, stop processing this page, move to the next one.  And you know, it works most of the time. 

SO: I would never. Yeah, the problem with inserting page breaks, as I’ve learned to my great sorrow, is that, of course, later you add more content and now you have a page break a third of the way down the page because you hard-coded it in. So this is bad, and you shouldn’t do it. But if you insist on doing it, do it l as late in the process as possible. Don’t try to fix your pages when you’re 80% of the way there, because you will have to reinsert them over and over and over again.  also.

BS: Right.

SO: Inserting empty tags that have a non-breaking space in them to introduce vertical space is wrong, and you shouldn’t do it.

BS: Ha ha. I will agree with that one. Also, I mean, doing these types of things to force a page break or to force extra space, it really flies in the face of automation because technically you have to create the output in order to know where you need to insert your page breaks and then go back and add them and then automate your output again.

SO: Ha ha. It looks bad.

BS: Yeah.

SO: So yeah. So really the limit of automation here is not really the automation, right?  It’s, well, it’s me, right? It’s the people who are like, I don’t like the way this looks, and I’m gonna find a way to fix it. And I have lots more demented tricks up my sleeve. I know all sorts of ways of bending this stuff to my will. And if you’re generating content at scale, you basically can’t afford to do any of this stuff, right? It’s one thing if you’re producing, you know, one document and it’s short and/or it’s marketing content, and we’re really concerned about the appearance because of people making buying decisions. But if you’re producing, you know, ten, twenty, fifty thousand pages a year, then you just need an engine that produces this stuff. So

BS: Mm-hmm. Yeah. And the page break problem is actually a good one to speak to at scale because if you’re in an environment where you’re sharing a bunch of different content and you’re reusing pieces, if you insert a page break for your own personal preference, suddenly that’s going to be in everyone else’s document that also uses that particular topic.

SO: All right. So I’m hearing that page breaks are bad and I shouldn’t do them.

BS: No, they’re great. It’s just that you have to be smart about it.

SO: Okay. Sneak them in. Don’t get caught. Got it.  So, while we’re on the subject of recalcitrant authors. Such is definitely not me. What about automation in a scenario where your content production system is not being used by the authors?

BS: Yes. That’s a completely different problem. Yeah, that’s an entirely different problem because then you’re in a situation where essentially you need a production team that are also, you know, data entry people, you know, and they need to somehow bring the content that’s being produced into the system in order to produce the output.

SO: Okay, but can’t we just use AI for that?

BS: Sure, why not? AI’s great for that, right? 

SO: Huh. Right. Yeah. So ultimately, I mean, sure. If the authors are authoring and they follow some sort of reasonable template or or they’re reasonably consistent in some way, we can bring the content in automatically.  I hope you heard the first part of that about the reasonably consistent part because that’s where this breaks, right? You’re gonna get all sorts of things.

BS: Mm-hmm.

SO: And our experience on this has been that this is a problem or a challenge across a variety of different factors.  Are your authors volunteers or are they paid? If they’re paid, you have, you know, some degree of leverage to say you need to work in the system. So are they volunteers or are they paid? Is content their full-time job or is it a small part of their responsibility? So, you know, 90% of the time I’m an engineer and 10% of the time I’m a writer. Or 90% of the time I’m a subject matter expert of some sort, and 10% of the time I’m writing content. The less time a given person is spending on content production, the more likely it is that they’ll push back on learning your weird content system. Right. 

BS: Mm-hmm.

SO: And then the third question, the third factor that goes into this is sort of level of expertise. I mean, we make jokes about rocket scientists, but it’s kind of that question. If you have a person who is responsible for contributing content and they are a world expert in their field,  unless that field happens to be something like content markup and formats, they’re not gonna be that interested in spending their time learning about content. They’re focused on,  you know, curing cancer. And I’m not prepared to argue that, you know, learning some tags is actually a useful use of their time, right? So you take the content that this world expert produces in whatever format you get it. Scribbled on a napkin, yucky Word file, whatever. We’ll take it. And you find a way to bring it into the system. Now, I will say there are some subject matter experts who are actually very interested in the content production process and will jump right in and kind of, you know, play around with it and learn what they need to learn and contribute directly in the system because they think it’s interesting and fun. But when you start talking about volunteers versus people that are being paid and/or experts versus, you know, people who are dedicated to this role and/or it’s part-time, the intersection of those three, the Venn diagram or actually the anti-Venn diagram of those three things is where you will find people that will not be working in your system.

BS: Yeah. And you know, with any kind of an external contributor to content, you also run the risk of having content that doesn’t necessarily follow the conventions that you’re expecting, you know, or that your automation pipeline is expecting. So you may get something from someone who absolutely knows their subject matter inside out and sideways, but they’ll give you a document that has, I don’t know, three levels of nested lists, and your system only accounts for two; then you have to figure out how you’re gonna get that content to play well within the construct that you have. Otherwise, you have to reengineer your pipeline.

SO: And then you have to balance the cost of reengineering the pipeline for the edge case against the value that the edge case brings to the situation.  And, you know, very early in my career, I ran into somebody who has since passed away, unfortunately,  who was terrible and I mean awful at  I I shouldn’t say it that way. He wasn’t terrible at following templates. He followed the templates until it didn’t suit him. And when it didn’t suit him, he would come up with some workaround to do what he was trying to do. So your nested lists are a good example, stuff like that.  And I was, I don’t know, 20-something,  and now I’m slightly older than that. It was my job to push back on this guy and say you didn’t follow the templates. And he would literally yell, which was a fun experience. 

BS: Mm-hmm.

SO: But the thing was that in every single case, he had thought about it. He never deviated from the templates by accident. It was always, I need it this way because of this other thing. I also learned the hard way that he had thought really carefully about word choice. And so if I ever changed any of his words, we would have well, we would have words.  And he would explain to me why I was wrong and why changing his word to my word or, you know, follow the style guide, whatever, was wrong in that instance. And he was always right. Always. Now, I’ve been doing this a really long time, and I’ve only run into one person like that in my entire career.  But I have not forgotten that at least once I ran into somebody who would work outside the provided, you know, color outside the lines in order to make his document better, and always had a defensible reason now. Do I wish he had yelled a little bit less? Yes, but you know, that’s life.

BS: Mm-hmm. All right, so we talked a bit about the automation pipeline and what people can inject into it. Should we turn our attention back toward our good friend Mr. AI?

SO: Mm. All right. So it’s interesting because a lot of the edge cases that we’re talking about can actually be addressed and flattened out by AI. So AI and AI tooling actually provide a potential solution to all of this. But I wanted to focus a little bit on a different problem with AI and automation. So when we talk about the limits of automation in, like a scripting workflow or a traditional non-AI workflow. A big part of the response to that is, well, the AI could actually address what you’re describing and solve for those things. Okay, fine. But now let’s talk about what AI can’t do, the limits of automation with AI. And it’s actually a really different problem than what we’ve been talking about. We’ve been talking about sort of following the template, don’t color outside the lines. With AI, what we’re running into is that.

BS: Mm-hmm.

SO: AI and AI systems only have visibility into the digital world. And so if there’s information floating around an organization that gets passed around in a hallway conversation, typically, and I say typically with great caution, the AI is not listening. And so if I, you know, if I run into Bill in the hallway and I say, which would be unusual because we’re always remote, so but never mind. We run into each other in the hall, and I say, hey, don’t forget to update that one thing. Or  I ran into so-and-so at lunch, and they told me this other little tidbit of information. Okay. Well, I’ve now transmitted that information to you and you’ve got to get it into your whatever you’re working on, right?

BS: Right.

SO: But the AI is not aware of that because it didn’t make it into a JIRA ticket or anything else, any sort of digital workflow. Now,  this is also in many cases, it’s similar to the problem that you actually run into with remote employees that they miss out on the so-called water cooler conversations, right? Like the conversations that happen on break or at lunch or this or that. But so the AI can only see what it has.

BS: Mm-hmm.

SO: And what it has access to is digital content. So your existing documents, your meeting recordings, your digital footprint, your email. I mean, it’s super creepy,  but what it doesn’t have access to is the person-to-person interactions that are not being recorded,  or conversations that are out there, or a note scribbled on a piece of paper or a couple of these Post-it notes that are sitting in front of my laptop, right?

BS: Yep.

SO: The AI doesn’t see those and therefore it can’t automate them. And because it can’t automate them,  they don’t get injected into the workflow.  And all of that sort of detritus, because if it’s important, I’m going to pass it on, right? I’ll put it in an email, or I’ll put it in a Jira ticket, or I’ll put it somewhere where it belongs.

BS: Right.

SO: In some sort of defect tracking. But it’s those little like, hey, I just saw this kind of side conversations. That cannot be automated. I mean, unless we move into an always-on surveillance cap surveillance world, which I mean I’m sure somebody’s working on it, but at the moment, if it’s not captured digitally, the AI can’t see it. And I think that we underestimate the amount of work and the amount of information that is sitting out there in the not-yet-digital world.

BS: Mm-hmm.

SO: So that’s the limit of AI automation, which is very different from “It can’t do really nice page breaks.” It probably actually can do really nice page breaks.

BS: So basically the quality and the efficiency of what you’re gonna get out of an AI automated workflow really depends on whether or not the information itself is available and being captured.

SO: Right, which is really quite different. I mean, AI is much better at taking sort of questionable inputs and doing things with them, right? Because it’s not a pass-fail script,  but it can only see what it can see.  And so we have to understand that there’s still limits there. And and it’s it’s a different limit. It’s a qualitatively different type of limit.

BS: Mm-hmm.

SO: And I think that’s important to recognize as we move forward.

BS: Right. And of course all that stuff that does need to be captured, it needs to be done in a way that AI can access it and can access it in the right way. So if it’s input wrong or whatever, then AI is not gonna have the right info.

SO: Yep. And so,  yeah, this is where I’ve been spending a lot of my time because, as you probably know, in the world’s worst-kept secret, Carlos Evia and I are working on a book on content and AI. 

BS: That’s my shocked face. 

SO: And yeah, I know, please contain your shocked face. It’s coming soon. She says hopefully, with many fingers crossed. We’re getting there. And what we’re hoping to do is get something out there that talks about some of these issues, right? We’re not gonna write a book that tells you how to optimize for a specific AI model or specifically how to build agentic workflows.

BS: Mm-hmm.

SO: What we want to do is give you the tools to ask the right questions so that you can start looking at how to put these things together and how to move forward with a reasonable content operations approach that also involves AI as appropriate.

SO: I look forward to being done. Also, for the record, the AI is not writing the book.

BS: Like, yeah.

SO: That seems important to say in this context today.

BS: That is quite important to say, given all of the AI-generated stuff we’re being force-fed at this point.

SO: No, I can assure you that my pain is human and personal when it comes to this writing. It has been real. It has been a slog. As perhaps it should be, because the process of putting something like this together forces you to really think carefully about what’s going on.  And the process of doing that with a co-author really forces you to think carefully about what’s going on. 

BS: And real? 

SO: And so it’s been great working with Carlos and also extremely painful because that’s just how books are.

BS: Yep. Cool. I think we’ll leave it there. Thanks, Sarah.

SO: Thank you.

Conclusion with ambient background music

CC: Thank you for listening to Content Operations by Scriptorium. For more information, visit Scriptorium.com or check the show notes for relevant links.

The post The limits of automation and AI in content workflows appeared first on Scriptorium.

Are you in control of your digital destiny? In this episode, Alan Pringle and Sarah O’Keefe define digital sovereignty. They break down what organizations need to consider as they bring AI into content operations, from data leakage and competitive intelligence to shifting international regulations.

Sarah O’Keefe: The working definition that I’m using is that digital sovereignty is control over your own digital assets, digital destiny. That could be you personally, it could be you as an organization, or it could be you as a country or as a group of nations. And when I say group of nations, probably 98% of the time I’m talking about the EU, which has some laws in this regard. Digital sovereignty is your ability to control, own, and manage your digital assets and how they are used, reused, processed, resold, repurposed, and all the rest of it.

Related links:

AI in the content lifecycle: three years later

From ad hoc to autonomous: The AI content ops maturity model

Upcoming AI book: The Uninvited Author

LinkedIn:

Alan Pringle

Sarah O’Keefe

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

AP: Hey everybody, I’m Alan Pringle.

SO: And I’m Sarah O’Keefe, hello.

AP: Hey there. And today Sarah and I want to talk about something that’s really starting to come to the forefront with all of the AI things that are going on in our world. And that is digital sovereignty. And before we get too deep in that, I need to throw up many, many disclaimers. Sarah and I are not lawyers, nor do we play them on television.

And we are absolutely not lawyers or experts on anything in regard to international intellectual property. So with those disclaimers out there, Sarah, if you would, would you define what digital sovereignty is?

SO: The working definition that I’m using is that digital sovereignty is control over your own digital assets, digital destiny. That could be you personally, it could be you, the organization, or it could be you as a country or as a group of nations. And when I say group of nations, probably 98% of the time I’m talking about the EU, which has some laws in this regard. So it is your ability to control, own, and manage your digital assets and how they are used, reused, processed, resold, repurposed, and all the rest of it.

AP: And I’m going to give a very basic example from my personal life in regard to email. Years ago, actually decades ago, when I got set up with an internet service provider, they provided me with an email address. It had their company name in the domain.com. And I used it for years. But when I switched my ISP, guess what? I had to make a decision.

Do I continue to pay that old ISP, basically rent, to maintain that old email address? Or should I move to one of maybe the free email providers? So I did a little research, and my ultimate decision was I created my own domain with my name, and I kind of just decided not to ever use again the email provided by an ISP because if you switch it, you’re going to possibly lose control of that email address. And at the time that I made the switch, a lot of the free providers, they would scan your email to provide targeted ads and some other things that was kind of unsavory to me. 

So I ultimately made the decision I was going to own the domain and set that up myself and pick my own software that I hooked up to it to use to to read it. And I did not use a third-party email provider to basically pull in or reference my account. I am using an open-source standalone email client to kind of minimize who can poke into my email. So that’s one very basic example of how I kind of took control of my digital life as it were.

SO: Right. And if we apply that to content ops, again, staying pretty general, you think about cloud systems, like the cloud universe versus on-prem.

AP: Yes.

SO: And when you talk about, let’s say, a CCMS, a component content management system that is on-premises, the the argument was always, well, that way all of our content lives on our servers, in our organization, we have complete control over it.

Along come the cloud services, the cloud-based CCMSs and everything else. And they say, well, yes, but it’s much cheaper for you to put this into the cloud on our systems, which are shared. And you know, there’s advantages of upgrading, and there’s all sorts of advantages to cloud systems, mostly around IT overhead. from a digital sovereignty point of view.

You are delegating to that cloud system and you have some sort of a contract or a service level agreement. And again, we are not lawyers, but you have this agreement that says we the cloud provider promise to not scan your information or not use it for evil or not, you know, there’s a bunch of stuff in that contract that governs how cloud provider is or is not allowed to handle your content, your personal information, your credit card information that you might be putting in there and all the rest of it. And with software as a service, with cloud systems, we have for the most part cut over into cloud. You know, that’s that’s kind of the default these days. There’s hardly anybody that is still putting their content, their content and their content management systems on their own proprietary in-house servers.

AP: Yes, correct.

SO: So we have decided that for cloud, you know, cloud writ large generally, that the advantages of cloud outweigh the disadvantages. Now, moving this a little bit more towards content and slowly towards AI, where things get really interesting with digital sovereignty, if we talk about machine translation for a minute.

There are a couple of different ways of doing machine translation, obviously, but big picture, you can have your in-house machine translation system and database, and you can control that and govern it and do things with it. Or you can take your content and you can throw it at a public-facing machine translation system. And the risk that you run when you throw something at a public system is that they will take your proprietary confidential content.

And use it. So I throw at it a sentence that says, the XYZ company has developed a special new thing, right? And I need that translated into various languages and I get it translated. But as a result of that, I am leaking information. I am leaking my confidential, potentially information into the machine translation services. And there are some really interesting security issues around that and how you might be able to.

As a competitor, extract that back out. But just dialing it back for a for a minute, we understand the concept of leaking information by using public-facing machine translation, right? Publicly available machine translation. But one thing that I think is very often overlooked is that machine translation, the fact that I ask for a specific language, never mind the content,

But the fact that I am now asking for a new language provides competitive intelligence in the sense that that means that I or my organization now cares about that locale, that that language. So let’s say that I’ve been consistently submitting European languages for machine translation, right? If you but if you look at my record of what I’m asking for, you will see that all of a sudden, about three months ago, I started adding a bunch of Asian languages.

Well, what does that tell you about what I’m up to? Either I’ve decided that Asian languages are interesting and fun, or my organization, I mean, presumably I’m doing this for work and not fun, or my organization is launching into Asia. And I don’t actually need to see the content to sort of get that piece of competitive intelligence out of it. So essentially.

The way that I’m using the machine translation, even if we protect or have a contract that says you you are not allowed to look at what I’m processing, but if you can look at the parameters of what I’m processing, that might give you enough information to tell you something about what I’m up to.

AP: Yeah, so basically what you request or don’t request, as the case may be, can give away clues that you may not want floating out in the world.

SO: Right, exactly. So now we take this to AI and we think about digital sovereignty for AI, and it gets very much more complicated. So t first, taking this example of machine translation, if you think about AI chatbots and prompting and public-facing models, then in the same way that requesting a particular language so well, let’s back up. 

Let’s say that we have a contract with XYZ model provider and it says we will not use your input as training data. We will not use your output as training data. Cool. But are you going to use my prompts as competitive intelligence? Because think about what I’m prompting on. Hey, tell me about the intellectual property laws in Vietnam.

Tell me about how to go to market in a particular country. Tell me about strategy for pricing tiers, right? If I’m doing those kinds of prompts, then you, as my competitor or adversary or whatever we’re dealing with here, can get an awful lot of information out of what I’m up to, right? You can figure out what I’m up to just by looking at my prompts. So we have to worry not just about the protection of the input and the output, but also the actual prompting that I’m using to get the inputs and the outputs, because the prompting itself has competitive information in it. So then when we start thinking about digital sovereignty, you say, okay, well, then what we should probably do is have a restrictions on usage of input, output, or prompting data by the vendor, right? The vendor who’s providing the AI model should have a contract that says we are now not allowed to use this stuff.

But then we take that another step forward. Nearly every contract I’ve seen in the past, you know, whatever years, decades says we promise not to use this unless we’re required to by law. So in other words, if a government subpoenas us, we will cough up this information as we are legally obligated to do, that sends us right down the road to something called zero data retention. Which is the idea that you process your AI prompts in such a way that they are not retained, that the inputs and outputs are not retained by the provider, so that if they are subpoenaed, they cannot in fact cough up the information because they don’t have it.

And then if you’re more paranoid than that, and some of you, you know, depending on what industry you’re in, should be, you start thinking about bringing the model in-house. So instead of using public-facing with a an enterprise contract of some sort, you think about bringing the model onto again, in-house on premises in the same way. This is right back to cloud versus not cloud.

And then we have some questions about what model are you using and do you know what’s going on in the internals of that model, which points you maybe at using something open source or maybe not. It depends. But there’s all these layers of decisions that you have to make around how you’re going to use AI and how concerned you are about leakage from your use of AI to you know your competitors or something else. 

Now, over the top of that, we start thinking about nations rather than organizations. And we have some of this with just generalized cloud systems. GDPR, the European Data Protection Regulation, says a lot of things about how you process personal information and what the requirements are and what you are and are not allowed to do. Now

If you’re a European company inside the EU, you’re clearly subject to GDPR. But many non-European companies are also subject to GDPR because you have a server in the European Union, or you have customers in the European Union, or you operate in some way in the EU, which then that’s enough to have you sort of folded into GDPR.

On the AI side, we have something very similar going on where companies are looking at AI models and making decisions based on what jurisdiction does that model belong to. Now, the most prominent of these is that, for example, currently, as of right now, as we’re recording this, the American models, like a ChatGPT, a Claude, that kind of thing, are largely not available in China. and it’s a little bit tricky in terms of is it actually banned or is it just restricted, but broadly not available. The Chinese models are available in the US and are open source, but if I’m an American or actually, let’s say I’m a European company and I’m trying to figure out my AI strategy. Do I use an American model? Do I use a Chinese model? What happens if I’m using a Chinese model and then the US government bans that model in the US and I have operations in the US? And I’m suddenly now what? 

In reverse, if I’m a an American company and I’m using American US models, and I have, let’s say, a chatbot, and I go to market in the EU. I am now subject to the EU AI Act. And the EU AI Act, with some complications for when it goes into effect, et cetera, has rules that say things like: if you put up a chatbot, you have to disclose that it’s AI. You can’t pretend that people are talking to a human. You have to say this chatbot is AI. This image was generated by AI. there are disclosure requirements in the EU. That are much more stringent than the disclosure requirements, which are none in the in the US.

AP: Yeah. In the US. And again, if you were making decisions about what model to get, where you should place your servers, etc., we highly recommend that you speak to your intellectual property attorneys and do not take advice from the two of us. Thank you very much. 

SO: Right. This entire podcast is just an ad for IP lawyers. You’re welcome, IP lawyers.

AP To me, this whole thing is so interesting. You know, we talk about the digital world and globalization and you know, there’s no boundaries anymore. Guess what? They very much apply here because, like you said, if you have customers in the EU, and you may be a US-based company, but that does not give you clearance to absolutely ignore GDPR. So we still have to be aware of geographical boundaries and the laws of all the nations inside those boundaries.

SO: Yeah, the thing that keeps me awake at night is the question of what if I build out an entire thing on some model? It it kind of doesn’t matter which one, but I build an entire infrastructure based on some AI vendor and then and then that AI vendor gets banned by a government whose jurisdiction I or my operations are subject to. 

AP: But there are significant costs.

SO: Right. And I’m not picking on any particular country. It it could go every which way that you can imagine. So then as a as an organization, especially if you’re a big international organization, you start thinking about well, maybe we should bring all this stuff in house so that we can control it.

AP: And infrastructure involved with that decision.

SO: Right, because now we’re talking about you operating your own data center, and that makes you, you know, not so beloved by literally anybody. So I mean, this is a really hard problem. And what’s fascinating to me is that we generally in software, software as a service, generally cloud has pretty much won with some minor exceptions for air-gapped systems, high security systems, network operations centers, or you know, software that runs utilities, that kind of thing. Things where you really, really, really do not want it taken down by external things. So you have this idea of secure systems, air-gapped systems, systems down at the bottom of a mine that are cut off because they’re literally down at the bottom of a mine.

AP: Yes, inside the earth, correct.

SO: We used to talk about airplane help, but that’s much less of an issue anymore because now, for better or for worse, we have Wi Fi on the planes.

AP: Yes.

SO: Mostly for the worse. Anyway…

AP: At least I haven’t heard a meeting yet, an online meeting. I’m sure it will happen at some point, but I have yet to see that happen. Thank goodness. 

SO: On a plane? Yeah. Hmm.

AP: Yeah.

SO: So anyway. So cloud, the risk-reward for cloud versus on-prem has pretty much tilted towards the cloud systems in general, with you know, very specific kinds of exceptions for I would say unique use cases, but it’s kind of like a ninety ten or an eighty-twenty kind of split.

Back in the day, it was everything was on-prem and cloud was the exception, but it’s it’s very much shifted. And now with all this AI stuff, everybody’s using currently big picture. As everybody’s adopting AI, they’re using public-facing models, public-facing chatbots, maybe with like some sort of an enterprise license. But I just have this feeling that a lot of this stuff is going to be brought into in-house at least managed models. 

AP: Exactly. Yeah.

SO: So still sort of cloud software as a service, but with a big enterprise contract wrapped around it that says, here’s what you you, the vendor, the AI vendor, can and cannot do with our data. But it’s a it’s just a really interesting problem because we in content we don’t deal with these sort of national issues that much, like sovereignty at the national level. And I think that with AI systems, we’re seeing a lot of concern around, well, what does it mean that this might be regulated differently in different countries? And how do we manage that? I mean, how do you put together an AI content ops strategy if you are not sure whether your model will be available in that country over there next week.

AP: Yeah, and I’m just thinking, think about audits are often a component of a contract, and you add AI on top of this, and all of the jurisdictional things you’re talking about, all of the facets of those audits just got even more complicated with AI than they already are, which that is probably a whole other discussion. Again, not a lawyer, don’t want to be, but I can see that being a very contentious something that’s gonna have to be ironed out in the very near future.

SO: Yeah, so digital sovereignty, it’s it’s very hard to say and even harder to spell. but I think that it’s something that we should be thinking about as content people and we should at least understand the big picture of what’s going on so that when we go to our corporate legal team and say, Hey, we’re worried about this, we can at least have a reasonable conversation about where this is going.

AP: And again, we don’t know where it’s going, but it’s definitely something to keep an eye on. This is a great place to wrap up. Sarah, thank you very much for a conversation that I frankly haven’t heard a lot about in the content world.

SO: Well thank you. Not a lawyer.

AP: Nor am I. Until the next time everyone, thanks. Bye.

SO: Bye everybody.

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The post Digital sovereignty in the age of AI appeared first on Scriptorium.

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)

LinkedIn:

Bill Swallow

Alan Pringle

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 correctly. And there are a lot of companies today that still have problems keeping content updated to the latest and greatest. So unfortunately, people turn around and call support. Or today they start hitting up the chatbot. But guess what? 

BS: Mm-hmm.

AP: If the chatbot doesn’t have access to the latest and greatest because frankly it doesn’t exist or it’s not hooked up to it. It’s it’s just like the poor people in support. It’s not going to know what to do and it’s going to spit out probably very authoritatively wrong outdated information. Again, yeah, it’s be it’s goes all the way back to

BS: That’s yeah.

AP: In your content creation process, how are you accounting for updates? How quickly are you getting them in place? How are you handling them in both your source language and how are you handling them in your other languages? It’s one thing I do want to bring up here, and and and I may be biased here, but I don’t think localization is getting enough attention on the AI distribution side. It’s been talked about for a very long time in regard to machine translation, AI assisted translation. 

BS: Mm-hmm.

AP: But I don’t just like I think sometimes localization is a second thought for a lot of companies, which still blows my mind in 2026. And by the way, we’re recording this in July 2026. So what we say right now may be outdated next month. Who knows? 

BS: Who knows?

AP: There is that issue. I’m curious, you have a more of a localization background than I do, but I do see that being a potential debt problem, part of this debt crisis that we’re talking about here.

BS: Definitely, because if you’re pushing your content out to AI, do you have targeted audiences in mind? Or is it going to be a free-for-all of people going in and using that AI to get answers to their questions? if you are localizing your content, I think probably the best practice here would be to somehow bundle for consumption all of the guides in all of the different languages together. So you have product XYZ and you have it translated in three languages. Then you put all of those copies together and give that to the AI so it can draw its associations as well as it builds out you know its understanding or I hate to use understanding because the AI doesn’t understand things. It’s very easy to make slips in that way. But so that AI can actually draw those relationships. So, you know, if you’re looking in English or you’re looking in Spanish or you’re looking in German, the same query in those three languages will retrieve pretty much the same result. That’s proper for that language. And we talked with someone last year on the podcast, Steve Maule from Acclaro, about AI in translation. 

AP: Yeah.

BS: And his focus was more on using AI as not so much a generative tool, but you know, a tool for aiding in AI, basically the next step from machine translation to neural machine translation to AI translation. And kind of the benefits and drawbacks at the time. Again, that was a year ago. So many of these things have probably changed again. But if you are translating content you may want to go back to that podcast and take a listen to what he had to say. and we’ll put a link to that in the show notes for this one.

AP: Yeah, again, I keep going back to this idea that we talked about at the beginning, how delivering via AI just puts this magnifying glass to what you’re doing and uncovers things that are wrong. But as you had mentioned, it doesn’t necessarily offer advice on how to fix it. And we will talk about that probably to wrap up the podcast. But what I also want to mention too. More and more, there’s actual real debt involved, not just the more indirect debt we’re talking about with the content debt, the technical debt. The AI companies, the providers, at one time had very generous offerings as far as the amount you could hit their APIs, the amount of tokens that they offered, whatever else. 

BS: Definitely.

AP: But now, the days of I guess you could say subsidized token use, they’re pretty much over. And now the actual real cost is being passed on to the people using these large language models. And as a result, the actual price, the cost of using AI is skyrocketing in all these organizations. And there have been a lot of news reports lately about very

BS: Mm-hmm.

AP: And there have been a lot of news reports lately about very big firms, and I will not name names, but you know them all, their household names, basically having to do a 180 and tell their employees, hey, you need to chill a little bit on your AI use because you’re burning up the tokens, converting PDF files to PowerPoint. And yeah, I get it.

BS: Mm-hmm.

AP: Doing that conversion is probably not the most efficient use, productive use of AI tokens. But this gets into kind of, I don’t know if there’s such a thing as cultural debt, but if your company is sending this message, and a lot of companies are, use AI for everything. And you know, they’ve got these leaderboards up showing these people use this much AI this week, you know, that sort of thing. You are encouraging a free-for-all. So, instead of saying, here are the good uses, this is where we think you need to focus your use of AI in coding, in content creation, in whatever else, this whole idea of just use it. You got to use it a lot. And now that’s coming back and biting people in the backside. So companies are scrambling and telling people, calm down.

BS: Mm-hmm.

AP: Chill out. Don’t use the AI as much. So it, there’s a mixed message there. And it’s because there was not good communication, nor was there good thought put into how workflows should incorporate AI. And I think that cultural thing right now is a huge problem. And everybody’s focus on the cost and the non-subsidized token use when they need to be going back and looking at, well, look at your comms. Look at what you were telling people six, twelve months ago about AI. It’s kinda on you.

BS: Mm-hmm. Yeah, basically don’t let your nine-year-old run around in a toy store with your credit card.

AP: Yeah, pretty much.

BS: That’s pretty much what’s been happening. And yeah, we’ve been seeing lots of reports. There was one in Forbes last week, where, you know, companies are saying that the cost of using the compute power is more expensive now than the people it was supposed to augment.

AP: Exactly. So I think the reality of what AI can do maybe is starting to sink in, maybe a little, but at least exposing people to the true cost of it on a corporate level, it may

BS: Mm-hmm.

AP: I’m probably being too positive here. What? Me being positive? It may result in some recalibration about how companies are telling people to use AI and maybe thinking a little more on a cultural level, all the way starting with communication. Here are the workflows you need to apply AI to. This is where you can apply it.

BS: Mm-hmm.

AP: And then there’s also the whole vibe coding discussion. I don’t know how much we want to get into it, but we all know, right, there is now an industry, you know, people are now going in to clean up vibe coding because, yeah, just because you can vibe code shouldn’t mean doesn’t mean you should be vibe coding. Because who was going to maintain and clean up that mess? So again.

BS: Well, we’re cleaning up a lot of vibe coding now.

AP: It’s another cultural issue that should have been addressed, but in this AI rush, everybody was like, use it, use it, use it, without thinking about how they should really how they should really apply it and basically sort of reimagine the way they do work in a productive way. And that does not mean let the AI do everything from my point of view.

BS: Mm-hmm.

AP: So I guess we probably need to kind of wrap up and talk about what people can do when all of their ugliness is amplified and magnified by AI. And it really it’s about rewinding and going back and looking at from the start, especially in the content world and the content operations world, how are you creating that content? Are you doing it in a consistent way? Are you building in intelligence into your content that an LLM can pick up to better understand the context of that content you’re providing it? Is your localization workflow efficient? Is it getting content turned around quickly so people in other markets are not six or eight months behind in the latest and greatest information. And by the way, yes, that still happens today, believe it or not, that people are months out and getting it because they are in another location. It’s shameful, but it still happens. It does.

BS: Mm-hmm. Yeah. And there’s also, you know, how are you feeding your content over to AI? How are you providing it? Are you just having the AI team you know scrape web pages and PDFs, or are you supplying something that’s a little bit more targeted and tied to the content itself, and maybe supplies some of that intelligence over?

AP: Yeah, because I think Sarah’s talked about this on a previous podcast. A company was noticing that their LLM was kind of not using PDFs or not weighing them, or and I’m again I’m personifying big time here. my apologies.

BS: Yeah. It’s easy.

AP: Was yeah, was not really it was not giving the weight to the content in PDFs. And when the company kind of did some reverse engineering, they realized it was because that PDF what they didn’t have a lot of context. 

BS: Mm-hmm.

AP: There wasn’t a lot of basically metadata built in that the LLM could basically parse. So it’s like, I’m gonna kind of put that to the side because it doesn’t have the richness that I need to do things well. So it it’s about looking at how you write. How are you delivering this content? And one possibility here, and it’s, well, there’s several structured content where you have metadata built into your content. You have tagging that offers semantic context.

BS: Mm-hmm.

AP: That’s one way to do it. And it’s not the only way. I mean, we have a lot of clients who use it, but it absolutely is not the only way to do it. Knowledge graphs can also be part of this solution. And sometimes knowledge graphs and structured content can play together to provide that rich feed of information that LLMs prefer and can do a better job with. So it’s not a one-size-fits-all solution when it comes to how to create that content or how to best hand it over to AI, but I think it is kind of a one-size-fits-all that if you aren’t doing things right foundationally, AI is going to kick your tail.

BS: Mm-hmm. Pretty much, yeah. And I think the best way to look at it is to consider AI another delivery target, just like you would a portal, just like you would a PDF or what have you, a help system. When you consider it as another endpoint for your content, another, you know, place to deliver to, it makes it a lot easier to start scoping what you need to do to reach, the requirements for that particular target.

AP: Agreed. So content people look at it as a delivery point and also look at as a job aid too to help enforce style guides, to help enforce taxonomy, whatever else. So it’s not just about content creation. 

BS: Mm-hmm.

AP: It’s also that part of your thinking needs to be a content distribution point, like Bill mentioned. And it can be hard to think of it as both of those things, but in the content world, it absolutely is.

BS: And I think that’s a good place to leave it. So thank you, Alan.

AP: Thanks, Bill.

BS: And we’ll see you on the next one.

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The post The debt crisis: AI edition appeared first on Scriptorium.

There are five levels of maturity for AI-driven content operations. Which level are you in? In this episode, Sarah O’Keefe and Bill Swallow walk through the AI content ops maturity model, from ad hoc experimentation to fully autonomous workflows.

Sarah O’Keefe: We want this automation, right? We want the ability to go in and extract release notes and do something with them. We have to have a certain level of maturity on the software development process so that we can grab the appropriate information. The same thing is true on the content side. You have to have a certain level of maturity in your content development processes, in your content management, so that you can identify the right things to process and the right things to access.

Related links:

Want to know more about Sarah’s nifty little side project? Register for our upcoming webinar.

AI in the content lifecycle

Enterprise content strategy maturity model

LinkedIn:

Sarah O’Keefe

Bill Swallow

Transcript:

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: I am Bill Swallow.

Sarah O’Keefe: And I’m Sarah O’Keefe.

BS: And today we’re going to talk about AI in content operations, or more specifically, a maturity model for AI.

SO: Everything needs a maturity model, even AI.

BS: Even me.

SO: I have no comment.

BS: My maturity model is written in crayon, what can I say? So okay, so we need a maturity model for AI as far as content operations are concerned, and probably in you know, many different degrees, but we’ll focus on content operations. So what might that look like?

SO: I’ve been thinking about this and what it looks like to employ AI as a tool to help you with content. And as I was thinking about what this looks like, you know, you always fall back on that standard five-step model where one is basically mass chaos, and five is the perfect world, generally. also, one is nearly always cheap, and five is nearly always expensive enterprise things. But, you know, let’s go a little beyond mass chaos versus governed, regulated, etc., and sort of sort of back up a little bit and talk about what this might look like. So level one in every maturity model typically is ad hoc. And what that means is that in this case, AI is being used sporadically by some people. It’s inconsistent. And I would say that when we look at AI and content specifically,

BS: Mm-hmm.

SO: This is going to be things like reprocessing your content using public-facing models. So I wrote a draft of something, I shove it into ChatGPT and I ask it to shorten it or tighten it up or identify areas that are problematic. Or I just say, hey, you know, write my article for me. The outcome that you’re gonna get on an ad hoc model is going to depend on an ad hoc level one.

BS: Mm-hmm.

SO: AI thing is going to depend on how good you are on the individual’s expertise and their level of interest. So if you want to just go in there and say, hey, I have a bio and it’s too long, and I’ve been asked to produce one that’s only 50 words for a particular conference, for example, then, you know, this is this is actually a really good example of ad hoc, right? 

BS: Mm-hmm.

SO: We have these long multi-paragraph bios and every conference I’ve been to has a different requirement for how that bio needs to be shaped. And the fastest way to success is to just shove it into a chatbot and say, give me a 50-word version. And and then read it and make sure it didn’t invent things or give you a PhD or anything like that, and then ship it off to the conference organizer. But this is much, much faster than rewriting it from scratch by hand. And also I think, it’s a good example of something where I have the extended version and I’m going to summarize down. And that usually works pretty well. So level one is ad hoc. It’s kind of sporadic. There’s no standard across the organization. It’s just me saying, this looks useful, or you’ve probably got some use cases in this space as well.

BS: Right. So it it kind of aligns with I guess level one of the the content maturity model that we talked about a while back, where level one is is simply content exists. Could be, you know, someone typing stuff up in Word or, you know, using a myriad of different tools, no style guide, just kind of getting content out there because people need it.

SO: Yep. So level two is tactical. And tactical is sort of like we’re using this tool to solve some specific problems. And what you’re going to see here is something like that Bill has invented some nifty time saving tool and he has shared it with other people. Or in a larger organization, maybe somebody invented a nifty validate or or something like that and they’ve rolled it out across maybe the department, probably not the entire organization. Aomething like AI support is being rolled out. Maybe the organization has created a chatbot internally for customers, right? So there’s a chatbot, it’s sitting on the company website, people can use it to get answers, but it’s really bad. And the reason it’s really bad is because nobody thought too carefully about the content going into the chatbot, because again, we’re tactical. So probably this looked like the AI team just raided the local SharePoint, grabbed a bunch of content, did not pay a whole lot of attention to the question of whether this content was up to date in release status. Those things don’t exist, right? It’s just, look, a bucket of PDFs. Cool. Let’s dump them into the AI and go for it.

BS: Mm-hmm.

SO: And tragically, in many cases, the techcomm team is sitting on rigorous, structured, vetted, approved content, and nobody remembered to go ask them, can we have your content? Or where is your official content source? Or how do I know what version belongs with which document?

BS: Right, because you know, in in their point of view there’s a PDF of it, so I don’t need to ask them.

SO: Yeah, it’s it’s just PDF. How hard could it be? So something like AI was support was rolled out, but nobody really thought about it. Maybe it’s at a departmental level, probably it’s not enterprise-wide. And nobody has really thought about connecting this AI thing to the assets inside the organization in a reasonable, rigorous, governed, organized kind of manner.

BS: And I suppose that’s where you get to the next tier.

SO: Right. So the next tier after tactical comes strategic, right? So we have an actual strategy. Now, one of the difficulties in talking about AI is that AI is a tool and it’s kind of like talking about electricity. You can apply it to lots of places and it’s more sensible in some places than others. But when we say what’s your AI strategy, like how do you use water? I mean, come on, and the answer is of course to drive the AI and you know destroy the environment. But there are things that you can do with AI that are useful for content. There are also things that you can do that are not. So if you have a strategic approach to this, a strategic approach to use of AI, backing up to the authors again rather than the delivery side, maybe this looks like a collection of prompts that have been built that are shared.

BS: Not with electricity.

SO: Maybe this looks like saying this is the workflow that you employ. These are the kinds of things that we do to actually test whether this thing is working. these are the metrics that we’re following. So there’s an actual overarching bigger picture that somebody’s thinking about that goes beyond, let me go shove this into chatbot of the day.

BS: Mm-hmm. Right, right.

SO: So there’s an actual strategy for the public-facing chatbots. Somebody has thought about the back end. The authors have useful AI tools that add to their you know their productivity. One of the things that I’m hearing a lot now, you know, low-hanging fruit, release notes. Nobody wants to write release notes. It’s a terrible drudge task. It’s and it needs to be done. Well,

BS: Mm-hmm.

SO: There’s now there are now a lot of solutions that look like look at the diff in the code, look at the delta from you know version one to version one dot one, find the diff in the code, find the changes that have been made, look at the JIRA tickets that have been addressed, that have been solved in release one dot one, and then consolidate that all into a set of release notes that say, here’s what’s been done. And that’s probably 90% of the work, and the last 10% of the work is read that and make sure it’s accurate. Right? Don’t please don’t skip that step. Like actually look at what the thing is generating. Now, what’s interesting to me about level three, this sort of more strategic approach, is that what you’re gonna start to see is that you have prerequisites for this. You can’t do this. 

BS: Yes.

SO: So release notes are good example. Let’s say that hypothetically, and this is gonna sound insane, but let’s say that hypothetically, you have software development and you have no source control.

BS: Hmm.

SO: Everybody’s screaming, right? Because this is nuts, and why would you ever do this? Okay. But hypothetically, you have no source control. Okay. How do you know what’s changed between version one and version one point one?

BS: It’s up here in my head.

SO: Excellent, great. Ha okay, cool. so I’m gonna need to connect the AI to your head so that we can pull those changes out of your head.

BS: That sounds fun.

SO: Yeah. Amazing. Right. So all of a sudden, because we want this automation, right? We want the ability to go in and extract release notes and do something with them. We have to have a certain level of maturity on the software development process so that we can grab the appropriate information. Now, the same thing is of course true on the content side. You have to have a certain level of maturity in your content development processes, in your content management, so that you can identify, you know, the right things to process and the right things to access. And why it is that, you know, we know that software has to be governed, but we’re not so sure about content is a mystery to me.

BS: I had never understood that.

SO: Yeah. So there we are. Okay, so that’s kind of like a level three. With there’s some sort of strategy emerging across the enterprise. There are some useful tools and they’re shared. This is kind of like in content when you start thinking about templates. We’re gonna have some templates and we’re gonna give them to people and they’re gonna use them and it’s gonna be great. All right, so level four is governed, managed. And so now good understanding of AI.

BS: Mm-hmm.

SO: It is being applied in a useful, intelligent manner, by which I mean don’t apply it to the wrong problem sets, right? Apply it to the things where it makes sense to apply it. Thinking about governance, thinking about metrics, thinking about success. And then your data sources and your content sources are being managed in such a way that the AI gets good input and can actually generate good output. So I’m actually not a big fan of the term human in the loop because human in the loop implies that the AI is doing all the work and then like the human eventually gets around to QAing it. You know what? We’re terrible at QA. You know who’s good at QA?

BS: Mm-hmm. AI.

SO: No, computers, not AI. AI is all about probability and whatever. It is actually not very good at QA. What’s good at QA is traditional software, right? One plus one is always two. In an AI, one plus one, sometimes it’s not two. So you manage that stuff and you put those guardrails up and you start putting up the guardrails that say, okay, when the AI kind of wanders off into the wilderness, we’re gonna like bring it back to reality. We’re gonna have, we’re gonna put it in a box, right? Make the AI think inside the box, and we’re gonna govern what that box is. AI is great at thinking outside the box. Unfortunately, that’s usually not what we want from technical content. So it needs to be in in the box and it needs to be consistent and needs to be managed and all the rest of it. 

BS: Mm-hmm.

SO: So we govern it, right? We go in there and we make sure that the processes and the tooling that’s being put in place and the automation that’s being put in place where we’re leveraging or using AI to do things is managed. And so the human in the loop thing. I don’t want the human in the loop to fix things on the back end. I want the human in the loop to fix things on the front end so that what goes in is better, so that there’s less work to do when it comes out. You know, fix it beforehand. Don’t remediate it afterwards. That’s a boatload of work and it is not fun. So fix it ahead of time.

BS: Right. Yeah. And likewise you probably wanna have, you know, some guardrails in there so that, you know, your AI, whatever it is, doesn’t go playing around with content that has been approved and released and is not slated for updating.

SO: Yeah, you know, don’t fix that. That one’s done. That one’s and you know, we’re not even talking here about what it means to be in a regulated industry or in a regulatory environment. there if you are shipping or sorry, if you are a large organization and you are doing things in Europe, then you are likely subject to the European, the EU AI Act.

BS: That’s a completely different beast.

SO: And you have to think about what that means for what you’re doing, because the fun gold rush wild, wild west strategy of just throw AI at everything is not gonna fly in Europe. Okay, so that’s governed, you know, hypothetically. And then level five is agentic, which is basically that everything, everything or a lot of it is running autonomously.

BS: Mm-hmm.

SO: You know, the layman’s explanation of what is agentic AI, the difference is that instead of saying I need to put a prompt into the chatbot, it does it itself because you’ve built out the systems that drive all of that happening. 

BS: It understands what needs to happen at what point in time.

SO: Well, let’s not say understands, but yes. I’m trying so hard. 

BS: Well, yeah, not understands, but there’s a workflow in place that the AI is following.

SO: And it’s so difficult. I think that, you know, there’s, as a side note, the why do we think, why do we impose personality on the chatbots? And the answer is I think that psychologically it’s very, very difficult to interact with something that play acts at human interaction. What a great idea! Good for you. I love your thinking, blah, blah, blah. So it makes you think you’re interacting with a human. And I don’t think that our brains are equipped to say, no, actually, this is a machine.

BS: It’s like a scary version of Teddy Ruxpin.

SO: It, well, it passes the Turing test. And so we just can’t separate if it feels like you’re interacting with a person, you know you’re not, but it feels as though you are, and feeling is always gonna win over knowledge. So

BS: Mm-hmm. Well yeah, the interaction is a lot more organic than you get from, you know, traditional tools.

SO: Or, you know, yeah. I mean, think about the difference between a search, typing in a search string, and you know, a conversational search, a conversational interface. It’s quite, quite troubling, actually. Yeah, so this is kind of the five-level model, right? From big mess ad hoc, some things are happening, th some things aren’t, up to it’s completely autonomous. Now, if it’s going to be the more autonomy you want, the better your inputs have to be, which circles us right back to, and therefore, you have to do the work on the content side, because if you don’t do the work on the content side, the AI is going to go off the rails in interesting, unexpected, and potentially disastrous ways.

BS: It will play with the mess you leave it.

SO: Yep. So that’s where we’re going with this. That’s the AI content ops maturity model as it stands today. I reserve the right to change it tomorrow.

BS: Today. Of course.So this model came out of I guess some little nifty side project you’ve been working on recently.

SO: I am working on a nifty little side project. We’re not quite ready to announce it. but I’ve got a a co-author and we’re working on a thing.

BS: Fair.

SO: I could say more but then I’d, you know, be in trouble.

BS: When might you be able to say more?

SO: I believe that we have a webinar coming July 22nd, where we will say some more things.

BS: Alrighty. Well we will learn more things then.

SO: I too will learn more things and probably we’ll have to we’ll probably we’ll have to change everything we’ve done up until this point because everything will change by then.

BS: Of course. Guess that’s a good place to leave this podcast. Thank you, Sarah.

SO: Thank you.

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