Content Operations

Scriptorium - The Content Strategy Experts

Scriptoriums delivers industry-leading insights for global content operations.

  1. 11/17/2025

    Futureproof your content ops for the coming knowledge collapse

    What happens when AI accelerates faster than your content can keep up? In this podcast, host Sarah O’Keefe and guest Michael Iantosca break down the current state of AI in content operations and what it means for documentation teams and executives. Together, they offer a forward-thinking look at how professionals can respond, adapt, and lead in a rapidly shifting landscape. Sarah O’Keefe: How do you talk to executives about this? How do you find that balance between the promise of what these new tool sets can do for us, what automation looks like, and the risk that is introduced by the limitations of the technology? What’s the roadmap for somebody that’s trying to navigate this with people that are all-in on just getting the AI to do it? Michael Iantosca: We need to remind them that the current state of AI still carries with it a probabilistic nature. And no matter what we do, unless we add more deterministic structural methods to guardrail it, things are going to be wrong even when all the input is right. Related links: Scriptorium: AI and content: Avoiding disaster Scriptorium: The cost of knowledge graphs Michael Iantosca: The coming collapse of corporate knowledge: How AI is eating its own brain Michael Iantosca: The Wild West of AI Content Management and Metadata MIT report: 95% of generative AI pilots at companies are failing LinkedIn: Michael Iantosca Sarah O’Keefe 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. SO: 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, I’m Sarah O’Keefe. In this episode, I’m delighted to welcome Michael Iantosca to the show. Michael is the Senior Director of Content Platforms and Content Engineering at Avalara and one of the leading voices both in content ops and understanding the importance of AI and technical content. He’s had a longish career in this space. And so today we wanted to talk about AI and content. The context for this is that a few weeks ago, Michael published an article entitled The coming collapse of corporate knowledge: How AI is eating its own brain. So perhaps that gives us the theme for the show today. Michael, welcome. Michael Iantosca: Thank you. I’m very honored to be here. Thank you for the opportunity. SO: Well, I appreciate you being here. I would not describe you as anti-technology, and you’ve built out a lot of complex systems, and you’re doing a lot of interesting stuff with AI components. But you have this article out here that’s basically kind of apocalyptic. So what are your concerns with AI? What’s keeping you up at night here?  MI: That’s a loaded question, but we’ll do the best we can to address it. I’m a consummate information developer as we used to call ourselves. I just started my 45th year in the profession. I’ve been fortunate that not only have I been mentored by some of the best people in the industry over the decades, but I was very fortunate to begin with AI in the early 90s when it was called expert systems. And then through the evolution of Watson and when generative AI really hit the mainstream, those of us that had been involved for a long time were… there was no surprise, we were already pretty well-versed. What we didn’t expect was the acceleration of it at this speed. So what I’d like to say sometimes is the thing that is changing fastest is the rate at which the rate of change is changing. And that couldn’t be more true than today. But content and knowledge is not a snapshot in time. It is a living, moving organism, ever evolving. And if you think about it, the large language models, they spent a fortune on chips and systems to train the big large language models on everything that they can possibly get their hands and fingers into. And they did that originally several years ago. And the assumption is that, especially for critical knowledge, is that that knowledge is static. Now they do rescan the sources on the web, but that’s no guarantee that those sources have been updated. Or, you know, the new content conflicts or confuses with the old content. How do they tell the difference between a version of IBM database 2 of its 13 different versions, and how you do different tasks across 13 versions? And can you imagine, especially when it comes to software where most of us, a lot of us work, the thousands and thousands of changes that are made to those programs in the user interfaces and the functionality? MI: And unless that content is kept up-to-date and not only the large language models, reconsume it, but the local vector databases on which a lot of chatbots and agenda workflows are being based. You’re basically dealing with out-of-date and incorrect content, especially in many doc shops. The resources are just not there to keep up with that volume and frequency of change. So we have a pending crisis, in my opinion. And the last thing we need to do is reduce the people that are the knowledge workers to update, not only create new content, but deal with the technical debt, so that we don’t collapse on this, I think, is a house of cards. SO: Yeah, it’s interesting. And as you’re saying that, I’m thinking we’ve talked a lot about content debt and issues of automation. But for the first time, it occurs to me to think about this more in terms of pollution. It’s an ongoing battle to scrub the air, to take out all the gunk that is being introduced that has to, on an ongoing basis, be taken out. Plus, you have this issue that information decays, right? In the sense that when, I published it a month ago, it was up to date. And then a year later, it’s wrong. Like it evolved, entropy happened, the product changed. And now there’s this delta or this gap between the way it was documented versus the way it is. And it seems like that’s what you’re talking about is that gap of not keeping up with the rate of change. MI: Mm-hmm. Yeah. I think it’s even more immediate than that. I think you’re right. But now we need to remember that development cycles have greatly accelerated. Now, when you bring AI for product development into the equation, we’re now looking at 30 and 60-day product cycles. When I started, a product cycle was five years. Now it’s a month or two. And if we start using AI to draft new content, for example, just brand new content, forget about the old content or update the old content. And we’re using AI to do that in the prototyping phase. We’re moving that more left upfront. We know that between then and CodeFreeze that there’s going to be a numerous number of changes to the product, to the function, to the code, to the UI. It’s always been difficult to keep up with it in the first place, but now we’re compressed even more. So we now need to start looking at AI to how does it help us even do that piece of it, let alone what might be a corpus that is years and years old, that’s not ever had enough technical writers to keep up with all the changes. So now we have a dual problem, including new content with this compressed development cycle. SO: So the, I mean, the AI hype says we essentially, we don’t need people anymore and the AI will do everything from coding the thing to documenting the thing to, I guess, buying the thing via some sort of an agentic workflow. But what, I mean, you’re deeper into this than nearly anybody else. What is the promise of the AI hype, and what’s the reality of what it can actually do? MI: That’s just the question of the day. Because those of us that are working in shops that have engineering resources, I have direct engineers that work for me and an extended engineering team. So does the likes of Amazon, other serious, not serious, but sizable shops with resources. We have a lot of shops that are smaller. They don’t have access to either their own dedicated content systems engineers or even their IT team to help them. First, I want to recognize that we’ve got a continuum out there, and the commercial providers are not providing anything to help us at this point. So it’s either you build it yourself today, and that’s happening. People are developing individual tools using AI where the more advanced shops are looking at developing entire agentic workflows.  And what we’re doing is looking at ways to accelerate that compressed timeframe for the content creators. And I want to use content creators a little more loosely because as we move the process left, and we involve our engineers, our programmers in the early, earlier in the phase, like they used to be, by the way, they used to write big specifications in my day. Boy, I want to go into a Gregorian chant. “Oh, in my day!” you know, but, but they don’t do that anymore. And basically the, the role of the content professional today is that of an investigative journalist. And you know what we do, right? We, we scrape and we claw. We test, we use, we interview, we use all of the capabilities of learning, of association, assimilation, synthesis, and of course, communication. And turns out that writing’s only 15% roughly of what the typical writer does in an information developer or technical documentation professional role, which is why we have a lot of different roles, by the way, that if we’re gonna replace or accelerate with people with AI, have to handle all those capabilities of a

    33 min

  2. 11/03/2025

    The five stages of content debt

    Your organization’s content debt costs more than you think. In this podcast, host Sarah O’Keefe and guest Dipo Ajose-Coker unpack the five stages of content debt from denial to action. Sarah and Dipo share how to navigate each stage to position your content—and your AI—for accuracy, scalability, and global growth. The blame stage: “It’s the tools. It’s the process. It’s the people.” Technical writers hear, “We’re going to put you into this department, and we’ll get this person to manage you with this new agile process,” or, “We’ll make you do things this way.” The finger-pointing begins. Tech teams blame the authors. Authors blame the CMS. Leadership questions the ROI of the entire content operations team. This is often where organizations say, “We’ve got to start making a change.” They’re either going to double down and continue building content debt, or they start looking for a scalable solution. — Dipo Ajose-Coker Related links: Scriptorium: Technical debt in content operations Scriptorium: AI and content: Avoiding disaster RWS: Secrets of Successful Enterprise AI Projects: What Market Leaders Know About Structured Content RWS: Maximizing Your CCMS ROI: Why Data Beats Opinion RWS: Accelerating Speed to Market: How Structured Content Drives Competitive Advantage (Medical Devices) RWS: The all-in-one guide to structured content: benefits, technology, and AI readiness LinkedIn: Dipo Ajose-Coker Sarah O’Keefe 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. SO: 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. I’m Sarah O’Keefe and I’m here today with Dipo Ajose-Coker. He is a Solutions Architect and Strategy at RWS and based in France. His strategy work is focused on content technology. Hey, Dipo. Dipo Ajose-Coker: Hey there, Sarah. Thanks for having me on. SO: Yeah, how are you doing? DA-C: Hanging in there. It’s a sunny, cold day, but the wind’s blowing. SO: So in this episode, we wanted to talk about moving forward with your content and how you can make improvements to it and address some of the gaps that you have in terms of development and delivery and all the rest of it. And Dipo’s come up with a way of looking at this that is a framework that I think is actually extremely helpful. So Dipo, tell us about how you look at content debt. DA-C: Okay, thanks. First of all, I think before I go into my little thing that I put up, what is content debt? I think it’d be great to talk about that. It’s kind of like technical debt. It refers to that future work that you keep storing up because you’ve been taking shortcuts to try and deliver on time. You’ve let quality slip. You’ve had consultants come in and out every three months, and they’ve just been putting… I mean writing consultants. SO: These consultants. DA-C: And they’ve been basically doing stuff in a rush to try and get your product out on time. And over time, those sort of little errors, those sort of shortcuts will build up and you end up with missing metadata or inconsistent styles. The content is okay for now, but as you go forward, you find you’re building up a big debt of all these little fixes. And these little fixes will eventually add up and then end up as a big debt to pay. SO: And I saw an interesting post just a couple of days ago where somebody said that tech debt or content debt, you could think of it as having principle and interest and the interest accumulates over time. So the less work you do to pay down your content debt, the bigger and bigger and bigger it gets, right? It just keeps snowballing and eventually you find yourself with an enormous problem. So as you were looking at this idea of content debt, you came up with a framework for looking at this that is at once shiny and new and also very familiar. So what was it? DA-C: Yeah, really familiar. I think everyone’s heard of the five stages of grief, and I thought, “Well, how about applying that to content debt?” And so I came up with the five stages of content debt. So let’s go into it. I’m not going to keep referring to the grief part of it. You can all look it up, but the first stage is denial. “Our content is fine. We just need a better search engine. We can actually put it into this shiny new content delivery platform and it’s got this type of search,” and so on and so forth. Basically what you’re doing is you’re ignoring the growing mess. You’re duplicating content. You’ve got outdated docs. You’re building silos, and then you’re ignoring that these silos are actually getting even further and further apart. No one wants to admit that the CMS or whatever system, bespoke system that you’ve put into place, is just a patchwork of workarounds. This quietly builds your content debt until, actually the longer denial lasts, the more expensive that cleanup is. As we said in that first bit, you want to pay off the capital of your debt as quickly as possible. Anyone with a mortgage knows that. You come into a little bit of money, pay off as much capital as you can so that you stop accruing that debt, the interest on the debt. SO: And that is where when we talk about AI-based workflows, I feel like that is firmly situated in denial. Basically, “Yeah, we’ve got some issues, but the AI will fix it. The AI will make it all better.” Now, we painfully know that that’s probably not true, so we move ourselves out of denial. And then what? DA-C: There we go into anger. SO: Of course. DA-C: “Why can’t we find anything? Why does every update take two weeks?” And that was a question we used to get regularly where I used to work at a global medical device manufacturer. We had to change one short sentence because a spec change and it took weeks to do that. Authors are wasting time looking for reusable content if they don’t have an efficient CCMS. Your review cycles drag through because all you’re doing is giving the entire 600-page PDF to the reviewer without highlighting what’s in there. Your translation costs balloon and your project managers or leadership gets angry because, “Well, we only changed one word. Can’t you just use Google Translate? It should only cost like five cents.” Compliance teams then start raising flags. And if you’re in a regulated industry, you don’t want the compliance teams on your back, and especially you don’t want to start having defects out in the field. So eventually, productivity drops, your teams feel like they’re stuck. And the cracks are now starting to show across other departments and you’re putting a bad name on your doc team. SO: Yeah. And a lot of this, what you’ve got here, is the anger that’s focused inward to a certain extent. It’s the authors that are angry at everybody. I’ve also seen this play out as management saying, “Where are our docs? We have this team, we’re spending all this money, and updates take six months.” Or people submit update requests, tickets, something, the content doesn’t get into the docs, the docs don’t get updated. There’s a six-month lag. Now the SOP, the standard operating procedure, is out of sync with what people are actually doing on the factory floor, which it turns out, again, if you’re in medical devices, is extremely bad and will lead to your factory getting shut down, which is not what you want generally. DA-C: Yeah, it’s not a good position to be in. SO: And then there’s anger. DA-C: Yeah. SO: “Why aren’t they doing their job?” And yet you’ve got this group that’s doing the best that they can within their constraints, which are, as you said, in a lot of cases, very inefficient workflows, the wrong tool sets, not a lot of support, etc. Okay, so everybody’s mad. And then what? DA-C: Everyone’s mad, and eventually, actually this is a closed little loop because all you then do is say, “Okay, well, we’re going to take a shortcut,” and you’ve just added to your content debt. So this stage is actually one of the most dangerous of the parts of it because all you end up trying to do without actually solving the problem is just add to the debt. “Let’s take a shortcut here, let’s do this.” The next stage is now the blame stage. “It’s the tools. It’s the process. It’s the people.” These here and then you get calls of technical writers or, “Well, we’re going to put you into this department and we’ll get this person to rule you with this new agile process,” or, “We’ll get you to be doing it in this way.” The finger-pointing begins. Tech teams will blame the authors. Authors will blame the CMS. Leadership questions the ROI of the entire content operations team. This is often where organizations see that we’ve got to start making a change. They’re either going to double down and continue building that content debt or they start looking for a scalable solution. SO: Right. And this is the point at which people look at it and say, “Why can’t we just use AI to fix all of this?” DA-C: Yep, and we all know what happens when you point AI at garbage in. We’ve got the saying, and this saying has been true from the beginning of computing, garbage in, garbage out, GIGO. SO: Time. DA-C: Yeah. I changed that to computing. SO: Yeah. It’s really interesting thou

    27 min

  3. 10/20/2025

    Balancing automation, accuracy, and authenticity: AI in localization

    How can global brands use AI in localization without losing accuracy, cultural nuance, and brand integrity? In this podcast, host Bill Swallow and guest Steve Maule explore the opportunities, risks, and evolving roles that AI brings to the localization process. The most common workflow shift in translation is to start with AI output, then have a human being review some or all of that output. It’s rare that enterprise-level companies want a fully human translation. However, one of the concerns that a lot of enterprises have about using AI is security and confidentiality. We have some customers where it’s written in our contract that we must not use AI as part of the translation process. Now, that could be for specific content types only, but they don’t want to risk personal data being leaked. In general, though, the default service now for what I’d call regular common translation is post editing or human review of AI content. The biggest change is that’s really become the norm. —Steve Maule, VP of Global Sales at Acclaro Related links: Scriptorium: AI in localization: What could possibly go wrong? Scriptorium: Localization strategy: Your key to global markets Acclaro: Checklist | Get Your Global Content Ready for Fast AI Scaling Acclaro: How a modular approach to AI can help you scale faster and control localization costs Acclaro: How, when, and why to use AI for global content Acclaro: AI in localization for 2025 LinkedIn: Steve Maule 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. SO: 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, I’m Bill Swallow, and today I have with me Steve Maule from Acclaro. In this episode, we’ll talk about the benefits and pitfalls of AI in localization. Welcome, Steve. Steve Maule: Thanks, Bill. Pleasure to be here. Thanks for inviting me. BS: Absolutely. Can you tell us a little bit about yourself and your work with Acclaro? SM: Yeah, sure, sure. So I’m Steve Maule, currently the VP of Global Sales at Acclaro, and Acclaro is a fast-growing language services provider. So I’m based in Manchester in the UK, in the northwest of England, and I’ve been now in this industry, and I say this industry, the language industry, the localization industry for about 16 years, always in various sales, business development, or leadership roles. So like I say, we’re a language services provider. And I suppose the way we try and talk about ourselves is we try and be that trusted partner to some of the world’s biggest brands and the world’s fastest growing global companies. And we see it Bill as our mission to harness that powerful combination of human expertise with cutting edge technology, whether it be AI or other technology. And the mission is to put brands in the heads, hearts, and hands of people everywhere. BS: Actually, that’s a good lead in because my first question to you is going to be where do you see AI and localization, especially with a focus of being kind of the trusted partner for human-to-human communication? SM: My first answer to that would be it’s no longer the future. AI is the now. And I think whatever role people play in our industry, whether you’re like Acclaro, you’re a language services provider, offering services to those global brands, whether you are a technology provider, whether you run localization, localized content in an enterprise, or even if you’re what I’d call an individual contributor, maybe you’re a linguist or a language professional. I think AI is already changed what you do and how you go about your business. And I think that’s only going to continue and to develop. So I actually think we’re going to stop talking at some stage relatively soon about AI. It’s just going to be all pervasive and all invasive. BS: It’ll be the norm. Yeah. SM: Absolutely. We don’t talk any more about the internet in many, many industries, and we won’t talk about AI. It’ll just become the norm. And localization, I don’t think is unique in that respect. But I do think that if you think about the genesis of large language models and where they came from, I think localization is probably one of the primary and one of the first use cases for generative AI and for LLMs. BS: Right. The industry started out decades ago with machine translation, which was really born out of pattern matching, and it’s just grown over time. SM: Absolutely. And I remember when I joined the industry, what did I say? So 2009, it would’ve been when I joined the industry. And I had friends asking me, what do you mean people pay you for translation and pay for language services? I’ve just got this new thing on my phone, it’s called Google Translate. Why are we paying any companies for translation? So you’re absolutely right, and I think obviously machine translation had been around for decades before I joined the industry. So yeah, I think that question has come into focus a lot more with every sort of, I was going to say, every year that passes, quite honestly, it’s every three months. BS: If that. SM: Exactly, yeah. Why do companies like Acclaro still exist? And I think there are probably a lot of people in the industry who actually, if you think about the boom in Gen I over the last two, two and a half years, there’s a lot of people who see it as a very real existential threat. But more and more what I’m seeing amongst our client base and our competitors and other actors in the industry, the tech companies, is that there’s a lot more people who are seeing it as an opportunity actually for the language industry and for the localization industry. BS: So about those opportunities, what are you seeing there? SM: I think one of the biggest things, it doesn’t matter what role you play, whether you’re an individual linguist or whether you’re a company like ours, I think there’s a shift in roles and the traditional, I suppose most of what I dealt with 16 years ago was a human being doing translation, another human being doing some editing. There were obviously computers and tools involved, but it was a very human-led process. I think we’re seeing now a lot of those roles changing. Translators are becoming language strategists; they’re becoming quality guardians. Project managers are becoming sort of almost like solutions architects or data owners. So I think that there’s a real change. And personally, I don’t think, and I guess this is what this podcast is all about. I don’t see the roles of a few things going away, but I do see those roles changing and developing. And in some cases, I think it’s going to be for the better. And I think what we’re seeing is a lot of, because there’s all this kind of doubt and uncertainty and sort of threat, people are wanting to be shown the way, and people are wanting companies like our company and other companies like it to sort of lead the way in terms of how people who manage localized content can kind of implement AI. BS: Yeah. We’re seeing something similar in the content space as well. I know there was a big fear, certainly a couple of years ago, or even last year, that, oh, AI is going to take all the writing jobs because everyone saw what ChatGPT could do until they really started peeling back the layers and go, well, this is great. It spit out a bunch of words, it sounds great, but it really doesn’t say anything. It just kind of glosses over a lot of information and kind of presents you with the summary. But what we’re seeing now is that a lot of people, at least on the writing side, yeah, they’re using AI as a tool to automate away a lot of the mechanical bits of the work so that the writers can focus on quality. SM: We’re seeing exactly the same thing. I had a customer say to me she wants AI to do the dishes while she concentrates on writing the poetry. So it is the mundane stuff, the stuff that has to be done, but it’s not that exciting. It’s mundane, it’s repetitive. Those have always been the tasks that have been first in line to be automated, first in line to be removed, first in line, to be improved. And I think that’s what we’re seeing with AI.  BS: So on the plus side, you have AI potentially doing the dishes for you, while you’re writing poetry or learning to play the piano, what are some of the pitfalls that you’re seeing with regard to AI and translation? SM: I think there’s a few, and I think it depends on whereabouts AI is used, Bill, in the workflow. I think the very active translation itself is a very, very common use now of AI. But I think there’s some kind of a, I’m going to call them translation adjacent tasks as well, like we’ve mentioned with the entire workflow. So I think the answer would depend on that. But I think one of the biggest pitfalls of AI, and it was the same again, 2009 when I joined the industry and friends of mine had this new thing in their pocket called Google Translate. One of the pitfalls was, well, it’s not always right. It’s not always accurate. And even though the technology has come on leaps and bounds since then, and you had neural NT before large language models, it still isn’t always accurate. And I think you mentioned it before, it does almost always sound smooth and fluid and almost like it sounds like it’s very polished, and it

    34 min

  4. 10/06/2025

    From classrooms to clicks: the future of training content

    AI, self-paced courses, and shifting demand for instructor-led classes—what’s next for the future of training content? In this podcast, Sarah O’Keefe and Kevin Siegel unpack the challenges, opportunities, and what it takes to adapt. There’s probably a training company out there that’d be happy to teach me how to use WordPress. I didn’t have the time, I didn’t have the resources, nothing. So I just did it on my own. That’s one example of how you can use AI to replace some training. And when I don’t know how to do something these days, I go right to YouTube and look for a video to teach me how to do it. But given that, there are some industries where you can’t get away with that. Healthcare is an example—you’re not going to learn how to do brain surgery that someone could rely on with AI or through a YouTube video. — Kevin Siegel Related links: Is live, instructor-led training dying? (Kevin’s LinkedIn post) AI in the content lifecycle (white paper) Overview of structured learning content IconLogic LinkedIn: Kevin Siegel Sarah O’Keefe 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. SO: 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 SO: Hi, everyone, I’m Sarah O’Keefe. I’m here today with Kevin Siegel. Hey, Kevin. KS: Hey, Sarah. Great to be here. Thanks for having me. SO: Yeah, it’s great to see you. Kevin and I, for those of you that don’t know, go way back and have some epic stories about a conference in India that we went to together where we had some adventures in shopping and haggling and bartering in the middle of downtown Bangalore, as I recall. KS: I can only tell you that if you want to go shopping in Bangalore, take Sarah. She’s far better at negotiating than I am. I’m absolutely horrible at it. SO: And my advice is to take Alyssa Fox, who was the one that was really doing all the bartering. KS: Really good. Yes, yes. SO: So anyway, we are here today to talk about challenges in instructor-led training, and this came out of a LinkedIn post that Kevin put up a little while ago, which will include in the show notes. So Kevin, tell us a little bit about yourself and IconLogic, your company and what you do over there. KS: So IconLogic, we’ve always considered ourselves to be a three-headed dragon, three-headed beast, where we do computer training, software training, so vendor-specific. We do e-learning development, and I write books for a living as well. So if you go to Amazon, you’ll find me well-represented there. Actually, one of the original micro-publishers on this new platform called Amazon with my very first book posted there called, “All This PageMaker, the Essentials.” Yeah, did I date myself for that reference? Which led to a book on QuarkXPress, which led to Microsoft Office books. But my bread and butter books on Amazon even today are books on Adobe Captivate, Articulate Storyline, and TechSmith Camtasia. I still keep those books updated. So publishing, training, and development. And the post you’re talking about, which got a lot of feedback, I really loved it, was about training and specifically what I see as the demise of our training portion of our business. And it’s pretty terrifying. I thought it was just us, but I spoke with other organizations similar to mine in training, and we’re not talking about a small fall-off of training. 15, 20% could be manageable. You’re talking 90% training fall off, which led me to think originally, “Is it me?” Because I hadn’t talked to the other training companies. “Is it us? I mean, we’re dinosaurs at this point. Is it the consumer? Is it the industry?” But then I talked to a bunch of companies that are similar to mine and they’re all showing the same thing, 90% down. And just as an example of how horrifying that is, some of our classes, we’d expect a decent-sized class, 10, a large class, 15 to 18. Those were the glory days. Now we’re twos and threes, if anyone signs up at all. And what I saw as the demise of training for both training companies and trainers, if you’re a training company and you’re hiring a trainer, one or two people in the room isn’t going to pay the bills. Got to keep the lights on with your overhead running 50%, 60%, you know this as a business person, but you’ve got to have five or six minimum to pay those bills and pay your trainer any kind of a rate. SO: So we’re talking specifically about live instructor-led, in-person or online? KS: Both, but we went more virtual long before the pandemic. So we’ve been teaching more virtual than on-site for 30 years. Well, not virtual 30 years, virtual wasn’t really viable until about 20 years ago. So we’ve been teaching virtual for 20 years. The pandemic made it all the more important. But you would think that training would improve with the pandemic, it actually got even worse and it never recovered. So the pandemic was the genesis of that spiral down. AI has hastened the demise. But this is instructor-led training in both forms, virtual and on-site. I think even worse for on-site. SO: So let’s start with pandemic. You’re already doing virtual classes, along comes COVID and lockdowns and everything goes virtual. And you would think you’d be well-positioned for that, in that you’re good to go. What happened with training during the pandemic era when that first hit? KS: When that pandemic first hit, people panicked and went home and just hugged their families. They weren’t getting trained on anything. So it wasn’t a question of, were we well-positioned to offer training? Nobody wanted training, period. And this was, I think if you pull all training companies, well, there are certain markets where you need training no matter what. Healthcare as an example, they need training. Security, needed training. But for the day-to-day operations of a business, people went home and they didn’t work for a long time. They were just like, “The world is ending.” And then, oh, the world didn’t end. So now they’ve got to go back to work, but they didn’t go back to work for a long time. Eventually people got back to work. Now, are you on-site back to work or are you at home? That’s a whole nother thing to think about. But just from a training perspective, when panic sets in, when the economy goes bad, training is one of the first things, you get rid of it. Go teach yourself. And the teaching yourself part is what has led to the further demise of training, because you realize I can teach myself on YouTube. At least I think I can. And I think when you start teaching yourself on your own and you think you can, it becomes, the training was good enough. So if you said, “Let’s focus on the pandemic.” That’s what started it, the downward spiral. But we even saw the downward spiral before the pandemic, and it was the vendors that started to offer the training that we were offering themselves. SO: So instead of a third-party, certainly a third-party, mostly independent organization offering training on a specific software application, the vendors said, “We’re going to offer official training.” KS: Correct. And it started with some of these vendors rolling out their training at conferences. And I attended these conferences as a speaker. I won’t name the software, I won’t name the vendor, but I would just tell you I would go there and I would say, “Well, what’s this certificate thing you’re running there?” It’s a certificate of participation. But as I saw people walking around, they would say, “I’m now certified.” And I go, “You’re not certified after a three-hour program. You now have some knowledge.” They thought they were certified and experts, but they wouldn’t know they weren’t qualified until told to do a job. And then they would find out, “I’m not qualified to do this job.” But that certificate course, which was just a couple of hours by this particular vendor, morphed into a full day certificate. They were charging now a lot of money for it, which morphed into a multi-day thing, which now has destroyed any opportunity for training that we have. And that’s when I started noticing a downward spiral. Tracking finances, it would be your investments going down, down, down, down this thing. It’s like a plane, head and nose down. SO: And we’ve seen something similar. I mean, back in the day, and I do actually… So for those of you listening at home that are not in this generation, PageMaker was the sort of grandparent of InDesign. I am also familiar with PageMaker and I think my first work in computer stuff was in that space. So now we’ve all dated ourselves. But back in the day we did a decent amount of in-person training. We had a training classroom in one of our offices at one point. Now, we were never as focused on it as you are and were, but we did a decent business of public-facing, scheduled two-day, three-day, “Come to our office and we’ll train you on the things.” And then over time, that kind of dropped off and we got away from doing training because it was so difficult. And this is longer ago than you’re talking about. So the pattern that you’re describing where instructor-led in-person training, a classroom training with everybody in the same room kind of got disrupted a while back. We made a decent l

    32 min

  5. 09/22/2025

    From PowerPoint to possibilities: Scaling with structured learning content

    What if you could escape copy-and-paste and build dynamic learning experiences at scale? In this podcast, host Sarah O’Keefe and guest Mike Buoy explore the benefits of structured learning content. They share how organizations can break down silos between techcomm and learning content, deliver content across channels, and support personalized learning experiences at scale. The good thing about structured authoring is that you have a structure. If this is the concept that we need to talk about and discuss, here’s all the background information that goes with it. With that structure comes consistency, and with that consistency, you have more of your information and knowledge documented so that it can then be distributed and repackaged in different ways. If all you have is a PowerPoint, you can’t give somebody a PowerPoint in the middle of an oil change and say, “Here’s the bare minimum you need,” when I need to know, “Okay, what do I do if I’ve cross-threaded my oil drain bolt?” That’s probably not in the PowerPoint. That could be an instructor story that’s going to be told if you have a good instructor who’s been down that really rocky road, but again, a consistent structure is going to set you up so that you have robust base content. — Mike Buoy Related links: AEM Guides Overview of structured learning content CompTIA accelerates global content delivery with structured learning content (case study) Structured learning content that’s built to scale (webinar) LinkedIn: Mike Buoy Sarah O’Keefe 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 Sarah O’Keefe: Hi everyone, I’m Sarah O’Keefe. I’m here today with Mike Buoy. Hey, Mike. Mike Buoy: Good morning, Sarah. How are you? SO: I’m doing well, welcome. For those of you who don’t know, Mike Buoy is the Senior Solutions Consultant for AEM Guides at Adobe since the beginning of this year of 2025. And before that had a, we’ll say, long career in learning. MB: Long is accurate, long is accurate. There may have been some gray hair grown along the way, in the about 20-plus years. SO: There might have been. No video for us, no reason in particular. Mike, what else do we need to know about you before we get into today’s topic, which is the intersection of techcomm and learning? MB: Oh gosh, so if I think just quickly about my career, my background’s in instructional design, consulting, instructor, all the things related to what you would consider a corporate L&D, moving into the software side of things into the learning content management space. And so what we call now component content management, we, when I say we, those are all the different organizations I’ve worked for throughout my career, have been focused in on how do you take content that is usually file-based and sitting in a SharePoint drive somewhere, and how do you bring it in, get it organized so it’s actually an asset as opposed to a bunch of files? And how do you take care of that? How do you maintain it? How do you get it out to the right people at the right time and the right combination, all the rights, all the right nows, that’s really the background of where I come from. And that’s not just in learning content; at the end of the day, learning content is often the technical communication-type content with an experience wrapped around it. So it’s really a very fun retrospective when you look back on where both industries have been running in parallel and where they’re really starting to intersect now. SO: Yeah, and I think that’s really the key here. When we start talking about learning content, structured authoring, techcomm, why is it that these things are running in parallel and sitting in different silos? What’s your take on that? Why haven’t they intersected more until maybe now we’re seeing some rumblings of maybe we should consider this, but until now it’s been straight up, we’re learning and your techcomm, or vice versa, and never the twain shall meet, so why? MB: Yeah, and it’s interesting, when you look at most organizations, the two major silos that you’re seeing, one is going to be product. So whether it’s a software product, a hardware product, an insurance or financial product, whatever that product is, technical communication, what is it? How do you do it? What are all the standard operating procedures surrounding it? That all tends to fall under that product umbrella. And then you get to the other side of the other silo, and that’s the hey, we have customers, whether those customers are our customers or the internal customers, our own employees that we need to trade and bring up the speed on products and how to use them, or perhaps even partners that sit there. And so, typically, techcomm is living under the product umbrella, and L&D is either living under HR or customer success or customer service of some sort, depending on where they’re coming from. Now in the learning space you, over the last probably decade or so, seeing where there’s a consolidation between internal and external L&D teams and having them get smarter about, what are we building, how are we building it, who are we delivering it to, and what are all those delivery channels? And then when I think about why are they running in parallel, well, they have different goals in mind, right? techcomm has to ship with the product and service and training ideally is doing that, but is often, there’s a little bit of a lag behind, “Okay, we ship the thing, how long is it before we start having all the educational frameworks around it to support the thing that was shipped?” And so I think leadership-wise, very different philosophies, very different principles on that. techcomm, very much focused on the knowledge side of things. What is it? How do you do it? What are all the SOPs? And L&D leans more towards creating a learning experience around, “Okay, well here’s the knowledge, here’s the information, how do we create that arc going from I’m a complete novice to whatever the next level is?” Or even, I may be an expert and I need to learn how to apply this to get whatever new changes there are in my world and help me get knowledgeable and then skilled in that regard. So I think those are kind the competing mindsets and philosophies as well as, I won’t say competing, but parallel business organization of why we don’t usually see those two. And if we think about from a workflow perspective, you have engineering or whoever’s building the product, handing over documentation of what they’re building to techcomm and techcomm is taking all of that and then building out their documentation, and then that documentation then gets handed to L&D for them to then say, “Well, how do we contextualize this and build all the best practices around it and recommendations and learning experiences?” So there is a little bit of a waterfall effect for how a product moves through the organization. I think those are the things that really contribute to it being siloed and running in parallel. SO: Yeah. And I mean many, many organizations, the presence of engineering documentation or product design documentation is also a big question mark, but we’ll set that aside. And I think the key point here is that learning content, and you’ve said this twice already, learning content in general and delivery of learning content is about experience. What is the learning experience? How does the learner interact with this information and how do we bring them from, they don’t understand anything to they can capably do their job? The techcomm side of things is more of a point of need. You’re capable enough but you need some reference documentation or you need to know how to log into the system or various other things. But techcomm to your point, tends to be focused much less on experience and much more on efficiency. How do we get this out the door as fast as possible to ship it with the product? Because the product’s shipping and if you hold up the product because your documentation isn’t ready, very, very bad things will happen to you. MB: Bad, bad, very bad. SO: Not a good choice. MB: It’s not a good look. It’s not a good look. SO: Now, what’s interesting to me is, and this sort of ties into some of the conversations we have around pre-sales versus post-sales marketing versus techcomm kinds of things, as technical content has moved into a web experience, online environment, and all the rest of it, it has shifted more into pre-sales. People read technical documentation, they read that content to decide whether or not to buy, which means the experience matters more. And conversely, the learning content has fractured into classroom learning and online instructor led and e-learning a bunch of things I’m not even going to get into, and so they have fractured into multi-channel. So they evolved from classroom into lots of different channels for learning where techcomm evolved from print into lots of different channels, but online and so the two are kind of converging where techcomm needs to be more interested in experience and learning content needs to be more interested in efficiency, which brings us then to, can we meet in the middle and what does it look

    32 min

  6. 08/11/2025

    Every click counts: Uncovering the business value of your product content

    Every time someone views your product content, it’s a purposeful engagement with direct business value. Are you making the most of that interaction? In this episode of the Content Operations podcast, special guest Patrick Bosek, co-founder and CEO of Heretto, and Sarah O’Keefe, founder and CEO of Scriptorium, explore how your techcomm traffic reduces support costs, improves customer retention, and creates a cohesive user experience. Patrick Bosek: Nobody reads a page in your documentation site for no reason. Everybody that is there has a purpose, and that purpose always has an economic impact on your business. People who are on the documentation site are not using your support, which means they’re saving you a ton of money. It means that they’re learning about your product, either because they’ve just purchased it and they want to utilize it, so they’re onboarding, and we all know that utilization turns into retention and retention is good because people who retain pay us more money, or they’re trying to figure out how to use other aspects of the system and get more value out of it. There’s nobody who goes to a doc site who’s like, “I’m bored. I’m just going to go and see what’s on the doc site today.” Every person, every session on your documentation site is there with a purpose, and it’s a purpose that matters to your business. Related links: Heretto Contact Heretto to walk through their support evaluation sheet with an expert! The business case for content operations (white paper) Curious about the value of structured content operations in your organization? Use our content ops ROI calculator. Get monthly insights on structured content, futureproof content operations, and more with our Illuminations newsletter LinkedIn: Patrick Bosek Sarah O’Keefe 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 process 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: Hi, everyone, I’m Sarah O’Keefe and I’m here today with our guest, Patrick Bosek, who is one of the founders and the CEO of Heretto. Welcome. Patrick Bosek: Thanks, Sarah. It’s lovely to be here. I think this is may be my third or fourth time getting to chat with you on the Scriptorium podcast. SO:  Well, we talk all the time. This is talking and then we’re going to publi- no, let’s not go down that road. Of all the things that happen when we’re not being recorded. Okay. Well we’re glad to have you again and looking forward to productive discussion here. The theme that we had for today was actually traffic and I think web traffic and why you want traffic and where this is going to go with your business case for technical documentation. So, Patrick, for those of you that have not heard from you before, give us a little bit of background on who you are and what Heretto is and then just jump right in and tell us about web traffic. PB: No small requests from you, Sarah. SO: Nope. PB: So I’m Patrick Bosek. I am the CEO and one of the co-founders of Heretto. Heretto is a CCMS based on DITA. It’s a full stack that goes from the management and authoring layer all the way up to actually producing help sites. So as you’re moving around the internet and working with technology companies, primarily help_your_product.com or help_your_company.com, it might be powered by Heretto. That’s what we set out to do. We set out to do it as efficiently as possible, and that gives me some insight into traffic, which is what we’re talking about today, and how that can become a really important and powerful point when teams are looking to make a case for better content operations, showing up more, producing more for their customers, and being able to get the funding that allows them to do all those great things that they set out to do every day. SO: So here we are as content ops, CCMS people, and we’re basically saying you should put your content on the internet, which is a fairly unsurprising kind of priority to have. But why specifically are you saying that web traffic and putting that content out there and getting people to use the content helps you with your sort of overall business and your overall business case for tech docs? PB: Yeah. So I want to answer that in a fairly roundabout way because I think it’s more fun to get there by beating around the bush. But I want to start with something that seems really obvious, but for some reason it isn’t in tech pubs. So first of all, if you went to an executive and you said, I can double the traffic to your website, and then you put a number in front of them, probably say a hundred thousand dollars, almost like any executive at any major organization is like a hundred thousand dollars, of course, I’ll double my web traffic. That’s a no-brainer. Right? And when they’re thinking of website, they’re thinking of the marketing site and how important traffic is to it. So intrinsically, everybody pays quite a bit of money and by transference puts a lot of value on the traffic that goes to the website and, as they should. It’s the primary way we interact with organizations asynchronously today. Digital experience is really important. But if you went to an executive and you said, I can double your traffic to your doc site, they would probably be like, wait a second. But that makes no sense because nobody reads the docs for no reason. I want to repeat that because I think that’s a really important thing for us, as technical content creators to not only understand, I think we understand it, but to internalize it and start to represent it more in the marketplace and to our businesses and to the other stakeholders. People might show up at your marketing site, because they misclick an advertisement. They might show up in your marketing site because they Googled something and your market and a blog like caught them and they looked at it. So there’s probably a lot of traffic where people are just curious. They’re just window shopping. Maybe they’re there by mistake. But nobody shows up at your documentation site. Nobody reads a page in your documentation site for no reason. Everybody that is there has a purpose and that purpose always has an economic impact on your business. People who are on the documentation site are either not utilizing your support, which means that they’re saving you a ton of money. It means that they’re learning about your product, either because they’ve just purchased it and they want to utilize it, so they’re onboarding, and we all know that utilization turns into retention and retention is good because people who retain pay us more money, or they’re trying to figure out how to use other aspects of the system and get more value out of it. There’s nobody who goes to a doc site who’s like, I’m bored. I’m just going to go and see what’s on the doc site today. So every person, every session on your documentation site is there with a purpose and it’s a purpose that matters to your business. So that’s why I want to start. That’s why it matters. That’s why I think traffic is important, but you look like you want to contribute here, so. SO: We talk about enabling content. Right? Tech docs are enabling content. They enable people to do a thing, and this is what you’re saying. People don’t read tech docs for fun. I know of, actually, I do know one person. One person I have met in my life who thought it was fun to read tech docs. One. PB: Okay. So to be fair, I also know somebody who loves reading release notes. SO: Okay. So two in the world. PB: But hang on, hang on. But this person, part of the thing is this person is an absolute, can I say fanboy, is that, they’re a huge fan of this product and they talk about this product in the context of the release notes. So even though this person loves the release notes, the release notes are a way that they go and generate word-of-mouth and they’re promoting your product because of the thing they saw in the release notes. The release notes are a marketing piece that goes through this person. All the people who are your biggest fans are going to tell people about that little thing they found in your release notes. Sorry. Anyways. SO: So again, they’re trying to learn. Okay. But, so two people in the universe that we know of read docs for fun. Cool. Everybody else is reading them, as you said, for a purpose. They’re reading them because they are blocked on something or they need information, usually it’s they need information. And then you slid in that when they do this, this is producing, providing value to the organization or saving the organization money. So what’s that all about? PB: Well, I mean there’s a number of ways to look at this. You want to start with the hard numbers, the accounting stuff, the stuff you can take the CFO. That stuff is actually, it’s pretty easy to do. You can do it in just a couple of lines. So every support ticket costs a certain amount of money. Somebody in your organization knows that number, if your organization is sufficiently large and sufficiently large is like 20 people probably. Maybe that’s not that small, but if you’re a couple hundred people, everybody knows what that number is. So it’s very easy to figure out how much it costs when somebody actually goes to the support. SO:

    31 min

  7. 08/04/2025

    AI in localization: What could possibly go wrong? (podcast)

    In this episode of the Content Operations podcast, Sarah O’Keefe and Bill Swallow unpack the promise, pitfalls, and disruptive impact of AI on multilingual content. From pivot languages to content hygiene, they explore what’s next for language service providers and global enterprises alike. Bill Swallow: I think it goes without saying that there’s going to be disruption again. Every single change, whether it’s in the localization industry or not, has resulted in some type of disruption. Something has changed. I’ll be blunt about it. In some cases, jobs were lost, jobs were replaced, new jobs were created. For LSPs, I think AI is going to, again, be another shift, the same that happened when machine translation came out. LSPs had to shift and pivot how they approach their bottom line with people. GenAI is going to take a lot of the heavy lifting off of the translators, for better or for worse, and it’s going to force a copy edit workflow. I think it’s really going to be a model where people are going to be training and cleaning up after AI. Related links: Going global: Getting started with content localization Lessons Japan taught me about content localization strategy Conquering content localization: strategies for success (podcast) The Scriptorium approach to localization strategy Get monthly insights on structured content, futureproof content operations, and more with our Illuminations newsletter 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 process 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. I’m Sarah O’Keefe, and I’m here today with Bill Swallow. Bill Swallow: Hey there. SO: They have let us out of the basement. Mistakes were made. And we have been asked to talk to you on this podcast about AI in translation and localization. I have subtitled this podcast, What Could Possibly Go Wrong? As always, what could possibly go wrong, both in this topic and also with this particular group of people who have been given microphones. So Bill. BS: They’ll take them away eventually. SO: They will eventually. Bill, what’s your generalized take right now on AI in translation and localization? And I apologize in advance. We will almost certainly use those two terms interchangeably, even though we fully understand that they are not. What’s your thesis? BS: Let’s see. It’s still early. It is promising. It will likely go wrong for a little while, at least. Any new model that translation has taken has first gone wrong before it corrected and went right, but it might be good enough. I think that pretty much sums up where I’m at. SO: Okay. So when we look at this … Let’s start at the end. So generative AI, instead of machine translation. Let’s walk a little bit through the traditional translation process and compare that to what it looks like to employ GenAI or AI in translation. BS: All right. So regardless of how you’re going about traditional translation, there is usually a source language that is authored. It gets passed over to someone who, if they’re doing their job correctly, has tools available to parse that information, essentially stick it in a database, perhaps do some matching against what’s been translated before, fill in the gaps with the translation, and then output the translated product. On the GenAI side, it really does look like you have a bit of information that you’ve written. And it just goes out, and GenAI does its little thing and bingo, you got a translation. And I guess the real key is what’s in that magic little thing that it does. SO: Right. And so when we look at best practices for translation management up until this point, it’s been, as you said, accumulate assets, accumulate language segment pairs, right? This English has been previously translated into German, French, Italian, Spanish, Japanese, Korean, Chinese. I have those pairs, so I can match it up. And keeping track of those assets, which are your intellectual property, you as the company put all this time and money into getting those translations, where are those assets in your GenAI workflow? BS: They’re not there, and that’s the odd part about it. SO: Awesome. So we just throw them away? What? BS: I mean, they might be used to seed the AI at first, just to get an idea of how you’ve talked about things in the past. But generally, AI is going to consume its knowledge, it’s going to store that knowledge, and then it’s going to adapt it over time. When it’s asked for something, it’s going to produce it with the best way it knows how, based on what it was given. And it’s going to learn things along the way that will help it improve or not improve over time. And that part right there, the improve or not improve, is the real catch in why I say it might be good enough but it might go wrong as well, because GenAI tends to … I don’t want to say hallucinate because it’s not really doing that at this stage. It’s taking all the information it has, it’s learning things about that information, and it’s applying it going forward. And if it makes an assumption based on new information that it’s fed, it could go in the wrong direction. SO: Yeah. I think two things here. One is that what we’re describing applies whether you have an AI-driven workflow inside your organization where you’re only allowing the AI to access your, for example, prior translation. So a very limited corpus of knowledge, or if you’re sending it out like all of us are doing, where you’re just shoving it into a public-facing translation engine of some sort and just saying, “Hey, give me a translation.” In the second case, you have no control over the IP, no control over what’s put in there and how it’s used going forward, and no control over what anyone else has put in there, which could cause it to evolve in a direction that you do or do not want it to. So the public-facing engines are very, very powerful because they have so much volume, and at the same time, you’re giving up that control. Whereas if you have an internal system that you’ve set up … And when I say internal, I mean private. It doesn’t have to be internal to your organization, but it might be that your localization vendor has set up something for you. But anyway, gated from the generalized internet and all the other people out there. BS: We hope. SO: Or the other content. You hope. Right. Also, if you don’t know exactly how these large learning models are being employed by your vendors, you should ask some questions, some very pointed questions. Okay, we’ll come back to that, but first I want to talk a little bit about pivot languages. So again, looking at traditional localization, you run into this thing of … Basically many, many, many organizations have a single-language authoring workflow and a multi-language translation workflow. So you write everything in English and then you translate. So all of the translations are target languages, they are downstream, they are derived from the English, et cetera. Now let’s talk a little bit about… First of all, what is a multilingual workflow? Let’s start there. What is that? BS: Okay. So yeah, the traditional model usually is author one language, which maybe 90% of the time is English, whether it’s being authored in an English-speaking country or not, and then it’s being pushed out to multiple different languages. In a multilingual environment, you have people authoring in their own native language, and it should be coming in and being translated out as it needs to be to all the other target languages. Traditionally, that has been done using pivot languages because infrastructures were built. It is just the way it is. It was built on English. English has been used as a pivot language more than any other language out there. There are some outliers that use a different pivot language for a very specific reason, but for the sake of this conversation, English is the predominant pivot language out there. SO: So I have a team of engineers in South Korea. They are writing in Korean. And in order to get from Korean to, let’s say, Italian, we translate from Korean to English and then from English to Italian, and English becomes the pivot language. And the generalized rationale for this is that there are more people collectively that speak Korean and English and then English and Italian than there are people that speak Korean and Italian. BS: With nothing in between, yeah. SO: With nothing in between. Right. Directly. So bilingual in those two languages is a pretty small set of people. And so instead of hiring the four people in the world that know how to do that, you pivot through English. And in a human-driven workflow, that makes an awful lot of sense because you’re looking at the question of where do I find English … Sorry, not English, but rather Italian and Korean speakers that can do translation work for my biotech firm. So I need a PhD in biochemistry that speaks these two languages. I think I’ve just identified a specific human in the universe. So that’s the old way. What is a multilingual workflow then? BS: So yeah, as we were discussing, the multilingual workflow is something where you have two, three, four different language sources tha

    29 min

  8. 07/21/2025

    Help or hype? AI in learning content

    Is AI really ready to generate your training materials? In this episode, Sarah O’Keefe and Alan Pringle tackle the trends around AI in learning content. They explore where generative AI adds value—like creating assessments and streamlining translation—and where it falls short. If you’re exploring how AI can fit into your learning content strategy, this episode is for you. Sarah O’Keefe: But what’s actually being said is AI will generate your presentation for you. If your presentation is so not new, if the information in it is so basic that generative AI can successfully generate your presentation for you, that implies to me that you don’t have anything interesting to say. So then, we get to this question of how do we use AI in learning content to make good choices, to make better learning content? How do we advance the cause? Related links: Synthetic audio example: Strategies for AI in technical documentation (podcast, English version) LearningDITA: DITA-based structured learning content in action (podcast) How CompTIA rebuilt its content ecosystem for greater agility and efficiency (webinar) Transform L&D experiences at scale with structured learning content (podcast) Overview of structured learning content Get monthly insights on structured content, futureproof content operations, and more with our Illuminations newsletter LinkedIn: Sarah O’Keefe Alan Pringle 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 process 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 Alan Pringle: Hey everybody, I am Alan Pringle, and today I’m talking to Sarah O’Keefe. Sarah O’Keefe: Hey everybody, how’s it going? AP: And today, Sarah and I want to discuss artificial intelligence and learning content. How can you apply artificial intelligence to learning content? We’ve talked a whole lot, Sarah, about AI and technical communication and product content, let’s talk more about learning and development and how AI can help or maybe not help putting together learning content. So how is it being used right now? Let’s start with that. Do you know of cases? I know of one or two, and I’m sure you do too. SO: Yeah. So the big news, the big push, is AI in presentations. So how can I use AI to generate my presentation? How can it help me put together my slides? Now, the problem with that from our point of view, for those of you that have been listening to what we’re saying about AI, this will be no surprise whatsoever, I think this is all wrong. It’s the wrong strategy, it’s the wrong approach. If you want to take AI and generate an outline of your presentation and then fill in that outline with your knowledge, that’s great, I think that’s a great idea. Also, if you have existing really good content and you want to take that content and generate slides from it, I don’t have a problem with that. But what’s actually being said is AI will generate your presentation for you. If your presentation is so not new, if the information in it is so basic that generative AI can successfully generate your presentation for you, that implies to me that you don’t have anything interesting to say. AP: And you’re going to say it with very pretty generated images and a level of authority that makes it sound like there’s something that’s actually there when it’s not. SO: Oh, yeah. It’ll look very plausible and authoritative and it will be wrong, because that’s how this generative stuff- AP: Or not even wrong, surface-skimmy, just nothing of any real value there. SO: Yeah. So then, we go into this question of, how do we use AI in learning content to make good choices, to make better learning content, how do we advance the cause? AP: Well, there’s that one case where we have done it, because we have our own learning site, LearningDITA.com, and we were trying to think about ways to apply AI to our efforts to create courses, to tell people how to use the DITA standard for content. And I think you and I both agree, one of the strengths of artificial intelligence is its ability to summarize and synthesize things, I don’t think that’s controversial. So if you think about writing assessments from existing content in a way that’s summarizing, so one of us suggested to our team, why don’t y’all try that and see what these AI engines can do to generate questions from our existing lesson content. And then, of course, we suggested that they—the people who were creating the courses—review them. So our folks reviewed them, and I think some of the questions were actually quite usable, decent. SO: And some of them were not. AP: True, this is true. SO: But the net of it was they saved a bunch of time, because they said, “Generate a bunch of assessment questions,” they went through them, they fixed the ones that were wrong, they improved the ones that were maybe not the greatest, they got a couple that were actually pretty usable. And so, it took less time to write the assessments than it would’ve taken to do that process by hand, to slowly go through the entire corpus to say, “Okay, what are the key objectives and how do I map that to the assessments?” So that’s a pretty good example, I think, of using generative AI, as you said, to summarize down, to synthesize existing content. On the LMS side, so when we start looking at learning management systems and how the learning content goes into the LMS and then is given or delivered to the learner, there are some big opportunities there, because if you think about what it means for me as a learner, as a person taking the course, to work my way through course material, maybe the assumptions that the course developer made about my expertise were too optimistic. I’m really struggling with this content, it’s trying to teach me how to use Photoshop and I am just not good at Photoshop. There’s this idea of adaptive learning, this is not an AI concept, the idea behind adaptive learning is that if you’re doing really well, it goes faster. If you’re struggling, it goes deeper, or maybe you do better with videos than you do with text, or vice versa. It’s that adapt to the learner and to the learner’s needs in order to make the learning more effective. Now, if you think about that, that is a matter of uncovering patterns in how the learner learns and then delivering a better fit for those patterns. Well, that’s AI. AI and machine learning do a great job of saying, “Oh, you seem to be preferring video, so I’m going to feed you more video.” Now, we can do this by hand or we can build it in with personalization logic, but you can also do this at scale with AI and machine learning. So there are definitely some opportunities to improve adaptive learning with an AI backbone. AP: I think it’s worth noting at this point, when you’re talking about gathering the data to make, I hate to, I’m going to personalize AI, so it can make these decisions or do the synthesis, there’s got to be intelligence that’s built into your content, and that goes all the way back to the content creation, going back from the presentation layer, back to how you’re creating your content. And again, this loops back, in my mind, to the idea of building in that intelligence with structured content, that is your baseline. SO: Yeah. I know we’re just relentless on this drum of you need structured content for learning content, but it’s because of all these use cases, because as you try to scale this stuff, this is what you’re going to run into. I also see a huge opportunity for translation workflows specifically for learning content. So if you look at translation and multilingual delivery, there’s a lot of AI and machine learning going on in machine translation. So now, we think a little bit about what that means for learning content, and of course, all of the benefits that you get just in general from machine translation still apply, but the one that I’m looking at that I think would be really, really interesting to apply to learning is learning has a lot of audio in it, audio and video, but specifically audio, and audio typically is going to be bound to a language. You’re going to have a voiceover, you’re going to have a person saying, “Here’s what you need to know, and I’m going to show you this screenshot,” or, “I’m going to show you how to operate this machine.” And so, you’ve got audio and potentially captions that are giving you the text or the audio that goes with that video. Okay, well, we can translate the captions, that’s relatively easy, but what about the voiceover? And the answer could be that you do synthetic voiceovers. So you take your original, let’s say, English audio and you turn it into French, Italian, German, Spanish or whatever else you need, but you synthesize the voice instead of re-recording. Now, is it going to be as good as a human, an actual human person who has expression and emotion in their delivery? No. Is it better than the alternative where you don’t provide it in the target language at all? Probably, yes. And when we start talking about machines, “Here is how to safely operate this machine,” the pretty good synthetic voice in target language is probably better than, “Here it is in English, deal with it,” or, “Here it is in English with a translated caption

    18 min
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Scriptoriums delivers industry-leading insights for global content operations.

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    Scriptorium - The Content Strategy Experts
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    2017 - 2025
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    © Scriptorium Publishing Services, Inc.
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    Content Operations