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Content Operations

Content Operations

著者： Scriptorium - The Content Strategy Experts
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The Content Operations podcast from Scriptorium delivers industry-leading insights for scalable, global, AI-optimized content.© Scriptorium Publishing Services, Inc. 経済学
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  • The debt crisis: AI edition

    The debt crisis: AI edition
    2026/07/13
    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 ReplacedTaming AI: Using AI for content conversion at scale (podcast) LinkedIn: Bill SwallowAlan 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...
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    22 分
  • From ad hoc to autonomous: The AI content ops maturity model

    From ad hoc to autonomous: The AI content ops maturity model
    2026/06/22
    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 lifecycleEnterprise content strategy maturity model LinkedIn: Sarah O’KeefeBill 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 ...
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    18 分
  • Tool selection and the unpredictable variable

    Tool selection and the unpredictable variable
    2026/06/01
    How do you really choose the right documentation tool? In this podcast episode, Sarah O’Keefe (Scriptorium) talks with Paweł Kowaluk and Michał Skowron (Guidewire Software) about building a successful tool selection process, the realities of docs as code, and what happens when the technology becomes the unpredictable variable. Paweł Kowaluk: It’s funny how programming used to be deterministic, and it was the people who were messy. We always knew that people are going to be whimsical and maybe harder to rein in, but the technology is going to be predictable. Whereas now, technology is not predictable anymore, and you give it a prompt and you hope it’s going to do what you want. You adjust the system prompts and change the weight of things which are retrieved versus metadata, et cetera, and it doesn’t always work the way you expect it to. Sarah O’Keefe: And now the people are being asked to be the deterministic layer, right? To be the QA on top of the AI. Paweł Kowaluk: That’s actually very insightful. I like that. That is true. The human in the loop or whatever you call it, that’s supposed to be the voice of reason. Related links: Scriptorium: AI in the content lifecycleTech Writer Koduje podcastTech Writer Koduje: DITA as code – a modern approach to the classic standardTech Writer Koduje: Are people abandoning docs as code?Tech Writer Koduje: A tech writing CCMS can also be a broken promise LinkedIn: Host: Sarah O’KeefeGuest: Paweł KowalukGuest: Michał SkowronTech Writer Koduje LinkedIn profile 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: Hey, everyone. I’m Sarah O’Keefe, and welcome to the podcast. In this episode, we are going to talk about tool selection with a couple of special guests. With me today are Paweł Kowaluk, who is a software architect at Guidewire Software, and Michał Skowron, who is a documentation tools developer, also at Guidewire. Both of them are based in Poland. Welcome. Paweł Kowaluk: Hi. Michał Skowron: Hello. SO: I am glad to have you. For those of you on this podcast that speak Polish, you’re probably already aware that they have the one and only techcomm podcast in Polish that is available out there, and Michał and Paweł are also experts on doc process and tool selection, so that’s what we wanted to focus on today. So I will start and throw it to Michał and ask you the big picture question, which is what does a good tool selection process actually look like? MS: For me, good selection tool process would be divided in three stages. The first one would be gathering requirements, looking what’s out there, defining what you want to basically achieve with this new tool. Then I would go to a pilot project where you can actually test the selected tool in the real world. Manufacturers and producers of software will tell you that it can do anything and it will promise that, “Okay, you can meet all your requirements easily and we can fix that, we can improve that, we can adjust that,” so everything can be done is usually what we hear, but then you want to test it in real world on a real project, so that will be a pilot project for you and your team. And the third phase that depends on the outcome of the second phase, which is you either productize the selected solution or you just say, “Okay, that was a bad choice and we don’t need that.” Then we need to go back to the first stage and then say, “Okay, we need to select another tool,” and again, requirements, et cetera, et cetera. So for me, that’s the whole process, and the first stage would be probably the longest one because you need to make sure that you are meeting all your goals. SO: So what’s the most common reason that a pilot doesn’t succeed, that you have to go back and say, “That didn’t work. We have to try something different”? MS: It’s usually because you didn’t see everything when you were planning. For example, you have some projects that are very specific or you didn’t see all the problems or things that are coming your way. It’s hard to say exactly what the reason is, but it can be multiple reasons. For example, using of, I don’t know, branching, let’s say, in a specific tool. When you have multiple versions of your product and you want to keep them separate when it ...
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    42 分
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