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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to a lot of the technical debt that we’ve been seeing over the years in content operations in general, whether you have outdated authoring formats or content that’s not being updated on a regular basis, new delivery formats not necessarily meeting the needs of the users, and so forth.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
  • Taming AI: Using AI for content conversion at scale

    Taming AI: Using AI for content conversion at scale
    2026/05/18
    AI promises to transform content conversion, but what does it actually look like when you’re processing thousands of documents a day? In this episode, Sarah O’Keefe (Scriptorium) and Rich Dominelli (DCL) dig into the real-world challenges of using AI for large-scale structured content conversion. Rich Dominelli: If you have millions of articles and you’re asking the AI, ‘What did we do for this project six months ago?” The AI has to find those articles, pull the relevant information out of those articles, summarize it, and hand it back to you. The best way of doing that is to give extra signals to the AI, structured relevant bits of information, front matter, back matter, publication date, keywords, abstract, that allows the AI to query the corpus and get the relevant chunks out of that corpus in a very quick manner. Then, it can summarize what those chunks are. So the AI almost becomes the user interface over that corpus. But to find that data in the first place, structured content is key. Structured content is key when you’re dealing with big indexes and the web, and it’s the same with AI. Related links: Defeating Nondeterminism in LLM Inference (white paper)Data Conversion Laboratory (DCL)Scriptorium, Machine experience (MX): Making content work for humans and machines (podcast) LinkedIn: Host: Sarah O’KeefeGuest: Rich Dominelli Transcript: Disclaimer: This is a machine-generated transcript with edits. Introduction with ambient background music Christine Cuellar: From Scriptorium, this is Content Operations, a show that delivers industry-leading insights for global organizations. Bill Swallow: In the end, you have a unified experience so that people aren’t relearning how to engage with your content in every context you produce it. Sarah O’Keefe: Change is perceived as being risky; you have to convince me that making the change is less risky than not making the change. Alan Pringle: And at some point, you are going to have tools, technology, and processes that no longer support your needs, so if you think about that ahead of time, you’re going to be much better off. End of introduction Sarah O’Keefe: Hey everyone, I’m Sarah O’Keefe and I’m here today with Rich Dominelli who is a Senior Developer and Architect at DCL. Rich, welcome. Rich Domineli: Hi, thank you for having me. SO: Glad to have you. We were talking before we hit the record button, and you described yourself as a perhaps hopeful AI evangelist. RD: Yeah, I am well and thoroughly immersed in the AI game at DCL and using it and plus I play with AI assistants at home. I’m enthusiastic about the future of AI, sometimes disappointed about the present. SO: So DCL, as I think many of our listeners know, is focused on conversion at scale, which to me makes a great use case for AI because ultimately conversion is about edge cases and about inconsistency, right? If everything was 100% consistent, conversion would be pretty easy. RD: Yeah, no, DCL does a lot of structured content generation out of unstructured data, and the creativity, especially in the academic space, of what that unstructured data looks like is sometimes nightmarish. So the AI lets us, does a lot of the heavy lifting for us when it comes to looking for particular items, identifying concrete data points within the documents, pulling things like authors and affiliation, front matter type information, and back matter type information out of the documents and in automated fashion. It can be painful from time to time, but it’s definitely helped. SO: Yeah, so this is, think, you know, the reality of working with AI and working with it in a production environment in order to address all these weird edge cases and what’s going on. So tell us a little bit about how you’re using AI in, you know, these conversion use cases. What does it look like to go in there and start applying some of these tools that we have? RD: So, I mean, typically our flows work in a way where we’re coming in with a PDF or a Word document or some other unstructured format. We take it, we reformat it into a version that’s more AI-friendly, like Markdown, for example. And that’s usually the first step we’re doing when we’re looking for information to pull out of it like front matter. It’s a very common use case. If you look at academic papers, the front matter, the authors and the affiliations that are on that paper can be formatted in more ways than I could list out during the course of this podcast. It’s kind of crazy. So what we’ve started doing, and we’ve been doing this for a couple of years now, is we’re using the AI, we’re handing it the Markdown document, and we’re saying we need to list authors and affiliations, please extract it for us. Now, naively, when we started that process, we assumed that the AI would give us a consistent list of authors and affiliations. And sometimes it does. But every time you do that call, you’ll get it in a ...
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    25 分
  • Machine experience (MX): Making content work for humans and machines

    Machine experience (MX): Making content work for humans and machines
    2026/05/04
    Your website may look great to humans, but can machines understand it? In this episode, Sarah O’Keefe (Scriptorium) and Tom Cranstoun (Digital Domain Technologies) explore the emerging discipline of machine experience (MX). Sarah and Tom discuss what AI agents actually encounter when they visit your web pages, why microdata and metadata are critical, and what content creators must do to ensure content is consumable for both human and machine audiences. Tom Cranstoun: Humans are looking for pictures, they’re looking for text, and they can infer. You may think, “Well, we’ve already added information on the page,” but by putting it in as microdata, it doesn’t appear on the page for the humans. It appears on the page for the machine. I think that that’s a critical distinction. We are trying to design for both. We don’t want to overload a human with information, but we do want to give the machine as much information as it can take. Related links: The GatheringDigital Domain TechnologiesMX booksThe Scriptorium Content Ops manifesto LinkedIn: Host: Sarah O’KeefeGuest: Tom Cranstoun Transcript: Disclaimer: This is a machine-generated transcript with edits. Introduction with ambient background music Christine Cuellar: From Scriptorium, this is Content Operations, a show that delivers industry-leading insights for global organizations. Bill Swallow: In the end, you have a unified experience so that people aren’t relearning how to engage with your content in every context you produce it. Sarah O’Keefe: Change is perceived as being risky; you have to convince me that making the change is less risky than not making the change. Alan Pringle: And at some point, you are going to have tools, technology, and processes that no longer support your needs, so if you think about that ahead of time, you’re going to be much better off. End of introduction Sarah O’Keefe: Hey, everyone. I’m Sarah O’Keefe. Today, our guest is Tom Cranstoun, who is founder of a machine experience, or MX community, called The Gathering. He has a couple of books on MX and is currently a consultant operating as Digital Domain Technologies. Tom, after 53 years in the business, some experience with AEM at very, very large companies, including a huge project at Nissan, has turned his attention to the question of how machines, which is to say AI agents, interoperate with the current public-facing web. And so today, Tom, I’m delighted to have you on to talk with you about machine experience, or MX, and what this all means as we move forward in this brave new AI world. So welcome. Tom Cranstoun: Thank you, Sarah. I’m very pleased to be with you today. SO: I am delighted to have you. So I guess we’ll start with the extreme basics here, which is what is machine experience, or MX? TC: Yeah. MX, well, to my definition, machine experience is like user experience, but it’s for machines. Machines cannot ask a friend for help if something goes wrong when they’re browsing a website. They can’t turn to a partner and say, “What do you think this means?” They can’t retry a failing form input because they will just go through the same mechanical patterns to try and carry on throughout the web journeys. Therefore, machine experience is thinking about what elements one must put on a webpage to help a machine understand and action the final goal of the webpage, whether that be a CTA that lets you purchase something, or an information document that lets you know about a government policy, or a charity good, whatever the author of the page is trying to get across to the audience. SO: And so at a high level, what does it look like to build out machine experience? What are some examples of things that you need to put onto a webpage to accommodate the machine that’s reading it? TC: Well, the very first level is the disabilities angle, things like the Americans with Disabilities Act, that kind of WCAG, W-C-A-G, the accessibility work. The more accessibility information is on the page, the more the machine can understand the background of the page. So machine experience and accessibility are pretty much at the top level, the same sort of thing. If you put in JSON-LD, microdata, and you enrich your pages with the things that Americans with Disabilities Act would like, you’re actually helping a machine understand the page. So that is the top-level constraint. When you go below that level, you need to give the machine lots of information about your product, not just the thing that a human wants when it’s glancing at the page now, and as you go through the journeys, things will be added on. Humans can only take in two or three items at a time, so we design pages to reveal what is happening. You go to a catalog, to a product, to a variation, to a purchase, four different steps. Each step introduces different pricing and concepts. It’s best to feed the machine on the page that the machine lands on with all of the information ...
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    20 分
  • Make the move successful: Replatforming content ops

    Make the move successful: Replatforming content ops
    2026/04/27
    Replatforming your content operations isn’t just about swapping systems. In this episode, Alan Pringle and Bill Swallow share what organizations must consider to successfully replatform. From navigating technical debt, system integration, and the people caught in the middle, they discuss change management, technical debt, and why your exit strategy should be part of the plan from day one. Software isn’t forever. Systems come, systems go, they get improved. Your requirements are ever changing with the content that you need to manage. Not thinking about your next jump is really to your detriment. — Bill Swallow Related links: Replatforming structured contentYour tech expertise + our CCMS knowledge = replatforming success (case study)Cutting technical debt with replatforming (podcast)Replatforming with localization in mind LinkedIn: Alan PringleBill Swallow Transcript: Disclaimer: This is a machine-generated transcript with edits. Introduction with ambient background music Christine Cuellar: From Scriptorium, this is Content Operations, a show that delivers industry-leading insights for global organizations. Bill Swallow: In the end, you have a unified experience so that people aren’t relearning how to engage with your content in every context you produce it. Sarah O’Keefe: Change is perceived as being risky; you have to convince me that making the change is less risky than not making the change. Alan Pringle: And at some point, you are going to have tools, technology, and processes that no longer support your needs, so if you think about that ahead of time, you’re going to be much better off. End of introduction Alan Pringle: Hey everybody, I am Alan Pringle, and today I want to talk with Bill Swallow about content operations and replatforming. Hey Bill, how are you? Bill Swallow: Good, how are you doing? AP: Good. So I guess we should start this by saying the reason why we want to talk about replatforming is really we have done a few replatforming projects. We’ve had some prospects reach out who are interested in doing it. So I guess we need to explain what it is and some of the things you have to think about when you’re going through the process. So if you would not mind, would you define what we mean by replatforming content operations? BS: Sure. So generally when I talk about replatforming, it’s in the context of a company having one system in place and maybe it’s time has come and they need to move into a new one. So it’s the entire process of determining what type of system you’re going to need, what your requirements are for that and being able to lift everything up from the old system that you want to carry forward and put it in the new system, configuring it and what have you to get it to work going forward. AP: So we’re not talking about using a whole new technology or a whole new platform. It’s shifting to a similar platform for some of the reasons that you just mentioned. And I think that’s another thing. There are several reasons why a company might want to do this. And I know our clients have had various reasons for doing this. Let’s focus on those for a little bit. One of them, I know you kind of already touched on this. Sometimes you just outgrow a system. It just… that’s how it is. So let’s start with that kind of, it’s not sustainable anymore because you’re bigger, too big now for what that system can do. BS: Sure, either you’ve outgrown it or it’s approaching end-of-life or it’s just not meeting the needs that you had five or ten years ago when you bought the system. So there are a lot of different factors there, but basically it comes down to what are your requirements and is it meeting your requirements? AP: Right. BS: Are you able to get the things done that you need to do given the fact that, you know, the world is quite different now than it was five or 10 years ago. AP: Exactly, and there’s another angle here too that I think we need to briefly mention is that sometimes you’re gonna have two sets of requirements because two companies can merge or there can be an acquisition and then all of a sudden you’ve got two content operations platforms that are pretty much doing the same exact thing and I guarantee you the IT department is not gonna have that. BS: Absolutely. AP: So there could be a situation where you’ve got two, and one of those is going to go away. And in some cases, and we should talk about this too, it’s not necessarily about picking one. It’s not uncommon to go to a whole other one. So there is quote, “No loser.” That’s also an option. BS: That’s very common because usually in the case of a merger, you have two established groups with two established systems that may be starting to age out on both sides. And it doesn’t make sense to spend the time and the effort to move one group into the other system when that system is probably going to be replaced in a few years anyway. AP: Yep. So ...
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    23 分
  • Who controls your content? AI and content governance

    Who controls your content? AI and content governance
    2026/03/30
    What does it actually mean to govern your content in the age of AI, and who’s really in control? In this episode, Sarah O’Keefe sits down with Patrick Bosek, CEO of Heretto, to unpack why the quality, accuracy, and structure of your content may be the most critical factors in what your users experience on the other side of an AI model. Patrick Bosek: In today’s world, you don’t have 100% control. There are a couple of different places where this needs to be broken up. One is the end user: what they physically get and what control they have versus what control you have. Then, there’s what control you have of how the AI model is going to behave based on your information and your inputs. Whether or that model is public, like a user accessing your documentation through Claude Desktop, or private, like a user accessing your documentation through your app or website, the governance piece comes down to what control you have immediately before the model. And that breaks down into a couple of things: completeness, accuracy, and structure of the content. Related links: AI and content: Avoiding disasterAI and accountabilityStructured content: a backbone for AI successHerettoQuestions for Sarah and Patrick? Register for the Ask Me Anything session on April 8th at 11 am Eastern. LinkedIn: Sarah O’KeefePatrick Bosek Transcript: This is a machine-generated transcript with edits. Introduction with ambient background music Christine Cuellar: From Scriptorium, this is Content Operations, a show that delivers industry-leading insights for global organizations. Bill Swallow: In the end, you have a unified experience so that people aren’t relearning how to engage with your content in every context you produce it. Sarah O’Keefe: Change is perceived as being risky; you have to convince me that making the change is less risky than not making the change. Alan Pringle: And at some point, you are going to have tools, technology, and processes that no longer support your needs, so if you think about that ahead of time, you’re going to be much better off. End of introduction Sarah O’Keefe: Hey everyone, I’m Sarah O’Keefe. I’m here today with Patrick Bosek, who is the CEO of Heretto. Hey Patrick. Patrick Bosek: Hey, Sarah. Long time no chat. SO: That is, I guess for certain values of long time. We decided today that we wanted to talk about AI and governance, except I promptly tried to come up with a synonym for governance because I’m afraid that when I say that particular word, our audience just walks off. So, okay, Patrick, what is governance? PB: Well, so first of all, thanks for having me on, and second of all, I’m excited about this one because based on our little bit of chat before the show, it sounds like we’re actually gonna have some things to argue about this time around. SO: I would never. PB: Well, usually we tend to agree right like I think that we’re generally pretty on the same page about stuff. So I’m excited. I’m pumped. Okay, so governance. I mean, obviously it has a ton of different meanings to different people but in the way that I want to talk about it today, because it was my suggestion. It’s related to the governance of content, specifically in the way of the inputs to AI systems. So you can think about the process of controlling for quality, accuracy, the things that matter in the actual content and information before it gets into the AI system. So it’s kind of the upstream quality, totality, structure, all of that checking and assurance ahead of whatever your experience is going to be downstream, of which one is the most contemporary and most interesting is AI. SO: Okay, so this is making sure that it is not garbage in so as to avoid garbage out. PB: Yeah, I would say that’s a fair statement. SO: Yeah. Okay. And can we use AI to do governance of the content we’re producing? PB: Well, that’s actually a very interesting question. And I think the short answer is somewhat right now. So before I go, okay, before I like fully answer that, I want to put a little disclaimer in here. The stuff with AI is changing so quickly that we should date-stamp this episode. SO: It is March 19th, 2026. And it’s nine-ish Eastern time. PB: Yeah, we are recording this on March 19th, 2026. Now I feel, yeah. Okay, so now that people know when it is that we’re talking about this, I feel a little bit safer in answering. So there are aspects of governance you can do with AI today, for sure. And there’s new capabilities coming online all the time. I actually think, broadly speaking, the thing that’s going to be most challenging about governance is going to be the pieces that can’t be done with AI continuing to not continuing to do them because it becomes like as the human part of the loop becomes smaller and smaller, it becomes so much easier and easier for the human to just click accept because like the AI gets it right, does it, the automation works that kind of thing. And ...
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    40 分
  • Good content = good AI: The fundamentals that never change

    Good content = good AI: The fundamentals that never change
    2026/03/23
    Good content fundamentals have been the foundation of effective product content for decades, and those same principles are exactly what make content AI-ready today. In this episode, Bill Swallow and Alan Pringle explain how attending to your hierarchy of content needs is the key to AI success. Alan Pringle: Right now, AI is not going to fix bad content problems. It is going to regurgitate that bad information, giving your end users information that’s flat out wrong. If your content at the basic source level is wrong, your AI by extension is going to be wrong. And that is the unglossy, unvarnished, hard truth that is still, I don’t think, seeping in like it should across the corporate world. Bill Swallow: It really does come back to the fact that, despite the world changing on a day-to-day basis, the fundamentals have not changed. Related links: A hierarchy of content needsTechnical Writing 101, 3rd editionStructured content: a backbone for AI success LinkedIn: Alan PringleBill Swallow Transcript: 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, I’m Bill Swallow. Alan Pringle: And I’m Alan Pringle. BS: And in this episode, surprise surprise, we’re going to talk about content. AP: Really? Who would have thought? BS: But more specifically, what good content means today. Today, everything is all about AI. There is lots of change in progress with regard to AI tooling and content delivery with AI. But have the needs for content really changed? And I would say that off the bat, if you’re doing content right, you really don’t have to reinvent the wheel to make it AI acceptable. AP: No, in this crazy AI-hyped world we’re in, there’s some very basic foundational things that tend to get overlooked because they’re not sexy, and they’re not special and hot and whatever else. All that kind of marketing garbage that just sets me on complete edge and makes me want to say profane things in podcasts. The bottom line is, there are things that the content world, and especially our little subdomain of it, product content world, has been doing for decades now. And I mean decades. BS: Or should have been doing. AP: Correct. There are basic tenants that have been in place for decades. That if you’re following them, you are starting down the road of success with AI. I think to kind of prove our point, we’re going to step back and look at some of the things that Scriptorium has talked about and written in the past and see how it stacks up. And Bill, you found one. And let’s talk about that blog post that Sarah O’Keefe wrote. What was the date on that again? BS: It was 2014. And that is when we came up with the hierarchy of content needs. And it really wasn’t so much an invention as it was just a regurgitation of what it means to create good content. So we have a pyramid of content needs. At the bottom, we have available. So is content available? Does it exist? Can someone get to it? I think that we’ve mostly solved that problem given the dearth of information we have out on the internet. But as we know, that information is not always useful. So we go up a rung or a layer on that pyramid and see whether or not the content is accurate. And if it’s accurate, if it provides the correct information, that’s fantastic. Then we go up another level and see whether or not the content is actually appropriate. So it can be correct. It can exist. But is it appropriate? Does it meet a reader’s needs? And is it formatted in a way that works for the reader to ingest? Then we go up a step further and see whether or not the content is connected. And this is where we kind of get to the more modern aspect of content. Does it link out to correct additional resources? Is it available to people in a variety of means? And does it engage with the audience? And then finally, at the top of the pyramid, we have intelligent content. Is the content intelligent? And we’re not talking about AI here at all, but we are really talking about is the content fashioned in a way that it can be used intelligently across different media? AP: That it can be manipulated for different purposes. And that is quoting Sarah directly. And I think that is key here, because that is what AI does. It takes information and ...
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    15 分