WritersUA: DITA pilot techniques
Mark Wallis of IBM ISS on how to run a successful DITA pilot. Some great information in this presentation on how to reduce risks.
How to survive the four horsemen of the AIpocalypse (webinar)
Confront the chaos that generative AI can unleash on your content and discover how to regain control. In this practical session, Torsten Machert (Congree Language Technologies) and Sarah O’Keefe (Scriptorium Publishing) revealed the four biggest threats that undermine quality when you rely on GenAI for content creation.
Sarah O’Keefe: It is way, way cheaper to build out the maturity of your content, to do the terminology work, to do the structure work, to do the metadata work, label everything, give it categories, give it classifications ahead of time than it is to try and remediate the content after the fact, after it’s been processed, after it’s been ingested into the AI and then spit back out. My fear right now is that we’re seeing a lot of, “Ingest everything, spit it back out, then consider how to fix it.”
Conversational AI: The cost of ignoring structured content (webinar)
Conversational AI is everywhere, but reliable AI responses depend on reliable content. So, how do you ensure your content is reliable? In this webinar, guest Rahel Bailie, Content Solutions Strategist at Content Seriously, and host Sarah O’Keefe, Founder & CEO of Scriptorium, examined how the intersection of structured content and conversational AI has evolved. They also share practical next steps that organizations can take to create a successful AI content strategy.
Rahel Bailie: How do you know your content is ready for AI? The level 1 test is, “Is the AI agent working well?” If it’s working well, then you go to, “Why isn’t it getting the right answer?” Then, you go to the content. The content can be good or bad and can be measured in a couple of ways. Is the source content marked up well? Does it have the right semantics on it? Does it have the right metadata? Do you have a knowledge graph in the background that’s making these relationships, so that the AI can pull out the right content?
UI for AI: Responsible content delivery (webinar)
With AI, users are taking control over content delivery through summarization, personalization, translation, and more. But what are the risks? In this webinar, Sarah O’Keefe, CEO of Scriptorium, and Fabrice Lacroix, CEO of Fluid Topics, explore strategies and share examples of UI for AI that empower users while protecting them—and your organization—from misinterpretation, incomplete information, and compliance breaches.
As somebody who works in structured content with metadata, taxonomy, and all those other fun things, we’re telling people, you have to do the work. You have to do the work upfront because once that ingestion step happens and the AI is ingesting not structured, not consistent, not governed, not accurate, not up-to-date content, then what chance does the AI have? The AI is not going to make your content magically more accurate. It’s not magic. I mean, it can do some magic looking things, but it is not magic. Your entropy always wins. Your content will always sort of degenerate, right? So you start for your best possible, and it goes down from there. So what’s the best possible thing that you can get into your database?
— Sarah O’Keefe
From black box to business tool: Making AI transparent and accountable
As AI adoption accelerates, accountability and transparency issues are accumulating quickly. What should organizations be looking for, and what tools keep AI transparent? In this episode, Sarah O’Keefe sits down with Nathan Gilmour, the Chief Technical Officer of Writemore AI, to discuss a new approach to AI and accountability.
Sarah O’Keefe: Okay. I’m not going to ask you why this is the only AI tool I’ve heard about that has this type of audit trail, because it seems like a fairly important thing to do.
Nathan Gilmour: It is very important because there are information security policies. AI is this brand-new, shiny, incredibly powerful tool. But in the grand scheme of things, these large language models, the OpenAIs, the Claudes, the Geminis, they’re largely black boxes. We want to bring clarity to these black boxes and make them transparent, because organizations do want to implement AI tools to offer efficiencies or optimizations within their organizations. However, information security policies may not allow it.
Why Cheap Content Is Expensive and How to Fix It, featuring Dawn Stevens
Will cheap content cost your organization more in the long run? In this webinar, host Sarah O’Keefe and guest Dawn Stevens share how poor workflows, inaccurate source data, and the commoditization race can undermine both product quality and brand trust. Sarah and Dawn also discuss why strategic staffing and mature content ops create the foundation your AI initiatives need to deliver reliable content at scale.
Sarah O’Keefe: I write content that’s great for today. Tomorrow, a new development occurs, and my content is now wrong. We’re down the road of “entropy always wins.” We’re heading towards chaos, and if we don’t care for the content, it’ll fall apart. So what does it look like to have a well-functioning organization with an appropriate balance of automation, AI, and staffing?
Dawn Stevens: I think that goes back to the age-old question of, “What are the skills that we really think are valuable?” We have to see technical documentation as part of the product, not just supporting the product. That means that we, as writers, are involved in all of the design. As we design the documentation, we’re helping design the UX.
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.
LearningDITA: replatforming structured learning content
For nine years, the Scriptorium site LearningDITA.com served more than 16,000 students seeking knowledge about the Darwin Information Typing Architecture (DITA) XML standard. A critical system failure forced Scriptorium to rebuild the site, so we focused our consulting expertise on ourselves to address a replatforming challenge for structured learning content.
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
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
