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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
AI and content: Avoiding disaster
As a purveyor of high-stakes technical content, I am watching the rise of AI with alarm. Our interest in automation and new technologies is on a collision course with our mandate to deliver timely, accurate information. I am not the only one who is concerned; many people are writing on this topic. (Here’s a recent post from Michael Iantosca.)
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
Detecting solutions for structured content: Lessons from LavaCon
At LavaCon 2025, we investigated the impossible dream of customer content, uncovered the potential of structured learning content, and shared solutions to make sure your content doesn’t sleep with the fishes—or the whale sharks.
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
Publishing DITA content to PDF
The second most common question we get about DITA-based systems is “How do we publish nice-looking PDFs?” (First, by far, is “How do we migrate our content into DITA?”)
