10 reality checks for AI-enabled content ops
Hoping AI will solve all your content problems? Here are 10 reality checks for successful AI-enabled content operations.
This post contains contributions by Sarah O’Keefe, Alan Pringle, and Bill Swallow.
1. Garbage in, garbage out
If you push garbage information into an LLM, you’ll get garbage results, just faster and at scale. Good AI output requires accurate input.
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.
2. People ask AI instead of reading your docs
Users access your content via AI chatbots, not your carefully crafted content experience. To keep up, you must optimize your content for AI ingestion.
3. You no longer control content delivery
AI lets end users reformat, simplify, and translate content. It’s the ultimate personalization engine. Make sure that content is accurate, complete, and structured to survive the transformation.
Sarah O’Keefe: Before, the person controlling the page presentation was the person who designed the publishing pipelines. But the publishing pipelines were designed on the back end by the authoring people. Now all of a sudden, we have no control over that end product. Just because the author thought it should be a PDF or an HTML page, the consumer can turn around and say, “Give it to me in a podcast, make me a video, show it to me in French,” and the LLMs will do it.
Alan Pringle: The publishing pipeline got moved over the fence to the content consumer side. They get to do what they want. That’s where things are headed.
Check in on AI: The true measure of success for AI initiatives
4. Messy workflows = messy AI output
AI will expose all your content debt. Clean up the workflows before you implement the technology. Eliminate duplicate content, follow style and terminology rules, and ensure accuracy.
5. Scalability requires structure
AI needs consistent content to find, interpret, reuse, and deliver information. Structured content is a competitive advantage.
Alan Pringle: Structured content is one framework that can sustain a useful, trustworthy AI experience. Without strong content back-end support, your AI front end is doomed to spout bad information that angers your customers and prospects, slows down your staff, and causes reputational harm to your organization—or worse.
6. High risks = human intervention
For high-stakes content (safety, medical, financial), you need the strong guardrail of human oversight.
7. As AI improves, errors are harder to find
When the AI error rates are low, it’s tempting not to verify content, which means errors creep in. Human review isn’t temporary scaffolding—it’s a permanent part of responsible AI governance.
Sarah O’Keefe: If AI is accurate half the time, then my hackles are up. I know it’s gonna be wrong. It’s wrong all the time. If it’s accurate 80% of the time, I just assume it’s accurate all the time. So the better these models get, the worse the errors are because we don’t expect them.
Check in on AI: The true measure of success for AI initiatives
8. Localization is the debt collector for content
Inconsistent, unstructured content is difficult to translate and doesn’t scale. If you want global content delivery across, structure your content before the bill comes due.
Sarah O’Keefe: Automated formatting reduces the overall effort of creating a document. For organizations that produce content in multiple languages, the cost savings are multiplied. As of 2017, the need for efficient localization is one of the most common business justifications for moving into XML.
9. “The AI did it” is not acceptable
AI content changes must be traceable, adjustable, and approved before going live. You need audit trails for accountability.
Sarah O’Keefe: As an author, you are accountable for your work. If you produce that content for an employer, the employer is accountable (and liable) for your content. If the spell-checker doesn’t catch a spelling error, that doesn’t make the error OK. Using AI doesn’t excuse you from getting the legal citation right in a brief, or ensuring that your image doesn’t have six-fingered human hands, or verifying that the machine translation doesn’t have howlers. Until we resolve the tension between AI-generated inaccuracies and author accountability, we’re going to have issues.
10. Now is the time
We need to lay the groundwork now for the AI world. Restructure content ops, establish governance and accountability, and develop AI workflows for competitive advantage. As AI matures, you’ll be ready.
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