Content debt is now an engineering problem

A Storyblok survey of enterprises across the UK, US, Germany, Australia, and the Netherlands found that 69% believe content strategies have become more of a technical challenge than a creative one, with content debt now costing 6% of revenue. CEO Dominik Angerer said AI prioritizes consistency over quality, and outdated content risks misleading chatbots and missing security patches.

You have 1 article left to read this month before you need to register /register a free LeadDev.com account. Estimated reading time: 6 minutes Key takeaways: AI rewards consistency, not quality . Content debt now costs 6% of revenue.- Content sits fragmented across departments nobody’s reconciling . - Nobody owns this. Agent sprawl is making it worse . As AI https://leaddev.com/ai/best-ai-coding-assistants pulls content from anywhere and everywhere, and when success with AI relies on unified, accurate data, organizations struggle to decide who is in charge of that data. Recent research out of https://www.storyblok.com/lp/enterprise-developer-shift headless content management system Storyblok found that 69% of enterprises across the UK, the US, Germany, Australia, and the Netherlands believe their content strategies have become more of a technical challenge than a creative one. That changes the questions the modern enterprise asks. Is marketing still the steward of content? Does AI force anyone who handles data https://leaddev.com/technical-direction/whatever-happened-big-data to become a technical expert https://leaddev.com/career-development/how-to-stay-technical-as-an-engineering-manager ? What’s more, if anyone could become a developer, does every role need the same guardrails to safely move faster? Should citizen developers https://leaddev.com/technical-direction/what-to-do-when-everyone-thinks-theyre-a-developer be onboarded to the internal developer platform? Or is it now IT’s job to guardrail data? What would that make the roles of legal, compliance, and privacy departments? AI shines a glaring light on organizational content debt and demands that engineering managers build another bridge across the age-old chasm between business and technology. Your inbox, upgraded. Receive weekly engineering insights to level up your leadership approach. AI prioritizes outdated info If machines are becoming the primary consumer of data, that changes a lot about how data is consumed by humans and machines. As large language models LLMs https://leaddev.com/leadership/llms-an-operators-view and chatbots rapidly replace googling, there’s a sudden demand for answer engine optimization AEO . Traditional search engine optimization SEO – whether for external or internal information – prioritizes recency. It also favors if you update older content. As long as it doesn’t slow down your website, Google and Bing aren’t fussed with your older content, even rewarding you with better search rankings if you posted regularly. “LLMs are statistical models. That means that everything that is consistent and is repeatable multiple times on different platforms and also on the same platform gets ranked at a higher pace and therefore the probability of returning an answer is higher,” explains Dominik Angerer https://www.linkedin.com/in/dominikangerer/ , CEO and cofounder at Storyblok. “If you give AI mixed signals, if you are not consistent, or if you created content 10 years ago, nine, five, two years ago, and just yesterday, I can guarantee, even if you write about the same topic, it’s going to be different,” as your internal and external messaging evolves. AI doesn’t necessarily want to return the best content, but rather what is most likely to be accepted, which is often the information that appears the most. When all content is given the same importance it becomes skeletons in an enterprise’s closet full of outdated and obsolete data, Angerer says. Even if your website is up-to-date, only 8% of Google users will click on a link found within the AI overview https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/ , so few will find it. The risk of this outdated content ranges from old pricing promised by your customer support chatbot to developers missing key security patches from newer documentation versions. According to the same Storyblok research, enterprise executives estimate that the cost of this “content debt” has grown to nearly 6% of annual revenue. More like this It’s tech’s problem too At most enterprises, messaging sits within communications or marketing. In the face of AI https://leaddev.com/ai/your-ai-coding-tools-buying-checklist-for-2026 , content debt becomes a company-wide problem to tackle. Across all companies, Storyblok has found that, no matter the industry, it’ll sit across two to three content management systems, owned by different departments. “All these different content silos have an impact,” Angerer says. Add to this, LLMs aren’t just training on your website but across backlinks, references, Reddit, and more. Older data is less likely to follow semantic data and other data structure best practices, leaving it challenging to combine into the large-scale data graphs, which are the backbone of not only AEO readability but of unlocking cross-organizational AI return on investment. Fragmented internal data leads to generalist AI that doesn’t deliver the specialization that becomes your unique data proposition. Unstructured data also results in poor metadata that can’t prove regulatory adherence and risks customer support chatbot hallucinations. “The developers themselves need to find out first if the structure that they have prepared is actually readable by AI, which means that not just clients are rendered, but servers are rendered.” This, Angerer explains, is how AI actually retrieves the data it consumes. He offers an easy pulse check for any employee: feed a URL to Claude https://leaddev.com/ai/why-microsoft-engineers-are-using-claude-code , asking it to load it and read out what it finds. Sometimes it’ll result in “just a blank page for many of the AIs out there — it’s actually quite scary.” In order to solve this, Angerer advocates for a combined effort, where content marketers and editors touch up old articles, while engineers clean up the structure and set up redirects. Don’t just delete all your old data, he begs. If a page has good traffic or backlinks, intentionally clean it up with AI in mind. Going forward, he believes the CMO is evolving to become more of a CIO, which is why he argues that the content czar should be a technical role within the marketing department. While AEO is overshadowing SEO, Google’s established SEO and accessibility best practices still reign because AI still reads similarly to humans. This means proper H1, H2, and H3 headlines, ordered lists, and alternative text, all backed by well-structured HTML, is as important as ever. All structured data, even with tables not visible to the human eye, makes it easier for machines to understand the purpose of your site. Not surprising since Google has been backed by LLMs for over a decade now. It’s not just about what shows up in AI results either. Just as pressing, especially for companies with customers in the European Union EU or that are in highly regulated industries like banking, is that they have data auditability for compliance reasoning. This demands, again, HTML, as well as the JSON-LD Schema Markup generator that allows easily readable and translatable schema data. Of course, AI is part of the solution for this, from flagging old content, to suggesting updates, to agents performing a series of redirects. However, that’ll only work if it is working from a source of structured knowledge. Berlin • November 9 & 10, 2026 Close the gap between what leadership expects and what’s actually possible at LeadDev Berlin . Who governs content now? Is this a guardrails or gate situation? If data is so important to security, privacy, compliance, and reputation, should we return to old-school command-and-control platforms? Or should usually overworked platform engineering https://leaddev.com/technical-direction/crowdsourcing-platform-engineering teams be inviting their marketing colleagues to onboard to the internal developer platform? “Platform teams can definitely act as enablers for content clean-up and structuring. We’ve seen organizations build compliance workflows that flag aging internal and external product and API documentation for verification or updates. We’ve also seen tooling created to generate structured data for AI consumption, such as LD-JSON, from existing content,” says Daniel Bryant, head of product marketing at Syntasso, emphasizing that the platform team should act as enabling advisors, not be in charge https://www.linkedin.com/safety/go?url=https%3A%2F%2Fwww.syntasso.io%2Fai-platform-engineering&trk=flagship-messaging-web&messageThreadUrn=urn%3Ali%3AmessagingThread%3A2-YTUwNTFmODUtYmRjOC01YWVlLTliNjItZGQ2YTY3OGM1YjEzXzAwMA%3D%3D . “However, the responsibility for content correctness typically has to be pushed to the place with the most experience, knowledge, and domain expertise. This makes centralizing ownership very challenging.” This challenge then extends to agents https://leaddev.com/technical-direction/how-to-prepare-for-ai-agents themselves, because agentic sprawl is rapidly becoming a reality as companies see ten different teams building and releasing ten different agents to ostensibly achieve the same thing. By breaking down silos and at least facilitating cross-organizational data visibility, you can cut cost and increase reusability. This data visibility also may be the most effective way to apply the right guardrails for the right data use cases. “ AI governance https://leaddev.com/ai/ai-governance-is-now-an-engineering-problem is very dependent on the use case. A system helping craft outreach or client touchpoints carries a very different level of risk than a system making recommendations,” says Ryan Deeds, head of AI and enablement at Alkeme Insurance. “The level of oversight, training, and verification should reflect the level of decision-making you’re asking the technology to perform.” This not only requires engineering leadership https://leaddev.com/the-engineering-leadership-report-2026/ to talk to marketing leadership. The cross-organizational risk of AI must be part of a cross-functional conversation across the C-suite. According to Storyblok’s research, more than a third of an enterprise’s content budget is spent addressing content debt, with teams spending more than 100 hours every week auditing, correcting, updating, consolidating, archiving, and retiring existing digital content. In the age of AI, that makes content debt everyone’s problem.