Five stages of content debt: building a solid foundation for AI and automation

Everyone wants AI to speed up content operations. Fair. But there is a hard blocker most teams skip past: content debt. In a recent conversation with Sarah O’Keefe, founder and CEO of Scriptorium, we unpacked why shortcuts, such as copying old content forward without verifying accuracy, outdated source material, and missing structure, like untagged, unclassified content, keep AI from delivering real business value. The punchline is simple. Automation works only when your inputs are consistent, accurate, and machine-readable. If they are not, AI turns small cracks into big risks.

This article defines content debt, frames the AI “assembly line” opportunity, and lays out practical steps to fix the foundation. If you own operations, transformation, or content at scale, this is about risk reduction and ROI. If your role involves designing how content is structured and governed, it is about structure, semantics, and governance. The goal is the same. Pay down content debt so AI can compound your results, not your mistakes.

We often hear about a future where AI handles content creation like an assembly line. The vision is enticing; eventually, well-functioning AI systems could automate all the things in our content workflow. For example, imagine being able to automatically: • Extract instructional text from product design specs – AI reads the engineering design documents and pulls out the steps or user guidelines without a human writer starting from scratch.

• Correct and copy-edit content – grammar, mechanics, and house style are automatically enforced, so no more embarrassing typos or off-brand tone.
• Generate data sheets from product databases – the latest product attributes in your database flow directly into formatted spec sheets or reference docs.
• Translate on the fly with high accuracy – content is instantly available in every required language, with AI providing near-human quality translation.
• Produce on-demand, personalized content – given a user’s context or profile, AI assembles just the right information in the right format, tailored to that person’s needs.

The payoff: safer, smarter, scalable AI

For organizations that invest in structured, reliable content, the business impact is immediate:

• Lower costs. Reuse reduces authoring effort and cuts translation spend.
• Faster speed to market. Streamlined content creation, reviews, approvals and localization shortens time-to-market and allows for easier global expansion.
• Quality and brand consistency. Standards, terminology, and templates protect brand and compliance.
• Operational resilience. A predictable content supply chain that scales with the business, reduces risk and aligns with ever-changing global regulatory requirements.

The promise of AI and automation becomes real only when the underlying content is standardized, accurate, and ready to flow.

Content debt (akin to technical debt) is the future rework you’ll face because shortcuts were taken, workarounds became habits, or content quality simply slipped over time. It doesn’t arrive all at once. It builds quietly in the background through the outdated PDF no one wants to edit, the spreadsheet with five competing versions, or the product description that never got updated after engineering changed direction.

These issues feel harmless at the start. But every missed update, every inconsistent unit of measure, every “temporary” fix adds weight to the system. Eventually, even small changes take days. And when AI tools try to help, they end up pulling from outdated or conflicting sources.

That’s content debt: the hidden backlog created by years of “we’ll fix it later.”

Content debt rarely announces itself. Teams rarely realize they’re carrying content debt until it slows everything down. It hides in old folders, SharePoint sites, legacy drives, and forgotten templates, gradually making work harder, slower, and riskier.

Across different organizations, I’ve seen the same pattern repeat. Teams don’t hit content debt all at once. They pass through a few predictable stages on the way before the pain drives them to fix the underlying structure. If you’ve experienced a messy migration, an audit surprise, or a frantic last-minute release, these will sound familiar:

1. Denial: “Our content is fine.”
2. Frustration: Slow reviews, duplication, rising translation costs.
3. Blame: The tools, the process, the people—everything becomes the culprit.
4. Acceptance: Teams recognize the problem and begin audits and governance work. 
5. Action: Structured content, reuse, metadata, and platform investment take center stage.

Once you start looking upstream, the sources of content debt become very clear. They usually begin long before a writer touches the content. A few patterns come up again and again:

• “As-Designed” vs “As-Built”: Often, the product is built differently than originally designed, but the documentation isn’t updated to match. Engineering might make last-minute changes that never make it into the design spec or user manuals. This mismatch means any content derived from the original spec is immediately wrong.
• Out-of-date CAD and specs: We’ve heard sad CAD stories – for example, CAD models or technical drawings that never get updated after the first version. The tech writers export illustrations or dimensions from these files, only to learn later that engineers changed a part and didn’t update the CAD. Engineers might assume someone else will fix the docs (“I’m too busy to update that”). So the burden falls on content creators to manually catch up, if they even know a change happened.
• Inaccurate product documents: Product design documents are infamous for being incomplete or obsolete as soon as they’re written. Maybe there was a spec sheet from two years ago that no one revised during development. If AI tries to pull info from that spec, it’s pulling from fiction, not reality.
• Spreadsheet chaos: Many companies run critical information on spreadsheets passed around via email. Think of pricing sheets, parts lists, configuration tables, test results – all in Excel files on someone’s hard drive. There’s no single source of truth. Different versions float around. This is basically unstructured data. An AI can’t confidently extract “the truth” from a tangle of spreadsheets and random emails.

In an assembly line metaphor, these issues are like having warped parts, missing screws, and wrong blueprints feeding into the factory – the end-product will be defective.

Then AI shows up and exposes it all. The uncomfortable truth is that many organizations dreaming of AI automation are building on a shaky foundation of content debt. The inputs we feed our large language models (LLM) on are often of low quality. Product designs might be inaccurate or outdated. Terminology is tribal knowledge rather than documented. Databases have errors. Content lacks structure or metadata. It’s the classic “garbage in, garbage out” problem.

While that might work for certain organizations, in regulated industries like medical device manufacturing, “garbage in” doesn’t just mean inefficiency it can lead to patient harm, non-compliance or compliance failures, reputational damage, or lost revenue.

The bottom line: AI needs good source material, and right now we often don’t have it. This is the essence of content debt – years of neglecting content quality and infrastructure are now a barrier to fancy AI solutions. To move forward safely and confidently, we must pay off that debt by fixing the foundation.

Before you automate, standardize. AI thrives on consistent patterns and explicit meaning. Here is a practical checklist that merges quality, structure, and semantics.

Content quality

• Accurate and current. Facts reflect the product as shipped, not as imagined.
• Consistent voice and units. One tone. One terminology set. One unit system.
• Complete and scoped. Each topic answers a defined need. No filler, no gaps.

Structured and semantic

• Componentized. Content is broken into the smallest reasonable units. Think topics, not blobs.
• Object oriented. Clear content types with known behaviors. Procedure, concept, reference, warning.
• Algorithmically predictable. Repeated patterns, stable templates, and ordered steps.
• Semantic tags and metadata. Labels that describe intent and context. Audience, product, version, region, lifecycle state.
• Controlled terminology. Approved terms documented and enforced across languages.

Why does it matter?

Because AI interprets patterns as meaning. If your content deviates from a pattern unpredictably, an AI might interpret that inconsistency as something significant (when it’s just sloppy writing). For example, if most of your product descriptions list “Weight: x kg” but one lists weight in a different format or in a different place, an AI could get confused or assume a difference in meaning.

• Predictable patterns help AI extract, assemble, and validate with confidence.
• Metadata narrows the search space, so AI selects the right component for the right context.
• Controlled terms reduce ambiguity in authoring, translation, and retrieval.
• Smaller components unlock reuse and lower localization costs.

Consistency is king. Large language models and similar AI systems learn from examples. They notice the frequencies and structures in the input. If half your content says: “Press the ‘On’ button to start” and the other half says: “Turn on the device”, an AI might not realize that those are the same action; it might think that they are different or miss one when generating instructions.

On the flip side, if all your content follows the same pattern, the AI can more reliably mimic or extract from it.

Content that is broken into its smallest reasonable pieces, which are explicitly organized and classified to be understandable by computers and humans.

Structured content is:

• Object-oriented
• Componentized
• Algorithmically predictable

It is a container that describes the intent of what the object or entity is, not what it looks like. Structured content is semantic. It contains meaning.

Most content and data stored today are unstructured.

For many teams, there is a proven path here. XML-based schemas like DITA separates meaning from presentation, codifies reusable structures, and scales across products and languages. You do not need to be dogmatic. You do need to be deliberate.

FIX your content and your data before you throw AI at the problem. It’s time to pay off some content debt. You do not have to fix everything at once. Target the few inputs that power most outputs. Then iterate.

Step 1: Map your content supply chain

• Where does information originate? Engineering specs, PLM, support, field notes.
• Who transforms it, and how? Writers, editors, localization, legal.
• Where does it land? Docs portal, agent assist, knowledge base, in product help.
• dentify bottlenecks, format shifts, and ownership gaps. Draw the flow. Name the risks.

Step 2: Stabilize product data

• Move from scattered spreadsheets to a managed source of truth.
• Normalize attributes, units, and naming.
• Add governance. Who creates, who approves, who changes.
• Tie IDs to components so content can reference data by key, not by copy paste.

Step 3: Clean the design layer

• Align as designed and as built. Update CAD and drawings when the product changes.
• Trigger downstream notifications automatically. Writers should not learn about changes by accident.
• Maintain a versioned reference set. No more hunting for “finalfinal3”.

Step 4: Formalize terminology

• Build a bilingual or multilingual term base with approved and forbidden terms.
• Put it behind an API so editors, translators, and AI can call it.
• Make ownership explicit. Someone owns term lifecycle and conflict resolution.

Step 5: Componentize and tag

• Break monoliths into topics. Procedures, concepts, references, troubleshooting, safety.
• Adopt templates. Lock the structure, not the creativity.
• Apply metadata with discipline. Audience, product, version, region, lifecycle.
• Use conditional attributes for variants instead of duplicating content.

Step 6: Establish governance

• Define roles. Content owners, reviewers, approvers, stewards.
• Set measurable standards. Readability, terminology match, metadata completeness, reuse ratio, translation leverage.
• Implement reviews at the right checkpoints. Early and light beats late and heavy.
• Track debt. Maintain a backlog of content fixes with priority and business impact.

Step 7: Align to AI use cases

• Be explicit about what you want AI to do. Answer support questions, generate data sheets, draft procedures, summarize change logs.
• Back map the content requirements for each use case.
• Pilot with a well structured subset. Measure accuracy, time saved, and escalation rate.
• Scale only when the foundation holds.

This is the work. It is operational, not glamorous. This is what makes automation reliable, traceable, and auditable; especially in environments where every change must be defensible.

Content debt festers in the gaps between teams. Fix that first.

• Create cross functional ownership. Engineering, product, tech comm, localization, support, legal. One table.
• Assign clear RACI. Who is responsible, accountable, consulted, informed for each content type and data source.
• Fund maintenance. Budget and KPI for keep the lights on work. If you do not fund it, you will not get it.
• Instrument the system. Dashboards for accuracy, reuse, translation leverage, and time to publish.
• Close the loop. Feedback from the field should update source content and data, not just troubleshooting tips.

Governance is not bureaucracy. It is how you make quality predictable.

The promise of AI in content operations is real – there will be incredible efficiencies and capabilities, from instant translation to personalized content experiences. But to get there, we must confront our content debt. Like an assembly line that only runs smoothly with standardized parts, our AI content “factory” will only deliver if we feed it quality ingredients. By tackling content debt – cleaning up your source content and data – you’re not only enabling future AI projects, you’re also improving current content for your users. It’s a win-win. Your human readers get better, more consistent information, and your future AI tools get a solid knowledge base to draw from.

• Automating the mess. Speeding up creation of inaccurate content is not a win.
• Skipping terminology. You cannot be consistent across languages without a term base.
• Ignoring metadata. If the system cannot tell who and what a component is for, neither can AI.
• Treating DITA as a silver bullet. DITA gives you structure. You still need ownership, process, and discipline.
• Underfunding maintenance. Content debt returns the minute you stop paying principal.

Stay honest. Measure what matters. Fix the root causes, not the symptoms. This is exactly where a structured platform like Tridion Docs makes the difference.

If you want a partner in this journey, Tridion Docs gives you the structured backbone to reduce debt and operationalize automation across teams and languages. If your organization manages complex, high stakes information at scale, a structured content management platform matters. Tridion Docs is a DITA based CCMS that helps you make this shift without losing your sanity.

What it gives you

• Component content management. Author once, reuse everywhere. Reduce duplication and translation volume.
• Structured authoring in DITA. Enforce templates and content types so patterns are consistent.
• Metadata and taxonomies. Label content for audience, product, version, region, and lifecycle. Improve findability and assembly.
• Terminology integration. Align authoring and translation with a governed term base across languages.
• Translation management. Connect to localization workflows and translation memory to accelerate global delivery.
• Review and workflow. Route content for the right approvals with an auditable trail.
• Publishing and delivery. Deliver multichannel outputs and enable personalized, on demand experiences.

Business outcomes

• Lower costs. Reuse and consistency reduce authoring effort and translation spend.
• Faster updates. Structured change flows through the system. No manual chase.
• Risk reduction. Governance, auditability, and traceability reduce compliance exposure.
• AI readiness. Clean, componentized, and labeled content is AI fuel. You get higher accuracy and less rework.

If you are serious about AI, get serious about the content backbone that feeds it.