AI breaks without structured context. Most companies aren't ready.

Here's something most AI vendors won't tell you: the bottleneck isn't the model. It's your content.

I've spent the last year talking to enterprise teams pushing hard on AI adoption. The pattern is consistent. They build agents, workflows and copilots. They hit a wall. Not because the AI isn't capable, but because their content is fragmented across systems, poorly structured for machines to reason over, and impossible to govern at the speed AI operates.

For a decade, we optimized content systems for humans: better authoring, faster publishing, more channels. AI was a feature you added on top. That's inverting. AI is becoming the workflow itself. And the systems built for human-speed content operations are structurally incompatible with how AI actually works.

We’re entering a new phase where content operations, not content presentation, determine who wins. And those who can move fast and scale safely in an AI-native world will be the winners.

AI breaks without structure

Over the last 18 months, enterprises have moved aggressively from curiosity to experimentation with AI. I’m seeing customers creating, reusing, and distributing content globally with AI. Work that used to take weeks now takes minutes.

Recently a large global e-commerce brand (can’t name it) took all product descriptions from their PIM and turned them into user facing, branded descriptions, and added blogs for each new product, all with a custom workflow calling LLMs and user facing UI to control the output.

Teams everywhere are building agentic workflows, internal chatbots, content generators, and user facing agents to support all parts of their business.

But almost all of these efforts hit the same wall. AI systems are only as good as the content they can access. The reality is that most Enterprise content libraries are:

• Fragmented across systems
• Poorly structured for LLMs to understand, and centered around humans to read
• Hard to govern in real time

LLMs can generate text. They cannot magically infer context, respect boundaries, or follow brand rules from PDFs, static pages, or loosely connected tools.

As organizations push toward agentic workflows and autonomous agents, systems that can create, adapt, and act without constant human intervention, the lack of a reliable content backbone becomes the limiting factor.

This is the real inflection point. The question is no longer “how do we use AI?” It’s “what does AI need to work reliably at scale without breaking?”

Content isn’t output anymore. It’s infrastructure.

What’s becoming clear is that content is no longer just an output of humans or LLMs. It is infrastructure. It’s the context for AI.

AI-powered workflows depend on content that is:

• Structured enough to be understood programmatically
• Contextual enough to be relevant at runtime
• Governed enough to be trusted and auditable by humans or AI
• Real-time enough to reflect the current state of the business

This applies whether you are building internal knowledge agents, automating product content across markets, personalizing experiences across channels, or enabling agents to act with confidence.

The industry is shifting from isolated AI-assisted creation toward autonomous, agentic systems operating with context. Yet there is no standard backend for enterprise content in this world.

Traditional CMS platforms are rapidly bolting on AI features, often as copilots or predefined agents, or tasks executors, but they were not designed to power dynamic, AI-native workflows across the entire content lifecycle for companies. They fail to scale.

Vector databases and retrieval systems solve only a narrow slice of the problem. They don’t model content. They don’t manage workflows. They don’t provide governance, collaboration, or omnichannel publishing. And critically, they lack the editorial and operational interfaces real teams need to work effectively.

The result is growing complexity, brittle stacks, and teams hacking together solutions, spending time on solving problems piecemeal that are not core to their business. So building this on your own, is, let’s say, not the path forward.

The rise of AI content operations

What’s silently emerging through leading companies, without most of us realizing it, is a new world of AI content operations at scale. The companies providing the right context to AI are pulling ahead. The rest are trying, failing, and trying again. And to be pragmatic, this is not about replacing humans with machines now. It’s about re-architecting how content flows through your organization so that humans can be dramatically more efficient.

In this new world:

• Content is treated as structured data, not static siloed artifacts
• Agents have enough context to be precise
• Governance is enforced by design, not by cleanup later
• Automation is triggered by events, not manual handoffs

We see this every day here at Sanity. Customers are exploring multi-agent systems for editorial workflows, product catalogs, support knowledge bases, and localization. But without a shared content foundation, these efforts struggle with version control, consistency, compliance, and collaboration (outside of a git repo).

This is why the AI content-backend matters more than ever.

Four shifts leaders need to make now, before is too late

The transition to AI content operations is already underway, whether you acknowledge it or not. The question is how deliberately you can act, and how fast you should do it.

To become more agile and go to market faster organizations need to:

1. Audit your content for machine readability. Can an AI agent query your product catalog right now and get structured, current, governed data? If the answer involves "we'd need to export it first," you have a problem.
2. Stop treating AI as a feature. Most teams are bolting copilots onto existing workflows. The winners are redesigning workflows assuming AI handles execution and humans handle judgment.
3. Consolidate before you automate. Every fragmented system is a governance risk when AI is in the loop. The cost of maintaining five content backends goes up exponentially when agents are acting on them.
4. Build for runtime, not batch. If your content can't update and propagate in real-time, your AI systems will operate on stale context. In agentic workflows, stale context means wrong actions.

The industry is rebuilding its foundations. Innovators are leading, and acting now. Winners are and will be those who recognize this shift early will define how AI actually shows up in products, content operations, and customer experiences.

Ready to see what this looks like in practice?

• See how loveholidays cut translation costs by 97% and reduced launch cycles from 3 months to 3 days →
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• Or talk to our team about rebuilding your content platform for AI →