AI in practice: Content orchestration

Q: How are publishers using AI in content orchestration?

A: Most AI investments have chased more content, faster and cheaper. That is table stakes now, not an advantage. And while publishers race to produce more, they are sitting on decades of reporting locked in PDFs, in systems nobody runs any more, in scanned editions. The ones getting ahead have stopped asking how to make more and started setting their foundations to get more value from what they already own.

Orchestration is what closes that gap. It means making every piece of content reachable and reusable, so any channel, product or system can ask for exactly what it needs and get back something properly formatted and cleared for use. Your CMS looks after content. Orchestration is the set of rails that lets everything else reach it, producing better experiences and stronger audience relationships.

How publishers are using AI in content orchestration:

• Governance inside the workflow: The first wave of adoption consisted of standalone tools operating outside the production system. AI agents now occupy defined roles within the editorial workflow itself, often triggered by a status change rather than invoked by an individual. That shift moves governance from something teams remember to apply to something the platform enforces, with context, usage rights and an audit trail behind every action.
• Content intelligence: One of the more consequential changes is the ability to interrogate an archive in plain language. Interesting use cases we are seeing include: what position has this title taken on a subject over the past twelve months? Which articles on a subject performed the best? These were the tasks that were complex and not feasible under time pressure. They are becoming everyday queries. That is a different kind of gain from efficiency.
• Greater lifetime value from existing content: Structured, rights-cleared content and archives are becoming inventory rather than storage, opening new products and revenue opportunities through AI licensing, syndication, premium platforms and archive subscriptions. Those are only the markets that exist today. A well-structured archive opens endless possibilities.

Q: What is a particularly illustrative use-case?

A: When one of our customers saw the content engagement and commercial results from Apple News Plus, they explored ways to use their archive content to generate new revenue. The content existed. The barrier was access, with no practical route for a non-technical team to identify the right articles and move them to a monetisable channel.

The solution was a custom interface built on an MCP layer over their content. The team searches in natural language, AI surfaces the strongest candidates, and editorial selects and publishes directly.

A working example: For one of their special editions, the system returned 1,619 matches, narrowed these to 120 credible candidates and recommended the strongest ten, showing its reasoning so the team could assess whether to agree. Revenue followed, with no new content commissioned.

Three lessons learnt:

• The content was already there. The constraint was access, not editorial capacity, and access is by far the cheaper problem to solve, as compared to creating new content.
• Non-technical users can operate sophisticated AI applications when the infrastructure genuinely understands the content underneath.
• The revenue came from investment already made. Existing assets finally reached a channel that previously needed production support to enter.

What made this work was not the AI, but the infrastructure sitting underneath it. Any publisher whose content is structured, governed and rich in metadata can build on the same foundation.

Q: What are your three top tips?

1. Treat your content as data. Is it organised and accessible? Can a system reach your content, and does it get back something usable? For most publishers, the honest answer is ‘no’. Decades of reporting sit in formats nothing can read, with no consistent structure and no way to request a component rather than a whole page. Anything built on that will underperform, and the AI will take the blame. Rebuilding an archive was prohibitively expensive until recently. AI has made it viable.

Establish how much of your content is machine-readable and how much is stranded, then test it: can one piece of reporting produce five usable variants without a journalist starting again?

2. Build provenance and rights into the structure, not into a document. Reachable content is not the same as usable content. The second question is whether you can prove what a piece is made of. For any published item, in any format, you should be able to say where each element came from, which systems edited it, who approved and what you are licensed to do with it.

Provenance protects you and also sells for you. Buyers in a licensing negotiation want to know what they are paying for and where it came from, so content whose provenance you cannot prove is content you cannot sell.

3. Make governance non-negotiable. While it is exciting to see teams experiment with vibe-coded tools and efficiency hacks, it is essential that any application going to be used in production is aligned to a governance model and ideally within a platform that can maintain control and scalability.

About us

WoodWing empowers publishing ecosystems by uniting technology with deep industry expertise. For 25+ years, we’ve helped teams create, manage, and deliver content across print and digital channels with greater efficiency and consistency. Our portfolio spans multi-channel production, digital assets, quality, knowledge, and information management. Founded in 2000, we operate globally from our headquarters in the Netherlands.

Web: www.woodwing.com