The great document unbundling: When enterprise content becomes your AI’s DNA
6 mins
Enterprise content is having its "Spotify moment." When streaming services broke apart albums and turned songs into building blocks for endless playlists, they changed how we think about music. Now AI is driving the same kind of transformation in enterprise content. The era of monolithic documents, measured in pages and stored in static files, is giving way to something far more dynamic: content as data.
The implications run deep: we're not just changing how we store content, we're transforming static knowledge into the building blocks of AI DNA – the genetic code that will power the next generation of intelligent enterprises.
In late 2020, Forrester published 'The Future of Documents,' predicting a five-to-seven year horizon before organizations would fundamentally rethink how they author and distribute content. As we enter into that 2025-2027 milestone, this transformation is becoming abundantly clear, driven by rapid advances in AI and growing recognition that traditional document paradigms are hampering digital innovation. What seemed like a bold prediction then has become an urgent imperative now, as organizations face a stark reality about their content readiness.
The AI readiness gap
In the race to implement AI across the enterprise, organizations are discovering an uncomfortable truth: their content isn't ready. While companies are investing heavily in AI capabilities, the reality of fragmented documentation, siloed knowledge bases, and disconnected systems is limiting their ability to fully leverage these investments.
For those managing technical content, knowledge bases, or documentation, the pressure is mounting. The C-suite's enthusiasm for AI has sparked demands to make content "AI-ready," yet the reality is sobering. According to McKinsey, while 70% of organizations have implemented AI in some capacity, only 11% have achieved successful deployment at scale. This stark gap between adoption and success points to a critical missing piece in the AI transformation puzzle.
The numbers paint an even clearer picture. According to the 2024 MuleSoft Connectivity Benchmark Report, the average enterprise today manages over 1,000 different applications, with 71% of these systems disconnected from each other. Meanwhile, 90% of organizational data remains unstructured, locked away in various formats and repositories. For companies trying to implement AI solutions, this fragmentation creates a major barrier - you can't train AI on content you can't access.
Breaking free from the document paradigm
So what does it mean to treat content as data? According to Forrester's analysis, it requires fundamentally rethinking how we create, manage, and deliver content. It's not just about better file management or more sophisticated search – it's about breaking documents down into structured, reusable components that can be dynamically assembled, updated in real-time, and consumed equally well by both machines and humans.
Making this shift requires several key capabilities working in tandem. Each plays a crucial role in transforming static documents into dynamic, AI-ready content.
1. Structured content: Breaking down the monolith
- Breaks traditional documents into modular components
- Enables individual tracking, updating, and reuse of content pieces
- Allows highly cost-efficient translation of just the necessary components
- Creates content once, reuses everywhere
- Eliminates redundant review cycles
- Ensures consistency across channels
2. Intelligent automation: Making content dynamic
- Transforms static documents into secure, automated workflows
- Enables automated content assembly and publishing
- Propagates updates automatically across all instances
- Reduces manual effort significantly
- Powers high-volume document processing
"Documents that are enriched with structured data will become more mainstream to help with high-volume transactional document processing." — Cheryl McKinnon, Principal Analyst, Forrester
3. The AI layer: Adding intelligence
- Enriches content through metadata and taxonomies
- Builds relationships between content components
- Enhances discoverability and context
- Provides structure needed for AI understanding
- Creates a dynamic knowledge ecosystem
- Enables context-aware content delivery
The result? A content ecosystem that's truly ready for the AI era – where information can be automatically assembled, updated, and delivered based on specific needs and contexts.
The shift in action
Salesforce’s recent acquisition of Zoomin, a technical documentation delivery platform, highlights how enterprises are adapting to the content-to-data shift. Instead of requiring content migration, their approach transforms existing documentation into structured, AI-ready knowledge. This evolution—from static documents to dynamic, structured data—illustrates a broader industry trend: organizations are moving beyond traditional content management toward AI-driven knowledge ecosystems that improve discoverability, automation, and intelligent content delivery.
Time to evolve
As AI continues to reshape how businesses operate, the ability to make content truly AI-ready will become a key differentiator between organizations that merely adopt AI and those that successfully harness its power.
The tools and technologies to enable this transformation already exist. What's needed now is a shift in mindset – from thinking about documents as things we create and store, to seeing content as a dynamic resource that can be continuously recombined, updated, and activated. Organizations that make this mental leap and begin treating their content as AI DNA will find themselves with a powerful advantage: the ability to turn their accumulated knowledge into fuel for true AI-driven innovation.
The question isn't whether to make this transition, but how quickly you can embrace it. In a world where AI capabilities are advancing daily, your content readiness may well determine your competitive edge.
Turning content into AI-ready data requires the right expertise. At RWS, we help organizations structure, manage, and optimize their content to power AI-driven innovation. Learn how we do it with Tridion Docs.
Fraser Doig
All from Fraser DoigRelated Articles
