As much as we love to run around in some proverbial pandemonium, afraid that AI is going to “take our jobs,” it’s easy to forget that AI is also creating new jobs. Better ones.
We’ve all heard about the “AI gap,” where businesses, governments, and enterprises are adopting AI to streamline workflows, quicken decision velocity and send productivity through the roof. But in reality, nobody’s actually getting it done — effectively, anyway.
That’s because we’re beginning to understand that AI is only as good as the data it’s fed. We’ve never before needed the level of data hygiene that’s required to make AI work. Now that we’ve got access to exponentially more data, we need someone to clean, structure and standardize it across all levels of business. Even in little pockets like content.
Your content data and automation needs to be uniform to genuinely accelerate performance through AI. It also needs to be able to “talk” the same language as the data across the entire business to avoid organization-wide silos that prohibit real gains. Someone has to make this happen — and that someone is a content engineer.
Let’s look at the role and see how a data engineer can make waves in your content and marketing function.
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What Is a Content Engineer?
A content engineer is a technical specialist who structures data and optimizes CMS workflows to enable digital content scalability, machine-readability and automated delivery across diverse platforms. They’re a critical spoke in the wheel of AI-readiness and content automation at scale, effectively bridging the divide between an AI content strategy and its technical execution.
Teams need content engineers because generative AI and machine learning tools thrive on structured data (from taxonomies and metadata to CMS architecture). A content engineer prepares a semantic model that enables AI to retrieve accurate, valuable information from your content.
Without detailed content engineering, it’s too easy for AI to produce hallucinations and drift, which can derail production and strategy at best, or the organization’s reputation at worst. When your data foundations are solid, engineering enables the separation of content into “modules” that AI can reassemble for different audiences and use-cases instantly, delivering personalization and reach at scale.
Differentiating Content Engineer vs. Other Content Roles
The content engineering profession touches on basically every aspect of your content team, so if you’ve confused the role with any other, we forgive you. Here’s how they stack up:
- Content strategists: Define what content to create and why. Content engineers design processes and workflows to execute the strategy systematically.
- Content operations: Manages the people, budgets and workflows. A content engineer provides the technical systems and data models that drive them.
- Content management: Oversees daily editorial lifecycle, approvals and brand voice. Content engineering provides the systems to seamlessly scale those narratives across every customer experience.
- Content creators: Execute content creation. Engineers build the automation and tools to execute more of it, and with higher-quality outputs.
What Does a Content Engineer Do?
Scaling systems and automating workflows is likely too vague a description for someone looking to hire for the role or develop their career into it. Here’s a more granular look at what a content engineer does and why it matters.
Taxonomy and Metadata Design
Content engineers build the underlying data architecture of your AI content with metadata tags and taxonomies. So, rather than categorizing content as Electronics, you might use Electronics > Smartphone Devices > iOS Devices.
Machine-readable labels allow large language models (LLMs), AI tools and algorithms to understand context and surface content instantly, which has two benefits. First, all content is “visible” to AI and therefore working hard for your strategy (even your best content, if poorly labeled, serves no value because it can’t be found). Second, it directly improves how a search engine or chatbot discovers and recommends your brand.
Structure and Schema
Structured content frameworks keep information logical, crawlable and indexable for a search engine. When your content follows a consistent hierarchy, for instance, strict rules for headers and formats, generative AI can digest your content as flawlessly as a kiwifruit, strengthening your SEO, AIO and GEO.
Workflow Automation
Using APIs, your content engineer creates pipelines that move data between your software stack, including CMS, DAM and AI tools (basically helping them “talk” to one another). This means no copying and pasting the latest assets from one platform to another; they’re all connected via a central LLM.
The role also involves upskilling marketing teams for AI so they can implement more pragmatic content workflows, accelerating content production using one interconnected system, rather than 10 disconnected ones.
Content Modeling
Content modeling is similar to how you find stuff in a library. You can search for information based on the Dewey Decimal System, or by author, title or subject. Inside a book, you can find information by page number, chapter title, and in many cases, references or indexes.
When you translate that into content, which is known as content modeling, you get different searchable fields for each asset (headline, author, body text, image content, templates, product links, etc.).
Defining how different types of assets and their data points relate to one another in content ecosystems enables massive reuse across platforms like LinkedIn, assets like emails and white papers or channels like webinar landing pages.
What Skills Does a Content Engineer Need?
A content engineer should be a critical problem-solver and systems thinker who exemplifies deep technical curiosity. While they understand the mechanics of traditional (ahem, legacy) content workflows, they can also pull out a vision to rebuild them into AI-powered pipelines that solve the real-world production bottlenecks we all know and love.
Short of turning water into wine, here are a few skills they should have:
- Technical: Understanding data-exchange formats (XML, JSON) to create interoperability between your APIs, CMS platforms and AI tools and achieve accurate data transmission.
- Information architecture: Creating complex taxonomies and ontologies to map pathways between data points and define a clear content schema.
- AI literacy: Understanding prompt engineering to consistently generate brand-accurate content and retrieval-augmented generation (RAG) to limit hallucinations, re-training and model drift.
- Strategic communication: Bridging the priorities of creative teams (the reality of production workflows, and often using plain English) and the IT/Dev teams (technical requirements and implementation logic) to keep everyone, somewhat miraculously, on the same page.
- Optimizing for multi-channel delivery: Ensuring assets are correctly formatted for different channels, including voice, mobile, web and apps.
Professional backgrounds for content engineers can range from highly technical fields such as computer science, IT and systems architecture to content strategy and digital marketing. Either way, it’ll require cross-education from both sides to ensure a technical and practical understanding of the systems in which they’re working.
Why Do We Need Content Engineers?
AI is shaping the way marketers do their jobs, but in many ways, it also isn’t. We’re mostly just swapping old challenges for new ones — and we need specialized skills to help us solve them.
The most monumental challenge marketing teams face (also small businesses, government and enterprise) in operationalizing AI and automation today is unstructured data foundations (lack of a common “language” from which AI can draw data and produce value). An engineer transforms unusable data into highly practical information that speaks to AI, LLMs and bots.
Brands with strong content engineering can pivot to new platforms like voice search, VR, and AI search and scale with automation significantly faster than those without it. If your AI implementation is failing, consulting with a content engineer may well be what you need to close the gap.
Content Engineers: The Bridge Between Your People and Your Processes
Content engineers create a necessary bridge between the evolving content function and increasingly complex AI and automation processes. They must connect human creativity with automation and scalable systems to help businesses gain a competitive edge in today’s marketing landscape.
Content teams will eventually need to start moving away from Google Docs and towards content models. Content engineers will be the first step in sustainably actioning that change.
