By Marc Stephenson |
February 10, 2026
AI Auto tagging: The Modern Approach to Metadata Tagging
For years, organisations, and especially their users, have tried to avoid adding metadata to content. And honestly, it’s easy to see why. Metadata tagging is boring. It’s time-consuming. It’s hard to do well. That combination almost guarantees it won’t get done consistently.
You’ve probably heard the argument before: “We don’t need metadata anymore—we have good search engines.”
That message has been repeated for years by technology vendors and search providers. Unfortunately, marketing messages don’t always reflect reality. Yes, search engines are much better than they used to be. But the real question is this: are they good enough for organisational search?
If you’re shopping for a new HDTV, getting four million search results is fine…you can browse, compare, and refine. But if you’re searching for your company’s official maternity policy, you don’t want four million results. You want one result. The right one. And you want it first time.
That’s where traditional search alone starts to fall short.
Why AI search still needs metadata
AI tools, such as ChatGPT, are increasingly being used as search engines. In theory, they can return helpful and relevant answers. However, there’s a fundamental limitation: AI tools work on the principle of most likely results, not most accurate results. After all, accuracy requires context, structure, and understanding of what matters in your organisation.
So how do we close that gap? The answer is Artificial Intelligence for metadata tagging—or AI auto-tagging.
What is AI auto tagging?
AI auto tagging is the process of using artificial intelligence to automatically assign descriptive metadata to large volumes of content.
AI content tagging analyses unstructured data, such as documents, files and digital assets, to identify relevant terms, descriptions, keywords and asset-specific information. These metadata tags are then applied automatically, accurately and at speed.
The result: Content becomes instantly searchable by meaningful terms, without the need for people manually tagging thousands of files. Automatic metadata tagging not only eliminates a tedious and error-prone task, it also delivers faster, more reliable and more accurate search results, directly improving productivity across the organisation.
Generic context vs. specific context
There’s an opportunity to go even further. AI metadata auto-tagging becomes significantly more powerful when it’s guided by specific organisational context, not just general knowledge. Large Language Models (LLMs) are excellent at understanding generic context as they are trained on vast amounts of common knowledge. But organisational search depends on domain-specific meaning: your policies, your terminology, your structure, your priorities.
The key is to augment generic AI understanding with your organisation’s specific context. This often involves using a taxonomy or ontology to define how information should be classified and understood. There are of course, implementation considerations, such as using a localised LLM to protect intellectual property, but the results are worth it. You get AI that understands not just language, but your language.
(And yes, AI can even help generate taxonomies and ontologies—but that’s a topic for another blog.)
Final thoughts on AI auto-tagging
AI can be a powerful tool for information management, but it delivers real value only when paired with human expertise and organisational context.
With AI auto-tagging, you’re not replacing good information management practices, you’re finally making them scalable.
