AI and metadata together have the potential to bring structure to unstructured data, powering scalable and efficient content workflows.
Think about how powerful a simple organized database of employee records is to use. The information in it can be filtered, sorted and aggregated, making it easy to dial into specific details about an individual or answer global questions like “How many employees do we have in each area of our business?” or “What’s the average tenure of an employee here?” These queries are made possible due to the structured column and row format, enabling indexing of the data for efficient retrieval.
But what if you want to do something in your organization that relies upon information and records from unstructured data — word processing documents, slide decks, audio recordings, meeting transcripts, emails, and other files that can’t be neatly filtered and sorted by label and category? That type of content makes up a resounding 90% of your organization’s data — and its rich valuable data, if you could mine it. But without structured records built into the framework, knowing what’s inside all those files has been historically challenging, as traditional systems simply can’t understand it.
The great divide between structured and unstructured data has always meant it’s simple to get answers and insights from the former, but not the latter. And although Machine Learning approaches have unlocked some use-cases such as image recognition, they have required substantial training and tuning, and were applicable only in narrow pre-defined content types.
Recent advances in large language model (LLM)-based AI make it far easier to parse diverse sets of unstructured data, tapping into the information contained within all kinds of nuanced content. But to effectively power apps at scale, you still need the ability to sort, filter and aggregate on structured data records attached to these unstructured data types. Fortunately, AI can actually enable this capability, even on the most complex of content.
Metadata: Creating order from chaos with AI
This is where metadata comes in. Essentially “data about data,” metadata is the crucial information that provides context and structure to content, making it easier to process at scale. It’s key to enabling automated workflows and routines at scale, turning the information harbored within unstructured data into structured information.
Metadata is a common prerequisite for workflows, but until now, the extraction of the metadata itself was slow and costly, leveraging either manual efforts or highly specialized narrow tools. AI simplifies metadata extraction significantly. It automates the process of sifting through the contents of a file to extract metadata, even when it’s buried or inconsistently formatted. AI recognizes and extracts metadata from a variety of formats—images, videos, documents (PDFs, Word files), etc.—without needing format-specific tools (and can also handle unexpected issues, like corrupted files) And if metadata is missing or incomplete, AI can fill in the gaps by working with patterns.
In action: AI-powered content workflows
AI models can be built into existing workflows (like a content management system or data pipeline) to automatically extract and manage metadata as files are uploaded, created, or modified. AI-powered metadata holds the power to transform the workflows organizations use every day. By speeding up content classification, metadata gives unstructured data the structure it needs to automatically trigger custom workflows.
Here’s an example: typically, in a procurement process, a company will issue a purchase order (PO) to a vendor, and in return, the vendor will issue an invoice. For the company’s Accounts Receivable department, though, there may be many purchase orders going out and many invoices coming in. In an ideal world, every incoming invoice would have the correct corresponding purchase order number listed, but as any receiver will attest, this isn’t always a reality. Humans have spent a lot of time over the years trying to match up invoices with purchase orders to get people paid and the books balanced.
With metadata, organizations can more easily assess a mismatch between invoices and POs and automatically surface likely matches. Even if the vendor contracts are in different file formats, AI can handle that complexity and be flexible enough to work with any document structure. Accounts Payable can then streamline and speed up the repetitive process of invoice processing by extracting payment terms from the various invoices, automatically routing them for payment.
Keeping humans at the center: AI-powered metadata extraction
Nowadays computers can certainly do a lot of things for us that humans used to need to do manually. But instead of talking about the ways in which AI can replace human work, the more meaningful conversation is around how AI can enable humans to do more by giving them better tools and replacing rote work. By leveraging AI-powered metadata extraction, we take a cumbersome, often manual process, and replace it with a flexible scalable one.
Today AI enables us to quickly spin up automated metadata-driven workflows, allowing us to work with our content in brand new ways. AI and metadata interacting together has the potential to give unstructured data the structure it needs to be put to use.
Metadata has always been the key to derive insights from our content and power our automation. Now AI is enabling us to do this with unprecedented scale and flexibility, unlocking a brand new frontier of content management.
