Why does metadata matter in an AI-first era?

Think about how smoothly your CRM system works in today’s digital world. 

It knows exactly when a deal is closing, which leads are hot, and what needs a salesperson’s attention. These systems are fantastic at handling structured data — information that fits neatly into predefined fields and categories.

If only most critical business content fit into these tidy boxes. Workflows could be efficient. Unfortunately, the average contract, presentation, or strategic document contains rich, unstructured information that traditional systems simply can’t understand. While a CRM can instantly give you a list of customers sorted by region, it can’t understand the nuanced terms buried in contract documentation. An email system can sort by urgency, but it can’t grasp the strategic importance of revenue numbers buried in a quarterly business review presentation.

It’s critical that enterprises have the means to tap into the insights of unstructured data. One approach is by leveraging the critical pieces of information within content, and storing it as metadata attached to it making it searchable, discoverable, and, ultimately, usable within workflows, and processes.

In this age of AI, metadata isn’t just a nice-to-have. It’s the foundation for a more intelligent, efficient, and competitive AI-powered enterprise.

Understanding metadata

Think of metadata as a label on a filing cabinet — it tells you what’s inside so you don’t have to open every drawer. In the digital world, metadata includes creation dates, authors, file types, and descriptions, but it goes far deeper. It encompasses keywords, classifications, compliance tags, and contextual information that makes content discoverable, manageable, and actionable.

Challenges of traditional metadata creation

Traditionally, metadata has been either manually entered or automatically captured during file creation or ingestion. As businesses begin to realize the importance of metadata, they face the inevitable challenge of how to create it. Typically, teams do this by manually tagging content with relevant metadata, by, for example, filling out a form. They may tag contracts with attributes like client name, type, value, and key terms. Other teams might tag presentations with categories, topics, and relevant projects.

While this approach is better than nothing, it’s flawed.

Traditional systems struggle with unstructured information found in contracts, presentations, and emails, leading to inefficiencies and lost insights. Manual tagging of metadata presents challenges such as inconsistent classifications, tag fatigue, scalability issues, and hidden labor costs, which hinder productivity and decision-making. Other challenges include:

• Inconsistent classifications: Human error is inevitable. Different employees might tag the same content in different ways, leading to inconsistencies that undermine the usefulness of the metadata. Manual tagging can also be subjective, leading to inconsistent interpretations and hindering the ability to accurately analyze and compare information, resulting in bias or subjective tagging.
• Tag fatigue: Users eventually stop tagging altogether as data volumes grow exponentially. Manual tagging becomes increasingly impractical and unsustainable.
• Scale problems: Manual systems break down as content volumes grow. For example, tagging a single document might take just a few minutes, but when multiplied by hundreds or thousands (or more!), the time adds up quickly.
• Hidden labor costs: Manual tagging is a labor-intensive process that diverts employees from higher-value tasks. Employing dedicated staff to manually tag and classify content can also be expensive and inefficient, diverting resources from more strategic activities. High-value knowledge workers end up spending time on low-value tagging.

For many of these reasons, organizations typically invest in comprehensive metadata creation for only their most critical, high-value content, leaving vast amounts of potentially valuable content essentially invisible to the enterprise.

Consider a corporate merger or acquisition. Law firms bill hundreds of thousands of dollars as their attorneys meticulously review thousands of contracts, manually tagging and categorizing critical terms, obligations, and potential risks. This painstaking process is necessary because the stakes are so high, but it’s simply not scalable for day-to-day operations.

Or take a major marketing campaign photoshoot. Professional photographers spend countless hours sorting through thousands of images, manually tagging them with descriptive metadata about subjects, moods, compositions, and usage rights. This intensive effort is justified by the high value of the assets, but the same level of attention can’t be applied to the thousands of other images in an organization’s repository.

These examples highlight a crucial truth: Traditional metadata creation is so manual, expensive, and error prone that organizations reserve it only for their most critical needs. Everything else — routine documents, internal communications, project files, and countless other content types — remains poorly described and difficult to discover. Knowledge workers waste hours searching for information, often recreating content that already exists somewhere in the system. Compliance officers struggle to ensure sensitive information is properly protected. Business leaders make decisions without access to the complete picture hidden within their organization’s content.

Moreover, as content volumes grow exponentially, the problem compounds. Manual tagging becomes increasingly unfeasible, and basic automated solutions fall short of capturing the rich context needed for effective content intelligence.

All of this positions enterprises in a never-ending cycle of content sprawl and unreliable, incomplete metadata tagging,resulting in inefficiencies and loss of potential revenue.

Automate metadata extraction with AI

AI-powered metadata extraction is a game-changer for enterprises. 

Now, organizations can automatically classify documents, extract key information, identify sensitive content, and create sophisticated taxonomies without human intervention. What once required teams of lawyers or content professionals can now be accomplished automatically, consistently, and at scale.

AI has the ability to transform the extraction of metadata by “filling out the form,” so to speak, with rich metadata that makes unstructured data as searchable and actionable as structured databases.

AI-powered metadata extraction has many benefits. It can ensure consistency across content at scale, ensuring metadata gets applied regardless of volume or complexity. It can enable smarter searches by making it easier for employees to find the content they need using natural language queries or filters. It can improve data quality, by identifying and correcting inconsistencies, errors, and biases in existing metadata, ensuring data accuracy and reliability.

Here are the three biggest categories of benefits of AI-powered metadata extraction:

• Automatic classification: AI can read and understand content like a human would, automatically extracting relevant metadata, such as keywords, dates, and entities (e.g., names, places, or product IDs), categories, and classifications for all kinds of documents, emails, images, and videos.
• Uncover content relationships: AI doesn’t just tag content. It can understand the relationships and relevance between different content. For example, it can ensure that a customer presentation is automatically linked to related customer accounts in the CRM, relevant contracts, and ongoing projects.
• Contextual understanding: Today’s generative AI models understand context and nuance. AI-powered metadata extraction can therefore distinguish between different priority levels based on content and context, not just predefined rules. It can look beyond the content, and understand intent and reasoning

Taking advantage of AI-powered metadata extraction results in reduced compliance risk, faster processing times, and more consistent global operations, all while freeing up highly skilled professionals from manual document review.

Advantages of AI-powered metadata across industries 

Let’s look at a few common scenarios across different industries where AI-powered metadata extraction can elevate workflows and reduce operating friction.

Financial services firms can better manage regulatory documentation, customer communications, and transaction records. Finance teams can extract payment terms, invoice amounts, and vendor information from financial documents, streamlining accounts payable processes and improving audit readiness. 

HR can automatically classify and tag employee documents, ensuring proper handling of sensitive information and streamlining onboarding and compliance processes.

Healthcare professionals can help hospitals, clinics, and other organizations to automatically classify patient records, identify protected health information, and ensure HIPAA compliance.

Manufacturing companies can organize technical documentation, quality control records, and supplier information more effectively.

The future of metadata in an AI-first era

With ever-growing volumes of content, there’s never been a greater need to plug unstructured data into your workflows. After all, 90% of every organization’s data is unstructured, and workflows are only as good as the content that fuels them. . 

AI-powered metadata extraction is a transformative solution, automating the classification and tagging of documents while ensuring consistency and accuracy. It allows organizations to uncover relationships between content, to understand context, and to streamline workflows. Businesses can reduce compliance risks, improve operational efficiency, and enable smarter decision-making, ultimately turning content into valuable insights.

Enterprises that fail to manage their content effectively risk falling behind competitors who use metadata to unlock efficiency, agility, and innovation. By embracing AI-powered metadata, organizations can turn exabytes of unstructured data into clarity, reducing operational friction and enabling smarter, faster, more informed decision-making.

Learn how to power intelligent workflows with metadata here.