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Picture this: a tool that can sift through your contracts, tag them accurately, and make them easily searchable - all without you lifting a finger. 

That's the promise of AI tagging for contract workflows. 

With AI tagging, you can focus on what really matters - the content of the contracts, not the laborious process of organizing them. 

This post will guide you through the benefits of AI tagging and how to implement it effectively, ensuring your legal team stays ahead of the curve.

Here’s what we’ll cover:

  • What is AI-assisted auto-tagging? 
  • How does AI tagging work?
  • What are the benefits of leveraging AI in tagging? 
  • What are the limitations of AI tagging?
  • What type of environments benefit from auto-tagging?
  • What types of tools are available for auto-tagging?

What is AI-assisted auto-tagging? 

To understand what AI-assisted auto-tagging is and how it works, we have to start by taking a couple of steps back.

First, let’s define tags.

Tags are a form of content metadata that identify what a piece of content (document, video, image, audio file, etc.) is about.

Metadata is information that is stored in the file itself but is not visible or audible when you look at the file. 

For example, a metatag on an audio file might include data about the track and artist names. You don’t actually hear those names read when you listen to the album, though. 

But that’s how, when you throw a CD in your car stereo, the player recognizes the artist and how long the tracks are.

Tags (sometimes called metatags) are just one kind of metadata.

We should note here that tags in this context differ from hashtags (used on social media platforms), not least because hashtags are user-defined, whereas metatags must follow a defined list of taxonomy terms in order to be widely understood by tag readers.

Next, let’s look at the tagging process.

For a piece of content to include all of the various pieces of information in its file code, someone (or something) needs to add those tags.

Traditionally, this was a manual process, where a human would have to say, “This image contains X.”

Clearly, this is a tedious process. That’s where AI tagging comes in.

Modern AI platforms can “read” files and understand what’s in them to automatically define and add the relevant tags. For example, an AI might look at an image of a fish and be able to add the tag “fish” without a human having to first tell them what’s in the file.

“I’ve been in Silicon Valley over the last 25 years, watching it go from consumer electronics and hardware companies to software-as-a-service and now ChatGPT and artificial intelligence. It’s been pretty exciting seeing where things are going and just being part of the history of Silicon Valley."

~ Doug Luftman, ex-DGC, DocuSign
The Key to Success as an In-House Legal Counsel & Leader
Also read: The Ultimate Glossary of AI Terms Every Legal Team Should Know.

How does AI tagging work?

AI tagging software uses a range of technologies to automatically tag content:

  • Machine learning (ML) and deep neural networks (mathematical pattern analysis and matching)
  • Named entity recognition (matching proper nouns identified in written text)
  • Semantic analysis (identified concepts referenced within written content)
  • Natural language processing (NLP) that analyzes written content at the sentence level

Most auto-tagging software will require some form of training, though you can expect a paid-for solution to arrive with some of this training already completed.

For example, a contract lifecycle management (CLM) platform with AI tagging will be able to recognize common terms and clauses but may require a little training to understand instances that are highly specific to your organization or industry.

Also read: 8 Top Contract Management Software Platforms.

What are the benefits of leveraging AI in tagging?

So, why invest in an AI-based tagging tool rather than just tagging content manually?

#1 AI tagging is faster

Manual document tagging is a tedious and time-consuming process.

This is especially true if you’re dealing with long documents (like a 100+ page corporate contract) that might require hundreds of tags to file correctly.

Tags can even be added to content in real-time, such as live podcasts.

#2 AI tagging is more accurate

AI-powered auto-tagging doesn’t make the same mistakes as humans do. It doesn’t get tired after 50 pages and revert to only adding the most important or obvious tags.

Sure, it's not perfect (more on that soon), but if you instead invest time in training and improving the AI and ML system, you’ll get much more effective and accurate results.

#3 AI tagging is more consistent

One of the problems with manually tagging hundreds of documents is that it often requires several people.

It's only normal, then, that inconsistencies arise, as those responsible for adding tags follow different processes or identify and categorize tags differently. 

#4 AI tagging improves searchability

More tags (especially more accurate tags) give you more data points through which to search.

This means that searchable contract repositories are more effective, making it easier to find documents when you’re looking for them.

Also read: Contract Repository: Everything You Need to Know.

What are the limitations of AI tagging? 

AI tagging can only assess what is actually in the content. It can’t, for example, understand the intent of the content, unless that is specifically discussed in the content itself.

For example, an image or infographic that is designed to provide education to its reader may still require a manual tag to explain its intent.

In other cases, descriptions and tags might be too vague or miss critical elements. Part of this comes down to how you train your AI, but there are distinct limitations in such a new and emerging technology that will improve over time.

The overall quality of your AI tags will also be based on the accuracy and completeness of existing tags. So, if you’re training an AI tagging system on bad data to begin with, you’ll be less likely to see desirable results.

Also read: AI Contract Analysis: Saving Time and Increasing Efficiency.

What type of environments benefit from auto-tagging? 

There are a number of important use cases for AI auto-tagging.


Text-based documents like contracts and SOPs (standard operating procedures) are a great candidate for automated content tagging.

Tagging your contracts, for example, helps with categorization and storage, allowing you to search for documents more easily. Instead of being confined to searching for the agreement’s title, you can instead filter by contract value or search for specific subject matter without having to match the exact language in the clause.

Text-based documents like legal agreements are the best use case for AI tagging, as AI and NLP are already very good at reading and interpreting written content.


Image tagging is the next best environment for AI tagging.

Auto-tagging can, in most cases, identify what is in an image. It can spot things like people, objects, locations, colors, and settings.

In more advanced cases, the AI engine can interpret collections of these data points and make inferences. For example, an image containing two groups of people wearing matching clothing, an ice rink, and a puck may be correctly identified as a hockey match and tagged appropriately. 

Audio files

Audio files can also be tagged, though it is generally more limited than written or image-based content.

The most advanced AI is beginning to recognize events and segments in audio, but it is still mostly confined to the linguistic content of audio. For example, it can transcribe a podcast and add contextual timestamps.


Video is, for all intents and purposes, the combination of an audio track with thousands of images played sequentially.

As you might imagine, this is within the realms of AI abilities, but it is the most difficult of all tasks to achieve.

So, while AI can be used to auto-tag videos, it generally needs a lot more human intervention than other use cases.

Also read: Unlocking the Potential of AI in Contract Drafting: Enhancing Speed and Compliance.

What types of tools are available for auto-tagging? 

There is no one-size-fits-all tool for auto-tagging. Some solutions tackle one specific kind of content, whereas others can handle all content types but only cover certain aspects of tagging.

In general, there are three categories of software tools for AI tagging.

#1 Classification services

Classification services combine AI with big data to add classification tags.

They’ve been trained on huge content repositories, meaning there’s a reasonable chance that the AI has seen content similar to what you’re feeding it.

Amazon Rekognition is an example of a tool in this category that is available via API.

#2 Metadata management solutions

Metadata management solutions were around before AI. They are the platforms that content creators and owners use to manage and organize content.

More recently, metadata management platforms have begun to incorporate AI-based auto-tagging.

Poolyparty, for example, offers an API that can connect to an existing repository and tag the content within it.

#3 Vertical solutions

Vertical solutions are AI tagging platforms designed to target specific domains. 

They are most commonly found in the ecommerce world (for adding metadata to products sold online, for example), as well as in content creation circles.

As the world of AI develops, we’ll begin to see more and more legal software solutions that incorporate auto-tagging to improve the storage and retrieval processes.

“I cannot even begin to think how much all the different categories of AI are going to change how legal work is delivered internally and by law firms as well as by ALSPs over the next couple of years. Understanding that is a key role that legal operations will play.”

~ Akshay Verma, Chief Operating Officer, SpotDraft
Shaping a Purpose-Driven Career in Law

SpotDraft: Bringing AI to contract lifecycle management 

Auto-tagging is just one way in which today’s powerful artificial intelligence can make legal operations more effective and efficient.

SpotDraft, our CLM solution, is bringing smart AI tools into the legal world.

SpotDraft AI helps you unlock a new level of productivity and gain valuable insights from your contracts with powerful features like:

  • Instant contract template creation from existing Word docs
  • AI-powered contract reviews and due diligence 
  • Smart Data Capture to help you pull out key insights, without having to wade through lengthy agreements 

Experience the future of legal contracting with SpotDraft AI.

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