How AI For Legal Documents Fuels Innovative Law Firms

Law practices are bound to paperwork because almost any litigation process requires collecting and working with documents. Depending on the type of proceedings, companies have to sift through hundreds of legal documents, taking notes and finding relevant information. With the help of machine learning and document review tech, any practice of law becomes more efficient.

If you run a legal firm yourself, you’d probably agree that spending much time on manual paperwork doesn’t create value. On the contrary, you’d rather invest time in taking more cases or focus on business operations.

Over the last couple of years, AI in the legal sector has seen a tremendous spike in quality and availability. Lawyers and legal departments can automate routine tasks, get help with contract review and contract analysis, implement chatbots, or track work time more efficiently. According to Thomson Reuters, these changes might take the shape of three waves, where companies will go from optimized legal workflows to the emergence of true winners who embrace AI agents and optimize the majority of business processes.

However, dealing with paperwork is among the most challenging areas. That’s where document automation comes in handy.

Legal AI-powered tools in that domain focus on providing a wide range of services related to document analysis, assessing potential risks, and helping make more informed decisions.

Read this article to learn how AI legal technology is a catalyst for change – from a traditional firm to an innovative one.

Where does document Artificial Intelligence help in the legal sector?

The legal sector generates and receives many different types of documents.

Older technologies, which were usually based on templates (a pre-AI approach), required time-consuming setups, constant maintenance, and adjustments whenever a new document structure came in. Also, they could complete only the most basic tasks and left much of the manual work still to be performed by humans.

Furthermore, the specifics of legal language and geographical differences made it even harder to automate.

AI addresses and removes the obstacles of pattern-based software.

Thanks to the application of NLP – Natural Language Processing, artificial Intelligence can assist lawyers in tasks revolving around creating, reviewing, reading, sending, classifying, and archiving documents.

NLP models can be used to perform several actions on a single document: read it, find information in it, and validate this information against a database.

They also help introduce order by classifying documents automatically and can be used as tools to fuel other systems with data found in the stacks of paperwork. On top of it, legal AI tools help reduce human errors.

Automating repetitive tasks in practice revolves around addressing the time-consuming aspects of working with documentation.

Let’s review examples of processes simplified with AI for legal tech.

1. Understanding and organization of document stacks

Whenever a new case opens, lawyers typically begin sourcing as much information about it as possible.

Depending on the type of practice, that may refer to gathering data from public and private registries, as well as documentation provided by the client. Altogether, the paperwork may require a lot of cataloging and reviewing.

However, AI models used in legal tech can be trained to recognize any given number of document types based on a sample. Instead of categorizing the files manually, you can upload documents to an automation platform and get them sorted out automatically.

The benefit in legal company terms is not only saving time, but introducing logical order and assigning documents to particular folders.

2. Access to the key information about the case

Another key impact of AI technology on a law firm’s internal process is the faster gathering of data.

Since law firms often specialize in certain types of litigation or a niche in the market, it is natural to gain expertise over time and understand which information is important in the process and which is not.

Consider the example of a legal company operating in the field of debt collection. 

Let’s start with a short explanation to paint the picture.

Unless the debt is resolved on schedule, it usually becomes a non-performing loan, subsequently becoming the responsibility of an internal collection team (usually found in large corporate structures) or part of a debt portfolio purchased by a third party – the company used in our example.

That third-party company gathers data from documents, and the quicker the proper information about the debtor is generated, the higher the chance of resolving the debt successfully.

Law firms that specialize in this field work closely with courts and bailiffs. Both institutions issue more than 80 types of varying documents that contain essential information for the law firm to either start or successfully close the collection process. These documents are generated at various stages, and there’s often a short time window for their validity.

AI for documents speeds this process up immensely. 

The law firm in our example can use a legal automation platform to recognize all 80 classes of documents and read more than 30 data pieces from them. When automation is in check, even vast amounts of documents will not be a problem.

As a result, the company gains insights quickly and efficiently and can proceed with generating new documents that include the extracted data, validate the sums and personal details against another database, and so on.

This example is not limited to the niche of debt collection. A similar process can be built for any litigation proceedings or business-to-business credit analysis.

3. Quick validation of provisions and records in the documents

Thanks to the OCR feature, which refers to optical character recognition, documents are firstly read by the machine, and then a mask is created on the document. A mask allows the user to copy entire text from a photo or a scan, as well as recognize particular elements of the document’s structure.

How does it benefit a lawyer working on a batch of documents?

Let’s consider an example.

In cases revolving around indemnity, there are certain provisions in the documents that may increase or decrease the chances of a successful litigation process. 

Since there are no unified structures of documents used across companies, countries, etc., manually validating whether certain provisions are included would take a lot of effort.

Document automation solves this issue by combining the OCR and extraction features. By creating a process where the AI model seeks particular types of information, you can scan documents in search of particular phrases. That way, you and your client know the chances of winning the court case before investing a lot of time.

The mask can also be used to copy chunks of text and use them elsewhere – for example, in generating new documents (instead of writing them from scratch).

One last aspect of OCR is object recognition. This feature helps you find visual elements, such as signatures or stamps. It cuts the time needed to verify if the document has been properly signed and is, therefore, valid.

4. History of documentation for similar cases

Law companies not only work with documents but also need to store them.

Keeping a log of previous cases benefits a law firm in several ways. Firstly, having such access may speed up future proceedings and create certain patterns for new clients. 

Needless to say, data privacy is of the utmost importance because documents contain personal information regarding one’s health, financial history, and so on. Therefore, utilizing free online tools for document reading is out of the question for legal businesses.

Luckily for you, there are already solutions that provide utility and safety.

Modern legal software that implements artificial intelligence is typically cloud-based and follows strict guidelines for maintaining data privacy. These guidelines may be defined by globally recognized certifications, such as ISO27001.

5. Bringing new lawyers up to speed

Less-experienced lawyers are often burdened with the most tedious tasks since they lack experience “in the field.” However, legal document automation may also positively impact the onboarding process and help them address the most time-consuming tasks immediately.

The historical data stored in an AI platform brings value to new hirings, who can search the database of documents, find relevant information quickly, and onboard faster. 

Where to start with legal tech automation?

The legal sector has been slowly but steadily opening up to innovation, yet embracing AI tools is still considered a pioneering move. It’s a step toward transforming your legal practice into a fast-moving one, with leaner internal operations and higher resilience against human-made mistakes.

Since documents will never go anywhere, and certainly not in the legal sector, consider starting with AI for documents today.

If you feel encouraged by the promise of a more innovative approach to your company’s operations, contact our Alphamoon experts by clicking below.

We will show you how the document AI platform can be fine-tuned to your needs.

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