Reviewing and analyzing documents is one of the most labor-intensive activities in a lawyer’s work. In M&A transactions or e-discovery processes, where hundreds of contracts need to be checked, manual analysis is time-consuming and error-prone . Using AI to compare documents can reduce this time by up to 90%.
AI tools solve this problem by automating the review. But every contract sent to the cloud for analysis is a potential leak of professional secrets. That’s why at nFlo we start with the fundamentals: Managed Security and Compliance/GRC, which ensure that the AI analysis process is secure and legally compliant.
Shortcuts
- Why is document review a challenge for law firms?
- How does AI improve the analysis of contracts and legal documents?
- Can AI find key information in documents faster than a human?
- How do AI tools detect risks and inaccuracies in contracts?
- How much time can you save with automated document analysis?
- Does artificial intelligence help with due diligence and e-discovery in transactions and disputes?
- Does AI reduce the risk of errors when checking documents?
- What AI tools for document analysis are lawyers using?
- Will junior lawyers lose their jobs by automating document review?
- How does the use of AI in document analysis affect costs for the customer?
Why is document review a challenge for law firms?
Reviewing and analyzing documents is one of the most labor-intensive activities in a lawyer’s work. Imagine an M&A transaction where hundreds of contracts of the target company have to be reviewed, or a lawsuit where both sides exchange thousands of pages of documentation (known as e-discovery). Traditionally, this requires the involvement of many lawyers and assistants, who painstakingly read each document, excerpt relevant passages, compare versions. This is not only time-consuming - it also carries the risk of errors. A person, reviewing the hundredth agreement in a row, may overlook something out of sheer fatigue. For a law firm, this means costs: you have to pay for hours of work, often overtime, and there is still no guarantee that nothing important will be missed. The challenge is compounded by the growing volume of data year after year. More and more communication is done by e-mail, electronic documents are coming in, files from various formats - all of which can be potential evidence or have legal significance. Manual queries in such a mass of information simply become unfeasible in a reasonable time. Hence, law firms are looking for technology support, and AI seems ideal: it’s designed to process large data sets, catching patterns and differences. By automating the review of documents, the burden on the legal team can be significantly reduced and the entire process can be shortened from weeks to days or even hours.
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How does AI improve the analysis of contracts and legal documents?
Artificial intelligence in document analysis works like an experienced assistant that never tires. It uses text search and machine learning algorithms to catch key information, differences and potential problems in documents. For example, we have 50 copies of contracts with different contractors - AI can compare them and point out which clauses differ between versions. It can do this much faster than a human, and with apothecary precision, highlighting every word that disagrees. When it comes to analyzing contracts, AI tools can identify relevant provisions (e.g., term, amount of contractual penalties, confidentiality clauses) and compile them into a report. As a result, a lawyer doesn’t have to read the entire contract from cover to cover - he or she gets synthesized information about the most important elements and can immediately focus on assessing risks. What’s more, AI learns from multiple documents, so if a standard clause is missing from the contract or is worded unusually, the system will signal it. It’s a bit like having a checklist for each contract, only it is created dynamically by an algorithm that “knows” what a typical well-written contract of a given type looks like. In practice, this streamlines the work - instead of manually digging through the text, the lawyer gets an abbreviated overview and a list of things to look at. It’s worth adding that such tools often integrate with law firm systems - for example, they can automatically analyze incoming emails with attachments and categorize documents into appropriate cases. All in all, AI plays the role of a filter and helper here: it preliminarily “reworks” the documents so that the lawyer can evaluate them more quickly and confidently.
Can AI find key information in documents faster than a human?
Definitely yes. This is one of the main advantages of AI - the speed of searching through text and data. The computer does not read sentence by sentence at a human pace; instead, it practically simultaneously analyzes the entire document or hundreds of documents, looking for specific phrases or concepts. If a lawyer, for example, wants to find all the provisions for contractual penalties of more than PLN 100,000 in a hundred contracts, AI will do it in a few seconds, while a human would have to spend many hours reading. Moreover, modern algorithms are not limited to simple keyword searches. Using natural language processing (NLP), they can understand context. That is, even if in one contract a penalty is called “contractual remedy” and in another “contractual sanction,” AI is very likely to find them anyway and assign them to the same category of terms. This ensures that key information doesn’t slip away just because someone used a different vocabulary. AI also has a memory for details - it can easily point out all the places in a thousand-page document where, for example, the name of a company or a specific patent number appears, which for a human would be cumbersome. We see this, for example, in due diligence applications that generate a list of all leases or all assets listed in transaction documents with a single click. Such intelligent searching gives lawyers instant access to the heart of the matter, rather than getting lost in a sea of information. Of course, AI has to be configured correctly and “taught” what to look for - but once learned, it works instantly and there is no problem with overlooking something due to fatigue or distraction.
How do AI tools detect risks and inaccuracies in contracts?
In addition to finding information, AI can also evaluate and compare the content of documents for potential risks. This works in several ways. First, the system can have a built-in model of an ideal contract - a set of clauses it should contain and its preferred wording. When it analyzes the actual contract, it compares it to this model. If something is missing (e.g., a confidentiality clause is missing) or a clause deviates from the standard (e.g., an unusually high contractual penalty), AI will flag it. Second, the tools learn from thousands of documents, including those that have been flagged as problematic. As a result, they can detect red flags - such as unusual wording that may give rise to ambiguity, or provisions that have caused disputes in the past. For example, AI can warn, “this arbitration clause is worded differently than usual and may give rise to a risk of unenforceability.” Third, AI compares documents among themselves. If, in a bundle of 50 similar contracts, one has completely different terms (e.g., a much shorter notice period than the others), the system will catch the difference, letting the lawyer know that it is worth checking. AI tools often present the results in a user-friendly form - e.g., a risk report by category (financial, operational, legal risks) or even in the form of graphical “lights” (green - ok, yellow - worth looking into, red - potential problem). All this makes it possible to quickly identify inaccuracies and elements that require negotiation or improvement. In practice, lawyers using such analysis feel safer - they are sure that they have not missed anything important and can go straight to addressing the issues they have found, instead of only laboriously searching for them.
How much time can you save with automated document analysis?
The time savings are enormous - and importantly, have been measured in practice. According to data cited by one consulting firm, using AI to compare documents can reduce the time for such analysis by up to 90% compared to manual methods . In other words, a task that usually takes lawyers 10 hours, with the help of AI, can take only 1 hour. This order of magnitude of efficiency is confirmed by the experience of many law firms, especially with due diligence in transactions - where teams used to spend weeks sorting and reading files, they can now generate a report with key findings in a few days. Another example is e-discovery in litigation: tools based on machine learning can analyze hundreds of thousands of emails or documents in a single day, whereas a manual review of such a volume of material would take a team of lawyers many months (and cost crores). Gartner’s research also indicates that the use of AI in the work of legal departments typically increases their productivity by at least a dozen percent over the course of a couple of years - due in large part precisely to the time savings in reading, comparing and retrieving information. On a weekly basis, as we mentioned earlier, this can be as much as several hours of a lawyer’s time regained. And what can be done with the saved time? Serve an additional client, analyze a difficult legal problem in more depth, or simply close a particular stage of the case more quickly (to the satisfaction of the client who is waiting for the result). Of course, the automated analysis itself is a start - lawyers still have to review the results and make decisions. But instead of wading through hundreds of pages, they can focus on the important bits right away, because the rest of the selection has been done for them by the machine.
Does artificial intelligence help with due diligence and e-discovery in transactions and disputes?
Yes, and it is in these areas that AI has proven to be the first “game changer” in the legal industry. Due diligence - that is, a comprehensive legal analysis of a company before, for example, a purchase or investment - traditionally meant an army of lawyers reviewing company documents: contracts, licenses, lawsuits, administrative decisions, and so on. The use of AI means that much of this tedious work can be automated. Systems can scan documents stacked in the data room and automatically categorize them (e.g., all employment contracts, all contracts with key suppliers, etc.) and then extract the most important data from them (e.g., duration, amounts, parties to the contract). Such an automatically generated due diligence report is a great base for a lawyer - instead of starting from scratch, he gets the key risks already mapped out and knows where to look deeper. In the case of e-discovery (i.e., the process of disclosing and reviewing electronic evidence in litigation, mostly in the common law system, but increasingly here with large cases) AI is downright invaluable. Imagine a dispute in which you have to analyze all of a large company’s email correspondence from five years - it could be hundreds of thousands of messages. Algorithms can sift through such collections for keywords, people, dates, and even learn which documents are relevant based on examples (so-called predictive coding). Interestingly, already a few years ago, the American Bar Association studied the use of AI in e-discovery - at least 10% of lawyers at the time declared that they used AI-based tools to select documents in litigation . Today, that percentage has arguably increased. As a result, where evidence review once dragged on for months and generated enormous costs, it is now sometimes a matter of a dozen days of intensive algorithmic work and review by a lawyer. Bottom line: AI has become an ally of lawyers in large transactions and disputes, making it possible to deal with masses of documents that would otherwise be overwhelming or require a disproportionate amount of work.
Does AI reduce the risk of errors when checking documents?
Yes, one of the most important advantages of AI is the reduction of the risk of human mistakes. Think of what mistakes can happen with manual analysis: overlooking a key clause, misinterpreting a clause by a tired lawyer, failing to find all occurrences of a given provision in the text (because, for example, a synonym was used in one place). AI is impartial and consistent in this regard - it will analyze every paragraph of the document with the same attention, because for the machine it is simply data to be processed. If it is well configured, it will not miss a single chapter or footnote. What’s more, advanced tools can signal potential errors or inconsistencies themselves. For example, if a contract says “30-day deadline” in one place and “60-day deadline” for the same event in another by mistake, AI will signal the conflict. Or if the name of a party to a contract is spelled differently in different paragraphs (e.g., once the full name of the company, once the abbreviation), the system can catch this and recommend unification - something it might otherwise miss, resulting in formal doubts. According to many lawyers, automatic verification acts as an additional layer of quality control. Of course, a human is still needed to give final approval of changes or interpret the result - but with AI support, lawyers can be much more reassured that they haven’t missed anything important. In an industry survey, as many as 35% of professionals indicated that they count on AI precisely in the context of reducing human errors. This is a significant number, showing that there is widespread awareness of the qualitative benefit. In practice, fewer errors mean not only peace of mind - it also means less risk of liability for overlooking something important, and a better reputation for the law firm (efficiency and accuracy).
What AI tools for document analysis are lawyers using?
A growing number of specialized tools sometimes referred to as “AI review” or “contract analysis tools” are appearing on the market . Among the pioneers were systems for due diligence and contract review, such as Kira Systems and Luminance, which used machine learning to identify clauses in contracts. Today, they are joined by others: such as Evisort, Leverton (for real estate), and Ayfie (for e-discovery). Large legal software providers are also integrating AI into their products - for example, case management platforms are adding AI-based document analysis modules. In 2023, there was buzz about an AI assistant called CoCounsel developed by Casetext (later acquired by Thomson Reuters). CoCounsel, based on the GPT-4 advanced language model, can, at a lawyer’s request, conduct a contract review, document analysis or even draft a legal memorandum - and in minutes, not hours . In other words, we enter into the system, for example, a stack of contracts or a transcript of testimony, and get a summary or list of the threads found. Many law firms also use TAR (Technology-Assisted Review) class tools in litigation. These are applications that learn from the lawyer’s tagging of documents (relevant/non-relevant) and then classify the rest of the collection themselves - this way, the lawyer has to look at only 5% of the material, for example, and the algorithm sifts out the rest. Finally, don’t forget about universal tools like Microsoft 365 with AI (recently, Microsoft is introducing GPT-based “Copilot” to its office suite, which can, among other things, summarize a Word document or indicate data in it). Bottom line: already, lawyers have a range of tools at their disposal - from specialized ones focused on one type of document, to general assistants capable of analyzing any text. The choice depends on a law firm’s needs and budget, but the trend is clear: whoever can, is reaching for AI to ease the burden of document analysis on their team.
Will junior lawyers lose their jobs by automating document review?
This question often comes up in discussions - especially among junior lawyers, for whom document review is sometimes the first rung of their careers (so-called “grunt work”). Indeed, tasks such as digging through case files or sorting contracts by clauses have traditionally belonged to trainees and lawyers in the lowest ranks. The automation of this work may raise the concern that why would a law firm need an applicant when a computer can do a good portion of their work faster? In practice, however, we are not seeing mass layoffs of young lawyers due to AI. Rather, what is changing is the nature of their work and the competencies required. Instead of dealing with monotonous reviews for weeks at a time, an applicant can supervise the work of the AI tool, review the results and immediately move on to more substantive tasks - such as legal analysis of detected problems, working on case strategy or preparing recommendations for the client. This can often be more developmental for them than photocopying documents or pasting data into a table. In addition, the importance of being able to use LegalTech tools is growing - young lawyers, brought up in the digital world, often become “masters” in using AI, which makes them very valuable to law firms. In a sense, AI can even make it easier to enter the profession - because it allows them to understand and grasp the entirety of a case faster (e.g., thanks to automatically generated summaries) and focus on what is legally relevant. Of course, the reduced need for manual work may change the staffing structure over time - perhaps law firms will need slightly fewer juniors for simple tasks, and instead more mid-level people who combine legal knowledge with technological proficiency. However, the overall number of lawyers is unlikely to decrease; they will simply all move a little higher up the value chain. Finally, remember that client relations, negotiations, court appearances, creative argumentation - all of these remain the domain of human beings. Young lawyers will still need to acquire these skills. AI, on the other hand, can take them out of the work that used to take a lot of time, without teaching them much about the law itself.
How does the use of AI in document analysis affect costs for the customer?
For clients, this is usually great news. Traditionally, lawyers’ time = client’s money, because many legal services are billed at hourly rates. When document review took hundreds of hours, the client had to pay for those hours (or the law firm had to give up something to reduce the bill). If, thanks to AI, the same analysis takes ten times less time, there is room to reduce costs for the client, or at least stop them from rising. Already, 71% of law firm clients say they would prefer to pay a flat rate for the entire case rather than by the hour - and the use of AI makes it easier for law firms to adopt such models (because they can better control the workload through automation). So in practice, AI can lead to more predictable and attractive pricing for legal services. What’s more, speed is sometimes crucial for certain assignments - for example, in tenders or negotiations, it counts to identify risks in a contract instantly. An AI-supported law firm is able to provide a client with an analysis almost “on the spot,” which increases its business opportunities. A satisfied client is a loyal client, so while the law firm may work a little fewer hours (and potentially billable hours), in the long run it will gain a competitive advantage and attract more business. It is also worth noting that when lawyers are relieved of mechanical work, they can devote more time to the client - explain the conclusions of the analysis, advise on optimal solutions. The quality of service increases. In summary, AI makes legal services more cost-effective: the client pays less for routine work and receives more value in the form of the lawyer’s knowledge and advice. This is a shift from a “pay per hour to read documents” model to a “pay per problem solution” model.
LegalTech Revolution : Artificial Intelligence in the Service of Law Firms](https://nflo.pl/ebook-legaltech/)
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