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Knowledge base Updated: February 5, 2026

AI writes contracts. Who will ensure that the process is safe and efficient?

Generating repetitive documents, such as NDAs or company agreements , is an ideal task for AI. It saves dozens of hours . But for this system to run smoothly, it needs a robust and secure infrastructure.

Marcin Godula Marcin Godula

Yes, artificial intelligence can prepare a draft of a contract or lawsuit. Generative models can reproduce standard structure and clauses based on simple guidelines, drastically reducing the time to prepare repetitive documents like NDAs or simple contracts .

However, before a law firm implements this system, it must run on a stable and secure platform. Our Package 2: Cloud & Automation Factory is the foundation for the efficient operation of these tools, and Package 1: Managed Security ensures that client data entered into contract generators will not leak.

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Can artificial intelligence write a contract or lawsuit on its own?

I’ll be tempted to make a bold claim: yes, AI can prepare a draft of a contract or lawsuit, but the key word is “draft.” Current generative systems are capable of generating legal text on a given topic - for example, an apartment lease agreement - and in a pretty decent form. They can do this by learning from the hundreds of thousands of model contracts they have seen in training. But they don’t know our specific situation or the customer’s preferences until we tell them. So if we say to the AI, “Write a commercial lease agreement, rent of £5,000, lease for 2 years, payment in advance every month, no possibility of subletting,” we will probably get a fairly sensible document covering these points, because the AI can reproduce the standard structure and clauses. What’s more, it will remember things that a human might forget, such as provisions for a deposit or a transfer protocol, if they were often present in the training data. Likewise with a lawsuit - we will state the facts and legal basis, and AI will compile the demand, justification, articles, perhaps even add a reference to case law. Sounds great, but… well, that’s exactly what it always needs to be revised. AI has no guarantee of legal or procedural correctness. It may, for example, use an outdated legal basis (if the data is stale) or use general language where a specific formula is expected in a particular court. So while AI will “independently” generate the document, the role of the lawyer shifts from the writer to the editor and proofreader. This can be compared to a situation where a young lawyer writes a draft and an older lawyer revises it. The difference is that AI will do the draft in no time. In some simple matters - e.g., generating standard pleadings, subpoenas for payment, bylaws - AI can be so accurate that the correction is minimal. In more complicated ones - the draft will have to be thoroughly refined. Nevertheless, the very fact that a machine can create a legal document of several pages in a minute is already revolutionary. In practice: yes, we can, in a sense, say that AI “wrote the contract,” but for it to become an actual, binding document, it must pass through the hands of a lawyer. This symbiosis is best reflected in the phrase that AI in law is a “centaur” - a combination of computer power and human intuition. The computer itself generates the text, but the human gives it the final, correct shape.

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AI generates documents using language patterns learned from huge collections of texts. The GPT-type model doesn’t have pre-written contracts or lawsuits as such in its head, but it knows the probability of words and phrases occurring in a specific context. For example, if we start a contract with “Lease agreement,” the model “knows” that typically further down the line the parties to the contract, definitions, subject of the lease, etc., will appear, because that’s what the statistics of the patterns it has seen indicate. In other words, AI generates the text word by word, each time predicting what should come next so that the whole thing makes sense and form. For legal documents - which are quite schematic - this works surprisingly well. In addition to general-purpose models, there are specialized tools. Some work on the principle of filling in intelligent templates. Such a system has a template document prepared with “holes” and asks the user questions (e.g. “provide the landlord’s data,” “do you allow subletting?”, “what is the rent payment date?”). Based on these answers, AI fills in the gaps and adjusts the finished text. This is a slightly different approach - more document automation than creative writing - but it often works because it combines a rich database of patterns with the cleverness of AI, which can, for example, help formulate an unusual contractual condition. There are also hybrid systems: AI generates the text and then automatically checks it for compliance (e.g., whether it contains all the mandatory elements of a lawsuit). An interesting aspect is that generative AI can adjust the style - if we say we want a letter “without legal jargon, accessible to the layman,” it will try to simplify the language, and if we want “very formal, with reference to paragraphs,” it will do so accordingly. Technically, then, AI has some abstract model of legal language and can create new sentences in it that “fit” the context, even though they are not copied from any single contract in the data. Of course, when it generates legal bases or type clauses, it looks like a standard, because that’s what the statistics tell us. If we told AI to create something very innovative, it might have a problem with it - such as a unique contract structure. But that’s still what a lawyer is for, to determine what unique should appear. In short: AI generates documents by learning from thousands of lawyers (through the texts they have written), and can now mimic their style and logic when creating new documents.

How do lawyers use AI in drafting contracts?

Increasingly hybrid - that is, some of the content is generated or prompted by AI, and some is created traditionally. The simplest scenario: a lawyer needs a clause for an unusual circumstance (e.g., a clause regarding the use of artificial intelligence by the parties to a contract). He can ask AI: “Generate a clause for the license agreement that governs the rights to the results generated by artificial intelligence during the execution of the contract.” AI will suggest the wording of such a clause. The lawyer will correct it to his knowledge and insert it into the contract. This saves time, because instead of inventing from scratch or searching the Internet for a similar clause, he got a ready-made one to adjust. Another way to use AI is to check contracts: lawyers, after writing a contract, have AI analyze it and point out potential problems or missing elements. AI acts as an assistant-editor: “Note, the contract does not specify the jurisdiction of jurisdiction in case of a dispute,” or “The definition of ‘Party’ is used, but any reference to it is missing from the body.” This gives the lawyer a list of amendments to consider. In addition, at some law firms, lawyers start their work with an AI-created template: for example, they generate the entire master supply agreement based on general assumptions, and then edit it within the team. This can sometimes be faster than manually reworking an old contract into a new one (where outdated sections are often left behind by oversight). AI can also be asked to optimize language: “simplify overly complicated sentences” or “remove repetitions.” This makes the final text more readable. Finally, some clever use is to generate checklists and summaries. A lawyer can generate a short summary of the contract in bullet points to send to the client, or a list of all the parties’ obligations extracted from the draft - this can be done by AI, which “reads” the contract and bullets out the important issues (e.g. payments, obligations, deadlines). In summary, lawyers use AI as a tool to assist in writing and editing. They don’t give it full control over the content, but are happy to entrust various stages of the document creation process to it: from the first draft to final proofreading. As a result, work goes faster and documents can even be more polished.

Do the documents created by AI meet the formal requirements?

Surprisingly often - yes, at least these basic requirements. AI “knows” that a contract should have a title, definition of parties, definitions, subject matter, etc., because it has seen thousands of contracts. So a generated contract will usually contain these elements. Likewise, an AI-generated pleading will probably start with the heading “To the Court…”, will have a designation of the parties, what the plaintiff is asking for, factual and legal reasons, and a signature at the end (although AI will not physically affix it). However, the devil is in the details. AI may not be familiar with local specific requirements. For example, in Poland, a lawsuit must include the plaintiff’s PESEL in property rights cases - if the AI model has not been taught the Polish CCP, it may not include this. Or it may incorrectly name the court or department. These are small things that a lawyer needs to correct. In contracts, formality is less of a problem (here it is the will of the parties that counts, there is no imposed official model), so AI is more likely to meet the “decent practice” standard. In pleadings, one has to be careful - those generated by AI may contain non-existent judgment signatures (a special case of hallucination). Imagine that AI writes in the justification of a lawsuit: “The Supreme Court, in its judgment of X ref. Y, ruled that…”. If the lawyer does not check this, it may turn out that there was never such a judgment. Unfortunately, there have been cases of lawyers who uncritically trusted AI and submitted a letter with fictitious rulings - which came to light and ended up in disciplinary trouble. So meeting the formal requirements is possible, but verification is a must. It is good practice to treat the document from AI as a rough draft and go through the checklist of formal requirements as if you were writing it yourself. The introduction of AI does not absolve responsibility - it is still the lawyer who signs the document and is responsible for its content. In time, there will probably be specialized legal models that will take into account all the formalities (e.g., build in the rules of the CCP or the PAC) - then trust will increase. There are already document automation solutions that generate documents based on programmed rules, and there the margin of formal error is negligible. In generative AI, the margin of error exists, but an experienced lawyer will quickly catch it and correct it. Bottom line: AI largely meets the formal requirements because it mimics existing patterns, but the final cut still belongs to the lawyer to make sure nothing is missing and everything is correct.

How can AI help tailor document templates to a specific case?

Traditionally, template customization involved manually replacing so-called placeholders (e.g., [First Name Last Name], [Date], etc.) with the correct data and adding/removing clauses as needed. AI can make this process more intelligent and less mechanical. First of all, it can understand the context of the case and suggest modifications to the template based on that. Let’s say we have a template contract for the sale of shares, but our transaction has an unusual condition - such as spreading the payment into installments and making the last installment dependent on the company’s financial performance. AI, knowing such information, can automatically insert the appropriate provisions governing the conditional payment mechanism (earn-out). A template adapted manually would require a lot of writing, and AI will do it because it “knows” what earn-out clauses usually look like. Second, AI can remove unnecessary sections. If, for example, you click in the form that the agreement does not provide for subletting, AI will generate a version of the agreement already without the subletting clause, instead it can add wording like “The tenant is not entitled to sublet the leased object.” That is, it intelligently adapts the text. There are also AI tools that analyze the entered data and warn if something is inconsistent with the template. Example: in the template we have a choice of applicable law (Polish or other), we chose Polish, but at some point in the contract a reference to a paragraph of the German BGB remained (by oversight). AI can catch this and suggest a correction to the Polish KC. Finally, AI can dynamically create a new template based on different sources - for example, we take provisions from three different contracts and ask AI to integrate them into one document, eliminating duplicates and contradictions. This is something that manually is quite tedious, and AI will do faster. Figuratively speaking, instead of customizing a template for a case, we can provide AI with information about the case and it will “build” the document by itself from the best-fitting building blocks. In some fields, dedicated assistants are emerging - for example, in the startup industry, where investment contracts are often negotiated under NVCA standards, there are tools that allow you to answer a series of questions (how many rounds of investment, what liquidation preference, etc.) and then generate a customized contract. AI brings flexibility here - if you answer “I don’t know” to a question, it can offer options and explain the consequences. Bottom line: AI improves customizing templates by intelligently filling in, adding/adding clauses as needed, and keeping an eye on consistency. As a result, the lawyer wastes less time mechanically adjusting the document and can think more about the substantive aspects.

Does AI speed up the creation of repetitive documents (e.g., NDAs, company agreements)?

Definitely yes - in the case of repetitive, standard documents, the time savings are probably the most noticeable. Take, for example, the NDA (non-disclosure agreement), or confidentiality agreement. Law firms often have their NDA template, but each time they have to modify it a bit for specific parties, scope of information, etc. AI can be used in a simple way: the lawyer enters the key data (parties, definition of confidential information, duration of the agreement) and asks AI to generate the NDA. In a dozen seconds, he receives the finished document. If several such NDAs were prepared every week, it’s easy to count how many hours will be saved per month. Similarly with company agreements or resolutions - they too are heavily schematic (especially, for example, the limited liability company agreement). Enter AI: “Shareholders: Jan Kowalski 60% shares, Ewa Nowak 40%, capital PLN 50,000, two-person board, no RN” - and we get the skeleton of a company agreement with all the required paragraphs. You may have to add some special provisions, but that’s a minor part of the job. According to some data, the automation of documents (even the traditional one, without AI) was able to reduce the time of drafting by several tens of percent. With AI, that percentage is even higher, because there is less need to think about the form itself. Some law firms boast that they have reduced the preparation of a typical contract from 3 hours to 30 minutes by using AI-assistant tools. Importantly, AI can also generate multiple versions at once. For example, if we need the same contract in Polish and English, AI can generate a bilingual document or two documents in separate languages almost simultaneously, which previously required translation. In addition, repetitive documents often need to be individualized in only a few places - AI allows this to be done in one pass, minimizing human errors (of the type you forgot to change one name in 10 places - AI will change it in all of them). Available statistics say, for example, that 95% of people who have integrated AI into their processes report saving time on administrative or document tasks every week . This suggests that almost everyone who tries to automate such routine letters experiences real time relief. It’s worth adding that lawyers sometimes use AI to review repetitive contracts from the other side instead of generating from scratch - e.g., several NDAs come to the company every day for signature from different contractors. AI can quickly compare them with its own template and point out differences. This again speeds up the work of the lawyer, who only has to check if there is something relevant among the differences, instead of reading the entirety of each contract. In general, the more repetitive and standardized the document, the more suitable it is for AI support - and there are quite a lot of such documents in law firm practice (leases, simple service contracts, bylaws, corporate resolutions, etc.).

How much time do law firms save by automating documents?

This can be difficult to put into numbers, as it depends on the type of document and its complexity. But industry reports and case studies provide some indicators. According to one study, the use of automation and AI in document creation can reduce lawyers’ time working on routine documents by as much as 20-30%. This means that if a team previously spent, for example, 100 hours a month drafting standard letters and contracts, it could save 20-30 hours - that is, almost four full working days to be used for other tasks! There are also more telling examples: a company that introduced a sales contract generator reported that the preparation of a set of transaction documents (master agreement plus attachments) was reduced from 2 weeks to 3-4 days, because many sections completed themselves. Thomson Reuters, in its survey, indicated that lawyers see AI as a way to save about 5 hours a week - some of that is certainly document tasks. On an annual basis, that’s hundreds of hours that could be used to handle additional clients or for strategic work. For large law firms that produce hundreds of documents per month, the scale of savings is even greater. Importantly, the time saved is not only a gain for the law firm (because it can handle more cases and issue more invoices or reduce costs), but also less fatigue for the team. Younger lawyers spend less time on tedious filing of templates and more time on the more interesting aspects of work - which translates into job satisfaction. Some point out that thanks to automation and AI, the case completion cycle shortens - for example, a client gets the first version of a contract on the same day they ordered the work, instead of in a few days. This builds a competitive advantage and strengthens the relationship with the client (because they are impressed by the speed and responsiveness). From the point of view of law firm partners, document automation is also an improvement in profitability - less time spent on non-billable tasks (because, for example, internal documents generate themselves) and more time spent on work that the client pays for. There is a reason why many law firms choose to invest in such tools, even though they initially require the implementation of templates or integrations - the return on investment is quick, because time is literally money in this business. Bottom line: the time savings are clear and measurable, on the order of tens of percent for repetitive documents, resulting in hundreds of hours of relief per year.

Does using AI in document creation reduce errors?

Yes, in many cases automation reduces errors, although generative AI can also introduce new types of errors (such as hallucinations). Let’s start with those positives: When we use structured document automation tools or AI as an intelligent editor, we eliminate common human mistakes. For example, auto-substitution of data throughout the document ensures that it won’t happen that we typed “John Smith” in the header, and somewhere further in the text by mistake there is still “Adam Smith” from the previous formula. The computer will consistently fill in all fields and spaces. AI also validates consistency often - for example, whether the dates make sense (it does not appear that something will occur “April 31” - because there is no such date, and AI knows this). In complex documents, it can catch things that humans easily miss, such as the wrong paragraph number in a reference (how many times do we manually edit a contract to add a new paragraph and fail to update the reference to it somewhere else; AI can be taught to keep an eye on such references). One survey found that lawyers agree - reducing errors is one of the main benefits of technology: 35% indicate that AI will help reduce the risk of human mistakes . This is felt especially in large documents and where a minor inaccuracy can have a big impact (e.g., an error in an account number in a contract, a typo in a client’s name in a lawsuit). Of course, generative AI also has its humor - it can, for example, introduce false information if it “thinks” it is likely (e.g., insert a clause into a contract that seems standard, but the customer doesn’t want it). However, these errors are easier to catch, because they are substantive and the lawyer is more likely to notice them during the review (e.g., why here is a provision on mediation that we did not agree on - then he removes it). More troublesome errors are the unintentional, minor ones - and here AI performs brilliantly, because it works precisely and systematically. Let’s remember that even the best lawyer can have a worse day and miss a detail; a computer - if programmed correctly - will not make that mistake. This is why AI is increasingly being used for final proofreading of documents: there is a plug-in for Word that reads our document and says: “I found 3 potential problems”. It’s a bit of an analogy to built-in spelling dictionaries - no one is likely to submit a text for printing today without checking it with “red underlines.” Similarly, in a few years it will be standard that an important contract goes through a quick AI scan for silly errors before signing. Anyway, simple AI is already there: e.g., systems that detect terminology inconsistencies, or the lack of definition of a term with a capital letter (once we used the word “Agreement” with a capital letter, we should define it - AI will point it out). To sum up: yes, AI reduces the number of errors, especially technical ones and those resulting from human oversight. One still has to be careful of errors on AI’s part, of course, but as long as lawyers verify the result, the gains (fewer mistakes) outweigh the potential new errors.

The market for LegalTech tools is rich, but a few names come up frequently in the context of document automation and the use of AI:

  • HotDocs, ContractExpress - are among the pioneers of document automation (although without AI strictly speaking). They allow you to create smart templates with forms. Large law firms have been using them for years to mass generate, for example, loan agreements. Now many of these systems integrate AI elements, such as recognizing data from existing documents to populate a template.

  • LawGeex, LegalZoom, Rocket Lawyer - these platforms focus on providing users with ready-made documents after completing a survey. LegalZoom and Rocket Lawyer (US) are aimed at small businesses and consumers, and use a mix of automation and human review. LawGeex, on the other hand, is AI for contract review and negotiation, but can also generate its own clause suggestions. Their report showed that AI can achieve high accuracy in detecting problematic clauses - even higher than a human in one high-profile NDA test (94% vs. 85% efficiency).

  • Avokaado, Legito, DocsJar - these are a newer generation of tools, often with a web interface, allowing law firms to create their own “library” of smart templates. They include a bit of AI, such as prompting clauses depending on the user’s response.

  • Microsoft 365 Copilot - in the near future (or already as you read this) Word users will get an AI assistant built into the word processor. It will be able to, for example, generate a draft of a contract based on a few points at our command, summarize a long document, or convert an outline into a full text. Since Word is a lawyer’s primary tool, such integration could become very popular (because you don’t have to buy anything new - it’s part of Word).

  • Spellbook - is a tool in the form of a Word plug-in, just for lawyers. It uses GPT-3/GPT-4 to suggest changes in contracts, analyzes the text of the contract and generates, for example, explanations of individual clauses or suggests additional provisions. Very useful for negotiations - you can, for example, highlight a section of the contract from the other side and ask Spellbook to assess the risk of that provision.

  • Harvey AI - once again, because this versatile assistant also helps with documents. You can say to it, “prepare the first draft of the share purchase agreement on terms A, B, C,” and you will get an outline of such a document. Allen & Overy has tested it extensively with various documents.

  • DocuSign CLM with AI Insight - DocuSign is associated with electronic signatures, but it also has a CLM (Contract Lifecycle Management) tool with built-in AI for document analysis and generation. For example, you can drop a contract into it and ask: “Create a contract based on this draft, but on our company template”. - and it will do it.

  • OpenAI Codex / ChatGPT - developers build custom solutions based on GPT APIs that are tailored to a law firm’s needs. That is, for example, a custom chatbot that uses a company’s database of clauses and generates documents according to its style.

From domestic solutions in Poland, we are seeing the development of, for example, IntelliDocs or automation modules in law firm management systems (like Vindicat, when it comes to debt collection). Some large law firms have in-house teams that build tools for a specific practice - for example, a generator of lawsuits with interest and attachments, which a legal assistant prepares with one click.

In general, there are quite a few tools, from very simple (Word scripts) to complex platforms. Those that combine ease of use with intelligence are gaining popularity - lawyers don’t want to spend hours learning new software, the tool is supposed to be intuitive. That’s why Word integrations are so cool - work is done in a familiar environment.

Can AI fill in the missing passages or propose alternative clauses?

Yes, and this is one of the more “intelligent” functions, because it requires understanding the context of the document. If, during negotiations, the client says: “I could use a clause about liquidated damages here, because I don’t see it in this contract,” the lawyer can use AI to quickly fill in the missing section. The tool will analyze the contract, find the right place (e.g., in the “Responsibility of the Parties” section) and generate a liquidated damages clause that matches the rest stylistically. Of course, the parameters of the penalty - their amount, triggering situations - it is already the lawyer who has to provide or later adjust, but AI will take care of the legal formula. Similarly, when a certain standard provision is not included - say, force majeure - AI will insert a nice paragraph about force majeure. What about alternative clauses? There are times when the parties differ on the wording of a provision and a compromise has to be proposed. AI can be helpful as an inspiration. One can ask: “Propose an alternative version of the liability clause, more lenient for the supplier,” and we will get a capped version, such as a limitation of liability in amount or an exclusion of lost benefits. The lawyer will assess whether this meets the intent of the parties. Sometimes AI will suggest a solution that a lawyer wouldn’t have thought of - because, for example, he or she has previewed it in some foreign practice. It is also important that AI can fill in logical gaps. If we use a definition in a document, but forgot to define it, AI will notice this and can suggest a definition. Or if the agreement refers to an appendix, and the appendix is missing, it will pay attention. In this way, we prevent situations where something is incomplete in the final text. At the stage of drafting pleadings, AI is sometimes used to add arguments: for example, if a lawyer feels that one more paragraph is missing from the justification of a lawsuit, she can ask AI for a suggestion. Sometimes she gets an interesting argument (e.g., a reference to EU jurisprudence), which can then be used, although of course its validity must be checked. In general, AI acts here a bit like a second pair of eyes and a creative mind. It is not infallible - proposed clauses need to be adjusted - but it speeds up the process. In contract negotiations, the rapid generation of alternatives is sometimes salutary: instead of postponing a meeting until tomorrow because “I have to think and write a new version of the clause,” a lawyer can generate 2-3 alternatives in minutes and discuss them immediately with the other party. This makes the process more flexible and interactive. Bottom line: yes, AI fills in the missing pieces (makes sure nothing is missing) and proposes alternatives (giving us creative ideas or compromise clause wording). It’s a bit like having an experienced editor say: “How about you write it like this?”.

How to maintain quality control with AI-generated documents?

This is a very important question, because at the end of the day it is the law firm that is accountable to the client (and itself) for the quality of the document. Quality control in the AI era requires some new habits and procedures. First, always read and review the AI-generated document carefully, as if someone else on the team had prepared it. This means not shortcutting the review process - on the contrary, perhaps paying more attention to it at first, until you gain confidence and experience with the tool. Second, it’s a good idea to use a two-step approach: first, the AI generates the document, and then another AI module (or another tool) reviews it. For example, we generate a contract, and then another program asks the question, “Are there any typical clauses missing or inconsistencies in this contract?” Such a double AI-check will sometimes catch something that escaped the first generation. Third, a red light should go on for any information that the lawyer himself did not know beforehand. If AI quoted a sentence - check the sentence. If it mentioned a regulation, make sure it applies. This is somewhat analogous to how lawyers use assistants or trainees: trust, but check. Versioning is also a good practice - we keep a version of the document before and after AI edits. If anything happens, you can go back to the previous one or see what specifically AI has changed. Certain law firms have formal policies in place for using AI: for example, prohibiting pasting sensitive data into cloud tools (for confidentiality reasons), or requiring approval from the lead lawyer before sending to a client. Sometimes clients are also told openly: “we use the support of AI tools, which reduces costs, but every document is finally reviewed by an experienced lawyer.” Transparency builds trust, while emphasizing that there is no witchcraft - a live human being is still in charge. Finally, quality control is also about training the team - employees need to be taught how to use AI effectively (prompt well, verify results). According to Clio’s Legal Trends Report 2024, although 79% of lawyers use AI, only 8% do so “universally” across the firm , suggesting that the rest are cautiously testing. Gradual implementation and sharing of experiences within the team helps catch common AI mistakes and learn to prevent them. Bottom line: maintaining quality control comes down to not relying blindly on AI. We treat it as a helper, but we don’t exempt ourselves from vigilance. We check, test, verify - exactly as an experienced partner would do when a document is prepared by a junior lawyer. In this way, we reap the benefits of AI while minimizing risks.

LegalTech Revolution : Artificial Intelligence in the Service of Law Firms](https://nflo.pl/ebook-legaltech/)

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