AI Again: Part 3: Supercharge Document Generation

Welcome to Legal Marketing 101. I'm Toby Rosen and welcome back to Episode 3 in our AI Again series, where we're talking about ways you can actually integrate AI into your processes right now. In Episode 2, we talked about using AI for intake and specifically how to use AI on top of your existing intake flow by integrating it with Zapier. If you haven't listened to that episode yet, go back and listen to it now, because today's episode is going to be about extending what we've just done with our intake and building out some document generation. But before we dive in, we need to understand what document generation actually is and how it applies to us. So let's do that quickly.

In the context of legal practices, document generation refers to the automated creation of legal documents using preset templates and data inputs. You may have noticed a keyword there preset. Right now, document generation means leveraging automation to quickly and accurately produce a variety of essential documents such as contracts, wills, pleadings, legal forms, etc. And just by automating this process, law firms can significantly reduce the time and effort required to produce high-quality documents, simultaneously ensuring consistency and minimizing the risk of human error. But by strategically integrating AI into document generation, we can take automation to the next level. We make it smarter, more adaptive and even more efficient. So today we're going to be diving into how AI can supercharge your document generation, but, fair warning, we are not going to be covering all the potential options for this. Like I've said many times, there are a lot of tools that do this job well, from Filevine to Gavel to Formstack. There is no shortage of options for document generation, both legal-specific and more general, so we're going to try and focus in on the process today, without specifically talking about any particular tech stack, but you should be able to find some parallels in whatever you're currently using or whatever you're looking at. This isn't necessarily an advertisement for the tools I like to use or the three that I just mentioned.

Before I jump into the specifics, though, I just want to quickly remind you about the prerequisites for this. I did say you should go back and listen to episode two, but you need to know about this. Number one you need to have a digital intake process. You already need to have this. This usually means forms on your website, automated follow-up emails and a CRM to manage this data, and since we're not going to be diving into the automation part of document generation today, you should have a cursory understanding of how automation and how document generation actually works. There's often more that's attached to all of this, but this is going to be our absolute minimum for today. You need to have that level of understanding, and that's because document generation with AI is going to require some input and specifically it's going to require the client to input a bunch of data, whether this is from a form or from a recording of a phone call or a collection of emails that AI analyzes.

We need that input data to get started and once we have that input data, what we're going to do with it at least right now, tends to fall into one of two categories analysis or extraction. And first we're going to talk about extraction because it's cooler and there's a cool Netflix movie about it. Of course, if your client is submitting all of their information through a form, you've built an active campaign on your website, or they're going into another email system MailChimp, whatever it is you're probably not going to need much extraction. You're already going to have a preset value for those forms and those are just going to pass automatically through your system to the correct place and then they'll be pulled into your document generation process when that's triggered. But in many scenarios we're receiving documents from a client that could be PDFs or Word docs of forms, or a folder full of letters or a bunch of emails or screenshots of text messages, whatever it is, and we need that information digitized and stored somewhere so we can generate our own documents based on that information. And that's where AI comes in to help us OCR any documents that need it and then extract the appropriate information from the documents.

Now, tools like Filevine have this integrated as part of their suite of default tools, so it's baked into your process from the get-go. But there are still a myriad of options to build this on your own. We talked about Zapier last time, and while it can be a little bit fiddly, it still is pretty straightforward to catch a document submission in Google Drive using a Zapier last time, and while it can be a little bit fiddly, it still is pretty straightforward to catch a document submission in Google Drive using a Zapier trigger, pull it into your OCR software, feed the resulting text over to an AI engine like OpenAI, process the information and then send it back to your CRM or document generation platform. But what I'm skipping here is some detail on the configuration of the AI engine, and that's because it's going to be highly unique to your use case. In Filevine's case, they've built a specific tool to parse input information and populate it in USCIS forms for immigration processes For a custom implementation.

Our focus and the tricky part of the equation is going to end up being on the instructions we give the AI for interacting with our data. In a family law or bankruptcy scenario, we might need lots of detail on assets owned by the parties, and the AI needs to make sure to keep that information organized together in a specific format. Your scenario is going to have its own specific needs and what the AI extracts is entirely dependent on what your case is going to require. But once we've extracted the information we need or not, if we're getting our information from forms we then move on to analysis, which is typically going to work hand-in-hand with extraction. We've seen plenty of examples of how AI crushes predictive analysis and turns through m mountains of data to find trends and correlations from puzzling sets of information. But here we're looking for it to help us advance the process and analyze a few key bits of information.

Of course, this will again depend on your practice area, but there are a few key things that AI can do really well to help speed up document generation. First, we can capitalize on that most powerful capability of AI by using it to automatically detect inconsistencies and anomalies in documents. Imagine you're working on a complex bankruptcy case with multiple documents spanning hundreds of pages. Manually checking for inconsistencies like mismatched dates or conflicting financial figures is going to be incredibly time-consuming for you or, more likely, for your intern. Ai can streamline this process by using its advanced algorithms to scan through the text and highlight those discrepancies immediately. It can flag errors like an incorrect creditor amount or a missing signature, ensuring that nothing slips through the cracks. This not only saves time, but it also reduces the risk of costly mistakes that could affect the outcome of a case.

But on top of that, ai's natural language processing isn't done. We can use it to parse through legal documents and identify key entities such as names, dates, addresses, financial amounts and even legal terms. For instance, when reviewing a bankruptcy filing again, ai can quickly pinpoint all mentions of creditors, debtors, assets and liabilities. This information can then be categorized into our predefined categories, making it much easier to access and analyze. By organizing our data this way, it helps us quickly find relevant information, it streamlines our workflows and over time, it improves our overall efficiency in so many different little places.

And if that wasn't enough, ai can also help us tackle cross-referencing. At this stage, it can easily automatically cross-reference data points within and between documents. So let's say you have a client's financial statement and their bankruptcy petition. Ai can compare these documents, verify that the reported assets and the liabilities match up and, if any discrepancies are found, ai will flag them for further review before you've even looked at the documents. This automated cross-referencing ensures that all documents are aligned and accurate and provides us with the cleaned up data we need to start to generate new documents.

But before we get to that final step, ai can also help us with standardization. This is something I think a lot of people forget, and this is really important if we're generating a significant number of documents. When we're drafting a new document, ai can pull relevant information from client intake forms and existing case files and then populate the template more accurately and efficiently than a human ever could. And AI can also ensure that the language used complies with the legal standards and the firm-specific guidelines, either by using training data that you feed to your model or just by giving it good prompts. And then, finally, we're arriving back at document generation.

Now, we're not going to dive too deep on that because it's a whole other thing and we just we needed a whole other episode for it. But do stay tuned because we are going to be digging into automation a lot more soon. It really connects well with AI and there's a lot of cool stuff we can do. So we're definitely not done with this. We've got one more episode in our AI series and we're going to be talking a little bit about what's coming next in this universe and how you can prepare yourself. But if you have questions about AI and getting it more integrated into your practice today, please don't hesitate to reach out. For today that's it for Legal Marketing 101. Check out RosenAdvertisingcom for more. Thanks.