Code & Counsel

The Rise of AI in Legal decision - making ( Part 1 )

• Chetan N • Episode 5

Share episode

Send us a text

Chect it out our Youtube channel - https://youtu.be/cGno5m239ls

Can AI truly revolutionize the centuries-old legal industry? Join us as we uncover the transformative impact of AI technologies on law firms and enterprises. From small practices seeking cost efficiency to large firms pioneering Generative AI, we promise to reveal how AI is reshaping legal operations and client services. Discover how AI-driven tools like contractual analysis, legal research, and predictive analytics are democratizing access to high-quality legal advice, ensuring precision and accessibility for all.

Explore the cutting-edge advancements in standardizing legal documents and evolving legal research with us. We delve into the stark contrasts between streamlined processes in countries like the US and the manual complexities faced in India. Learn how AI is not only preserving institutional memory within firms but also significantly reducing the time required to understand intricate legal frameworks, providing immediate and accurate insights. 

Finally, dive deep into the powerful world of advanced Language Learning Models (LLMs) and their role in legal research and outcome-based analysis. With sophisticated statistical capabilities, these AI tools offer nuanced and highly accurate insights, turning them into indispensable assets for legal professionals. Whether you're a seasoned attorney or a curious listener, this episode promises to equip you with a comprehensive understanding of how AI is revolutionizing the legal landscape.

For more insights and discussions on the intersection of technology, law, and business, subscribe to our podcast and stay updated. Connect with us on social media for live updates, behind-the-scenes content, and more. Thank you for listening, and don’t forget to share your thoughts and questions in the comments or reach out to us directly!

Mail us at hello@quoqo.com or visit our website at www.quoqo.com.

Chetan:

Hey Guru, hi CC, hi, welcome back to another round of podcasts, and you know we've had a fairly successful series till now. We've had a number of esteemed guests come in give their views, but you've been asking me several questions and you wanted to sort of go in depth on some topics as well, right so?

Guru:

over to you. Yeah, thanks a lot. So hello hi, viewers. I'm hoping that you are enjoying our series. Uh, the coding console has been around for a few weeks now and, uh, there have been a lot of viewership lately. Uh, I want to take an occasion, this particular occasion, I want to to take an occasion, this particular occasion, I want to uh, you know, pick the brains of our esteemed lawyer, because our guru is now pulling my leg on these things.

Guru:

But yeah, no, no, I want. So you've been uh around in the legal field for a long time. I want to take your opinion on some of the things that we are doing on ai how it can change the world for people who are actually in the industries, for the enterprises and all those things.

Chetan:

So we were really thinking what should be the title for this podcast, and this podcast is titled the Rise of AI in Legal Decision Making which actually sets the trend for today's discussion because increasingly, even in our day-to-day work at Poco or otherwise, and also some of the clients that we deal with, ai has taken a pivotal role in decision-making, and we've seen this day in and day out. And even our routine work, as we do, often has a bearing on how we use AI.

Chetan:

and now what's happened is it's become so rapid even in our workflows, internal workflows and so on that it's become part and parcel of how we do work, and it's certainly been helpful in a number of ways, and we will go through this during the podcast as well, so go to you.

Guru:

Guru. Okay, so let me start by asking you a question. Can you start by giving an overview of how the AI is currently being integrated into the legal industry.

Chetan:

I mean, if you look at how AI is being integrated into the legal industry, correct? And if you talk of legal industry, there are essentially two main buckets. One is the law firms and the other are enterprises. You know these are still, I would say, at an early stage of you know, utilizing AI in day-to-day tasks and things like that. Let's look at law firms. There are, once again, many categories within these law firms. There are some forerunners which have integrated Gen AI technologies and have, in fact, there are some law firms that also market some of these technologies that are in partnership with other companies as well. There are law firms that also market some of these technologies that are in partnership with other companies as well. There are law firms that have developed their own data sets. They have relied on their own data and it's gone through a process of sort of leaning and also fine tuning and you can have these data sets and you can build out a few applications, things like these.

Chetan:

That's another case. Other law firms have, and these are primarily limited to very large law firms which have the resources to do something like these. But if you look at some of the smaller law firms and if you look at some of the smaller law firms and if you look at individual practices, which are also a backbone to communities across the globe, they are quite intrigued and they want to know what kind of solutions are available to them. And from an individual practitioner's perspective, or from the perspective of a small business, legal business owner, the issue becomes can I save costs, can I cut down the time that it takes for me to go back to my clients? So these are some of the things that they are primarily looking at. In the larger scheme of things, it becomes a question of access to specialized data, which some of the larger law firms provide.

Chetan:

That's how it's turning out In enterprises. It's a totally different volume. As you know, every part of enterprise is being revolutionized because of GenAI and also going forward when you have general AI coming on board as well, which is anticipated soon and is being recorded in July of 2024. So you can anticipate in the next six months to a year that there'll be another pivotal change coming. You know through all of these things as well, but if you look at an enterprise, you know whether it is to increase efficiency, you know to do more business, to better provide support to customers, to better provide legal support to both existing and external customers, to streamline document data, to introduce security measures such that compliance needs are met across markets, and these are all the things that are being changed radically. Now imagine if you are now again.

Chetan:

There will be two cases here which I want to discuss. One is on a let's take a multinational enterprise which is operating in many countries and typically, if you look at legal departments in such multinational enterprises and these are both local plus you'll usually have a hub. So they operate on a hub-and-spoke model, with individual lawyers in critical countries or individually built teams and so on and so forth. But the challenges are becoming global If you really look at it, whether it's a regulatory challenge or whether it's a competition block challenge or if it is a tax challenge. Now how do you ensure that people let us say you are operating in a mall and you want to sort of have the same response to a tax query as that may be given by headquarters, such that there is both sanctity to a task query as that may be given by headquarters, correct, such that there is both sanctity and there is also the same you know nothing is being misconstrued or miscommunicated to a local authority.

Chetan:

This is becoming a challenge, but you know, with the advert of JNI you can streamline all these challenges very easily, so you can have at the touch of a button, you can also have predictive analytics to understand how things may actually pan out, whether it's on regulatory issues or whether it's on litigation, litigation, things like these. So that's where, from a multinational enterprise, there are many, many use cases for all of us. But let's take a small business, on the other hand, and for a small business, time is money and you need access to legal services as an example, and these tend to be expensive and you need access to legal services, as an example, and these tend to be expensive and for a small business.

Chetan:

If you look at compliance requirements, say, in India, even a small business, as an example, will end up spending, at least as a private limited company, at least 8 to 10 lakh rupees in compliance each year. That's a lot of money for a small business, but to be able to even achieve that kind of compliance, there is a lot more money to be spent. So you are looking at upfront costs of, say, a private limited company or an LLP in the range of, say, 15 to 20 lakhs a year just to keep it running. And there are a lot of things that we can streamline there. You can standardize documents, you can use standardized contracts, you can collaborate well. You don't have to necessarily go through a lawyer to use standardized contracts.

Chetan:

We love digital signing very easily and you know if you receive contracts from third parties. You don't have to ping somebody to get it. You can quickly review it and respond to it because you're a small business owner. So what I'm seeing is there's a lot of efficiency to be drawn within the lapse that is being spent today to run a small business across the globe. This is the same issue. India does be exacerbated because you don't necessarily always have access to professional services and the professional services are not necessarily of quality nature, and this is true of many functions.

Guru:

And also they are not immediately available. There are time factors involved and for a're not immediately available, there's a time factor involved, etc.

Chetan:

And for a small business owner who wants to get back with his customer pretty much immediately Time is money.

Chetan:

They want to conclude all of these things as well.

Chetan:

Also, for a small business owner, gen AI tends to also give the power of a much larger company or the resources available for, or the notion that they have much more experienced resources than what is available, simply because of the nature of JNI.

Chetan:

So you can have, you can draft very sophisticated contracts at a fraction of the price, at a fraction of the time, or you can have very sophisticated risk reports at a fraction of the price, Things like these which, if you really think about it, is access to legal services, as it's meant to be and how things are moving. So, in both ends of the spectrum, especially when it comes to businesses, both on the enterprise side and think of a multinational enterprise which I began with and if you look at a law firm, both a solo premiers ship for a thousand person law firm. Now, those are, I would think that they are each of their sizes, given how things are going, you know, especially in the AI front. But you know these are some of the typical you know typical overview as to how it's being integrated. You know, I would. I mean, this is this is how I would think that this is an overview as to how things are being integrated both in the enterprise space as well as in the law firm space.

Guru:

Sorry, very long answer to your question, but uh you know, I just wanted to you know.

Chetan:

These are things that we've seen and you know. Continue to explore as as as time goes by and continue to explore as time goes by.

Guru:

So thanks a lot for the detailed answer. To summarize, so, you have answered about how AI is changing the sector across the various market segments and also with respect to the use cases. You said that there is contractual analysis applications, there is legal research, predictive analytics, chatbots all the things that you have talked about basically covers all these aspects, and you also talked about democratizing the legal, using AI for both the smaller organization as well as the bigger organizations. Basically, using AI is actually going to help them to use a similar level of quality of legal services or applications in both the companies. Thank you, so what are some of the immediate, you know, impact or effects that AI can bring in to the legal departments across the?

Chetan:

sector. Yeah, and this is something which increasingly people are also discovering saying what are the benefits that EI can bring to the legal sector, whether you're a law firm or whether you're a company or a business doing all these things right? I mean, if you really think about it immediately, what brings to mind is accuracy. Now, legal just as any other functional function. As an example, you have functions such as finance procurement. You have functions such as finance procurement. You'll have legal.

Chetan:

You know, typically these are all different cogs in a larger game. That's how we look at it from if you're running an enterprise. But if you're running, you know one of the main things whether you're a law firm or whether you're a consumer of legal services as a company is the accuracy in legal services, and that we know can. As practitioners, we know that can really really vary, and the more years that is spent in the profession, the more articulate and the more detailed an answer can get, and the more nuanced and the more detailed an answer can get and the more nuanced things can get. So there's always premium for more experienced analysis and things like that.

Chetan:

What Gen EI has done is really bridge the divide and, yes, there is always a market for critical what Gen AI has done is really bridge the divide and yes, there is always a market for critical legal advice, legal research, and there are some. It may not be possible to cover all of these nuances seeing Gen AI, unless you know what to ask Gen AI for. But one of the things that Gen AI has done is bring in a lot of accuracy, correct, and it also, depending on how you view it and how it is programmed, it can either be very balanced or it can take the view of someone, basically review it from a certain perspective as well, just as you would do in real life. Let's say you are entering into a lease agreement for premises. You would want a lease agreement to be either balanced or slightly in your favor. The same thing is going to be happening depending if you're a lessor. If you're a lessee, then you want to make sure that you know that no additional obligations are passed on you. But if you go to a lawyer today, you will not get the same answers from two different professionals, depending on the complexity of the document. Now imagine this is a backing document or a project finance document. It's incredible. These are very complex documents. There are even IPO documents, for that matter. Nobody I don't think anyone in the country except SEBI actually goes through those 700 page forms. It has so many disclosures, but how is it even relevant to someone who is investing in the market? So there are many, many use cases like this.

Chetan:

What GNI has done is make it very accurate, at least from a review perspective, so that you, as a consumer of legal services, have access to a fair degree of accuracy in at least what it is doing. Also, it takes an unbiased opinion. Why I mention this is especially in law firm circles, and you know, the more you practice and the more know you will, and the more you practice and the more clients you have, the more risk of conflicts arise, right. So from that perspective, you know, and conflicts can arise in very different kind of circumstances as well, or you know. So from that perspective, you want something which is unbiased, which is unconflicted and which you can rely on as a fair degree of accuracy as well. So that's something that, as a first layer, you can immediately achieve through Gen AI. Now we can also go much deeper, and we know this because we have products that do exactly this, that go into different nuances of the law, different sectors of the law things like this as well. So in the hands of a practitioner it is like a surgical knife you can use, cut it or sealle, in either way that you do it, but it's a very fine instrument. So that's how I would do something like this.

Chetan:

And secondly, it also especially in legal departments, in law firms helps sorry, not just in law firms, but also in enterprises. So, legal department, it helps to maintain institutional history. Helps sorry, not just in law firms, but also in enterprises, right? So, legal department, it helps to maintain institutional history. What happens in both cases, both in law firms as well as enterprises, is over a period of time, there's a lot of institutional history which is left with one or two people in the law firm it's impossible to replace, even in law firms firms, for that matter. It would be like go to this person to get an idea as to why this happened, where and when and how, and why the firm took a view that this is the right approach and something else.

Chetan:

In an institution scenario it would be like what did we do in this deal, say, five years back?

Chetan:

How do we tackle a certain problem?

Chetan:

How do we respond to legal notice that's come from a regulatory authority in a certain manner.

Chetan:

So this GENAI helps maintain the institutional memory which till now was lacking, which till now was lacking, which now till now was has been relegated to senior partners in law firms and long tenured folks in companies. So what happens? That is, bridge that gap, so what you know how. Now the question is, how has it reached the gap? All the data that that comes, whether it's correspondence or whether it is documents or contacts and things like this, is available today to be converted into a data set or a mini legal model or whatever the case is. So, over a period of time, it maintains institutional history as to what happened when. So, to give you an example, in many banks, especially in Mumbai, every department would have somebody who had the knowledge of foreign exchange management, right From FED and FAMA. These are typically the officers who have spent at least 30 years in foreign exchange and knew all of the nuances that RBI would have taken. Today we have got a board to do all these things.

Guru:

Right, that can transfer the 2000 page book that you have shown me the other day into one single board, which you can simply ask for some time.

Chetan:

So just for the benefit I was studying, I showed Guru saying you know, the data set that we have is essentially the equivalent of Snow White publication, which is a 2000 fish tome on foreign exchange. As an example, yes.

Chetan:

Yes, you know, if you really require like a go through, you know you'll need a publication to go through it, but one. It is not always going to be correct and if you look at 2,000 pages and the time, you commit those 2,000 pages to memory and then analyze what the implications are, it's going to take a long time Correct For a typical practitioner, it will take at least 10 years for them to even understand what the nuances are for all of these things.

Chetan:

So that's the third thing. Cutting short the time is basically cutting short the efficiency. No, it's not just about that.

Guru:

I need an answer, and I need it now.

Chetan:

So from a current generation's perspective as well, and the way that Gen Z also uses AI. They have grown into a generation where it has all of these things, so you need access immediately. So when you can order food, they'll order in five minutes, or you can watch something on your mobile phone or your TV which is immediate.

Guru:

So why shouldn't this be the delivery of services?

Chetan:

You know when you can get something at 3am in the morning, then why shouldn't this be so? If you really look at it, it is immediate. So that is another. Immediacy is another feature for all of these things. Now, the last, one of the other things is efficiency, which you talked about. So what would take? A lot of tomes, reams of paper, a lot of people looking into it, external counsel coming in doing this is now all cut short.

Guru:

So from an efficiency perspective.

Chetan:

If you are in a legal Essentially you have a multi-specialty team at your beck and call.

Chetan:

It's the same thing on the law firm front as well, and the law firms spend a lot of time training people. It takes many years to train good lawyers and then for them to come out and do all these things. That part of efficiency is clearly taken care of as well, and also as models improve, as specialist data sets are created. The other advantage is also on knowledge retention. Today we have pipelines that continuously update data sets, so every time a judgment comes out as an example, you can say with certainty how is it, what the results were, how is it going to be? You tie that into predictive analytics, you will know what the trends of cases going on are. All these things exist today as well, but you did not have a conversational interface with all of these things, which has really brought it to the core. And also there are other things as well multilingual, you know you can. We have the ability today to upload a handwritten or multilingual text and, you know, have it reviewed pretty much in real time.

Guru:

Also, in a country like India, where there are 25 plus 30 plus official languages, there's something that is going to come out.

Chetan:

So I can go on and on and all of these things but, these are some of the things that you can really sort of like. You know, these are some of the improvements that, and then AI, can bring you to all of these things as well.

Guru:

Okay, so we know that the legal sector is a document heavy sector, so we deal with contracts, documents, hundreds of thousands of them on a regular basis. So how does AI steal my tasks, such as documentation storing hundreds of thousands of them on a regular basis? So how does AI streamline tasks such as documentation storing, legal research, etc. Can you provide us some examples of some of these technologies at work?

Chetan:

Yeah, see one of the things which is increasing. We live in a very complex world, correct? One of the things is standardization of documents.

Chetan:

You know whether you, even today, if you take a case of a country like India and you go to the courts, as an example, a lot of the documentation is not standardized. You still have to sit and handwrite it. You know some portions of it is automated, like cause lists and things like that, but that's older. It's like you're taking 200-year-old rules and lines of like streamlining or trying to create an application based on it. It is no longer and therefore you will end up with all sorts of complications. People will not know what form to use when. So one of the things if you practice in the US as an example, you'll certainly find is that a lot of the forms are standardized and this is across states as well. You know this helps in. This helps not just in very efficient justice delivery, but also, if you really look at it, say, for companies as well or law firms as well. You have standardized documentation.

Guru:

Makes it easier to manage. Yeah, it makes it easier to manage.

Chetan:

Correct you have, and you can easily make out which of these are your documents and you know from a client's perspective which of these clauses in documents can be faked or not Correct. Certain documents can be faked or not. So standardization, document automation is very core to solving a lot of things. It's very rare that you would actually go and create a document from scratch. The only time that happens is there's a major regulatory change and there are no precedents that are created. But in 99% of the cases, standardization is the way to go. So, whether it is forms, whether it is precedents, whether it is contacts, I think you need to have document documentation as one of the ways in which you can streamline tasks as well.

Chetan:

The second is on legal research. Legal research, as I did as a law student or as a practicing attorney, was, you know, go through huge books, correct. Spend your time in books and it will give you a very nuanced view as well. But there's a limit. I used to work, I did an internship with a tax counselor who meticulously maintained handwritten records of every tax ruling that came out.

Chetan:

Now, that's basically like you know, the index of documents which you can automate today in like a few seconds. This was a gentleman who did it and he took a lot of pride in doing all these things as well. From my perspective, it took a lot of time and the index is not searchable. So these were things that occurred to us 30 years back. But now, as an example, legal research will change.

Chetan:

Legal research today has also access to various databases and various articles and things like that. If you you know when I used to study in my alma mater National Law School, you know you would have access to all these online journals and you know, and also you know if you didn't have access to certain journals, by the way, india still may not have a network library system, so you would go to IAM, bangalore or IAC to access certain journals which they would have access to. If you go to the US, or the UK for that matter, most of the library systems are networked. So that means that even if you don't have access to a particular publication, you can use network. Basically, it's a form of the internet like that you can access in real time.

Chetan:

Now this is from a legal research perspective, say from law school or legal scholars or younger lawyers to do this, but in a law firm, legal research tends to be very, very specialized. It is a question of knowledge, having prior knowledge and looking at the outcomes of what you require and supporting those outcomes through research. So both these today are able to be used. You can use, for instance, like our QBOTS, to have general access to legal research, but you can also use very specialized data, which is the IP of the firm or the company concerned, and basically derive an analysis based on the standards of using very custom data sets is what I'm trying to say Using custom data sets to answer very specific questions that a firm may be specialized in. So these are some of the things that you can use to sort of do legal research, also arrive at outcome-based research and also tie in the two, because analysis one of the things that today's LLMs depending on the LLM also, you know, having the ability to sort of, you know, arbitrate on what is the highest from a statistics perspective, what is the highest level of where this is occurring.

Chetan:

So this automatically comes in from statistics, statistics point of view, saying that your text output is linked to whatever is strongest in the data sets that you're doing, what happens if you use it in a custom data set, is you get very accurate answers. So that you know I'm trying to model it essentially right. So you know that is something which did not exist before. So, and you use a bot as a thinking man's tool, it becomes your favorite assistant which can answer pretty much, or meet your level of intellectual capability or more, to be able to, sort of like, give you a very nuanced answer back. So once again I go back to the surgeon's life saying you can use the tool, it can be as sharp as you want it to be, and we know this. And so it really depends on the ability of firms or companies to be able to deploy all of these things, because this is what is required to make it to the next level, and that's how things are changing.

People on this episode

Head Shot

Chetan Nagendra

Host