How AI Solves Contract Management Challenges

Explore how AI is disrupting the CLM landscape and enabling leading organizations to:

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Accelerate sales cycles
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Improve risk management
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Reduce contracting costs

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In this webinar, you will learn how you can leverage AI to:

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Reduce risk exposure and prevent revenue leakage
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Create a searchable repository for your contracts
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Identify, extract, and analyze key terms, clauses, & dates

Meet Our Speakers!

John D. Wagner

EVP of Products & Engineering
IntelAgree
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Kyle Myers

Director of Professional Services
IntelAgree
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Jasmine Jenkins

Marketing Manager
IntelAgree
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Read the Transcript!

Jasmine Jenkins:

All right, we're going to go ahead and get started. Hi everyone. Thank you for joining us for our webinar, How AI Solves Contract Management Challenges. My name is Jasmine Jenkins and I'm the marketing manager at IntelAgree and I'm also today's moderator. Today, I'm joined by your presenters and my colleagues, John Wagner, who's our co-founder and EDP of products and engineering and Kyle Myers, our director of professional services. Kyle and John have a fantastic conversation in store for you today. But before we talk about, get into the presentation, I want you to imagine, what would your business be like if it were 30% more efficient? Whether that's a reduction in contracting costs or the amount of time that you spend in your contract management process, well, according to Gartner by 2023, AI will improve the contract management process by 30%, but we don't have to wait until 2023 for that to happen because it's already happening today.

Jasmine Jenkins:

And that's exactly what John and Kyle will be discussing. They're going to be talking about the CLM landscape and specifically how AI is disrupting this traditional approach to CLM. They'll also share real use cases and examples of how companies today are using AI to better manage the end-to-end CLM process. So during the presentation, if you have questions, I encourage you to enter them in the questions tab, which is on the right hand side of your webinar window, and we'll have some time at the end to get to them. In addition to that, we'll also be sending this recording out so that if you want to share this content with people in your network, or perhaps you want to refer back to it at your convenience, you'll be able to do that. Let's dig into our conversation. Starting with the CLM landscape, John, can you share what you're seeing in terms of industry trends and growth in the CLM market?

John Wagner:

Sure. What we're seeing is that this is a really fast growing market. There's a lot of analysts at both Gartner and Forrester who are actually predicting that the CLM market could double in the next four years, so a lot of activity here. What we think we're seeing when working with prospects and clients is that a lot of this growth is actually fueled by an increased demand for digital transformation. And I think we probably all have experienced this recently with COVID-19 that companies are trying to make that shift to getting everything online so that all of their employees can access the information they need when they need it and contracts are really no exception. In general, with CLM, we think the days of contracts being stored in a simplistic document storage system are really over. When we look at most of the key players in the CLM space today, they're offering very different tools than they were just five years ago.

John Wagner:

And I guess what we've observed is that some of this is due to market consolidation. You see a lot of different CLM players that merged together and brought their specialized pieces of CLM into integrated platform offerings. But you also see startups like us, IntelAgree, that are starting fresh, taking a brand new look at CLM and trying to reimagine it. What we see at the forefront of the modern CLM platform is one that would be capable of doing just a lot more than storing contracts. So some of the best CLM tools out there are the ones that they're facilitating workflow during the contract creation and negotiation process, integrating with other key enterprise systems like CRM tools, providing e-signature capabilities that we all know and love. In fact, I would guess we all recognize that that's just something you have to have these days to do any kind of contracts online.

John Wagner:

And so getting that type of functionality built into the product so you don't have to go buy that service from somebody like DocuSign is a huge advantage. And then lastly, and I think we think most importantly, these modern CLM tools are capable of reading the contract text and using what it finds in there to drive workflow, to automate red lines and really just make your contract repository much more searchable than if you had to do all that by hand. So we're really excited about how artificial intelligence is disrupting the CLM space. And I think we just see that there's really this clear gap emerging between CLM products that do use AI and those that don't.

Jasmine Jenkins:

Can you tell us a little bit more about that gap?

John Wagner:

Yeah. When we compare and contrast the old standard CLM tools and the new AI enabled ones, we usually start by talking about the basic functionality of CLM that has always been there. How do you create contracts? How do you version them? How do you do maybe some workflow, can you summarize the agreement? Can you get them to be signed up? You have to have all of that if you're going to be a viable CLM platform. But when we talk about the AI powered CLM, we start to see platforms that can not only do those basic five things, but they can, for example, import a contract that wasn't originally authored in the platform and can actually make sense of that document and read it and extract information and concepts from it. One of the things that's really impactful when you first are starting with an AI powered platform that you're going to notice right away is, you may have tens of thousands of documents that you need to get into that system.

John Wagner:

And historically, if you were to go put in a new CLM platform, you had to do all that work yourself. It was a very uphill battle that you and your team had to fight. So one of the biggest disruptions of AI in CLM is having the system, reading those agreements, being able to sort and make sense of them, weeding out the duplicates, grouping similar agreement types together, identifying the parties and key dates. It's a huge accelerator to getting value from that investment in the platform right away. And I think the other thing we observe is that with this new ability to efficiently get the data extracted from the agreements, you end up with better integrations to the other systems. So you have data that's now flowing out of your contracts tool into other key systems, maybe like a billing system where you're operationalizing the terms of your contract. And so instead of having to manually do those extractions and key them into other systems, you see much more seamless integrations happening today.

Jasmine Jenkins:

Got it. [crosstalk 00:07:03] If a company isn't investing in CLM and specifically AI based CLM, what's the impact of that, John?

John Wagner:

I think you've probably heard me say this before, I'm not a huge fan of people just implementing technology because they think it's new and cool. There's got to be some economic driver for why you're considering that investment in technology. And so, I think what we advise people is, if you're a company and you're thinking about implementing a CLM tool, or maybe upgrading your legacy platform to a more modern one, you really want to spend some time and take a look at some of the research and case studies that have been done recently. Most of those studies are showing that it is surprisingly expensive to get a contract done. You may not think about that on the surface, but if you dig in and look at these studies, they're finding pretty consistently that to get a low risk, low effort agreement done, a company still might spend 5 to $7,000 in labor.

John Wagner:

When you think about all the people who have to get involved and all the drafts that are moving around. As you move to higher complexity agreements, that number goes way up. And so if you can do anything to bring more efficiency to your organization, to that process, to speed it up and remove the roadblocks during creation and negotiation, to get you signing done quicker, that gets you revenue faster, that drives your costs down. It's pretty easy to justify an investment in one of these platforms because of the efficiency gains that you get. We know that looking back 5 or 10 years ago, I don't think anybody would have said that a CLM tool is something that's going to give your company a competitive advantage, but now with these accelerators that are available, especially in the AI enabled platforms, you really can fall behind your competitors if you're not using a platform that's bringing some of that new technology to bear.

Jasmine Jenkins:

John, talking through this a little bit more, how do you define an AI based CLM platform?

John Wagner:

I get that question a lot. When we're stuck down in the technology, sometimes we just start throwing buzzwords around and forget to define them for people. I guess, from business terms, the way I would boil it down is we're trying to create tools that are going to help someone in the contracts world draft and negotiate and evolve their contracts more quickly, but also make sure that we are doing that constant near real time data extraction from those agreements so that we always know what's in there. Historically, that was always the challenge of CLM and CLM platforms is, you could implement a system because it had the promise of being able to store everything centrally and put everything at your fingertips, but you had to do all this work on top of the work you were doing to create the contracts in order to track everything.

John Wagner:

When you think about putting together a contract that's 20 or 30 pages long, lots of legalese that's super important, that all has to be there. It has to be well-crafted and skillfully negotiated with the other party, but at the end of the day, what's articulated inside all of that text are some really important business terms that need to flow through your organization, need to flow to other systems. And so trying to make sure that all of the stakeholders in your company can quickly understand what's in the agreement so they can deliver the work or understand what's in the agreement so they can bill and collect for that work. You've got to get that data out of the contract, and so the AI enablement is really about making those contracts more of a living agreement that can, in some ways, speak for itself.

John Wagner:

It can tell the rest of the enterprise what's in there because the tools can get that data out. When Kyle and his team, I know they talk to customers all the time and the thing that we hear over and over again is legal departments are swamped. They are so busy trying to draft and review agreements. They don't have time to extract that operational data from the document. Most organizations are trying to do that, but it's somewhere on the priority list and guess what? It usually falls behind drafting the next agreement. Once you fall behind on extracting that data out, you find that you stop doing it and that's really bad for the organization. The way that these tools today make this work in an efficient way is through a technique called machine learning.

John Wagner:

Your [heroes 00:11:55] throw around the terminology of training. And really what we're talking about there is the way that the system doesn't have to be told specifically where information is, but it just kind of knows how to find it. And what most of these tools do, they allow someone, let's say, in your company who knows these contracts to read them within the tool, highlight things that are important, say, here's a certain business term, or here's a concept that's important. And maybe even indicate is that favorable or unfavorable in the particular situation. And after the tool sees this 40 or 50 times, it starts to wire its brain together to know how to find that in the next agreement that it's never seen before.

John Wagner:

It sounds a little mystical and magical, but this machine learning technology is pretty well proven and is now just being brought to the world of CLM. And all of this just really accelerates your ability to review documents more quickly, to get the right people in your organization called to action when their approval or input is needed. And it takes your risk down because you have a pretty clear picture of what's in these agreements, or sometimes what's missing from certain agreements. I know that's a long winded answer, Jasmine, but I hope that helps a little bit.

Jasmine Jenkins:

No, that works. Thanks, John. You've talked about the landscape and you've defined exactly how AI CLM works, Kyle, from your experience, can you drill down a little bit more into those benefits and share what those benefits are incorporating AI into your CLM process?

Kyle Myers:

Yeah, absolutely. So while the benefits are certainly innumerable, I think there's probably four key ones for us to concentrate on today. The first is that we can just get started and get organized more quickly. We can get indexed and searchable much more rapidly. We can then find those key data points and themes using some common and custom machine learning models. From there, we have the ability to search across the entire universe of our contracts to find specific data terms and be able to report out, which was really important. And then ultimately that enables us to be able to compare specific clauses and terms, and that can really help to strengthen our negotiation strategies. Let's get started with organization here. As John mentioned, the folks that we're working with, the legal minds are often the most busy or some of the busiest within an organization.

Kyle Myers:

They don't really have a ton of time to organize things. And either they have a junk drawer or a messy filing cabinet, and they may not have a standard naming convention, or they may have done some of this previously and really dug in in organizing things, but now they kind of want to take it to the next level. But either way, AI can really help to accelerate that process. You can turn through thousands of agreements at a time and the system will kind of help you to understand these 500 agreements all are very similar and not only are they very similar, but they're very similar to what we think is an MSA or what we think is an NDA or what we think is an SOW. And so those clusters then, we're able to label those very quickly, get them into a system and organize, and also help us sort of that process of deduplicating.

Kyle Myers:

The last thing you want to do is bring in bad data or extraneous data into the system. And so if you have five or six agreements that are exactly the same, you don't want to confuse your operating team by having extra agreements in the system. But also, AI can do the opposite side of that too, which is making sure that some of those that are near dupes, they look almost identical, but they're actually different parties in the contract, we don't want to exclude those. So it kind of helps with both sides of that, but either way, whether you're in that junk drawer or you're in that like, "Look, we've already done a lot of work before, but now we want to improve upon that," AI allows us to kind of have a clean starting point for transformation.

Kyle Myers:

And then once we've begun that transformation effort, there's two things you're looking for when you go to market in terms of looking for a tool, and that is one, what AI models are they providing out of the box? If I were just upload 500 agreements, what could you tell me about them? What would I learn immediately? Termination information, insurance information, those sort of common governing law type things across all of your agreements that are very common to all industries and all lines of business. Those are really helpful and you want to make sure that you're getting those that are very informed by a lot of data points, but then also you want to make sure you have the ability to train custom models that are maybe specific to your industry, or even maybe specific to your competitive advantage within an industry.

Kyle Myers:

Maybe you do things a little bit differently. You've got a different type of guarantee. You need to make sure you've got credentialing if you're in the healthcare space. You need to make sure you understand SLAs and rebate opportunities if you're looking through your vendor contracts. You want to make sure that you have the ability to fine tune those to your organization. So it's really important that during the evaluation, you understand how easy it is to train those models. The last thing you want to do is have to employ a data scientist just for this purpose. You want to make sure that it's just as easy as point and click, highlighting in red line through a Word document, or even if you had printed it out old school and highlighted it with a marker, you kind of want it to be that intuitive to it a user, to sure that it's really easy to build and refine those models.

Kyle Myers:

From there, what that really enables is, you've got organized, you've got these models in place, you've read thousands of agreements against them. We now have the ability to have a very well-defined, searchable contract repository. Your finance team can come in and have visibility into key billing and rate information. Show me everything, the payment terms created in 60 days. I want to go renegotiate that. Maybe you want to make sure that you're not operating on an expired contract. We've heard horror stories of where someone has been working on a handshake basically because the contract expired for two years and it's all great until things go south, but then you might have a problem there.

Kyle Myers:

It's really good to make sure you have the ability to do that kind of searching, but you also want to make sure that your repository has flexibility because you may not always know what questions you're going to need to ask. Think back six months ago, force majeure was a concept that was in every contract, but it was kind of loosely defined or maybe just written as acts of God, but you needed the ability now to be able to search across all of those agreements to see where do I have pandemic language? Where do I have access acts of God? How is that impacting my business? John, I think you've dealt with a few clients that that was a real concern for them.

John Wagner:

Yeah. As I think back to your COVID example is good, we saw something similar with some of the pro sports teams that use IntelAgree where even pre-COVID, they were wanting to look at all of their agreements and understand work stoppage. If their league had a threat of the upcoming season, having some sort of work stoppage due to the lockout, how would they understand which of their agreements had that concept in it and what the consequences of that happening, if that event happened in their business, what would that mean to them? And so we saw them using our product to quickly scan their agreements, train models to show examples of what they meant by generally work stoppage, which could be expressed hundreds of different ways in these agreements, and then get that list of contracts back. And maybe most importantly, then go examine what levers they had to pull in those contracts if they needed to react and not have a huge impact to their business.

John Wagner:

So I think that again, even pre-COVID, was a great example of somebody just wanting to know what's in there and how might it impact my business and if it starts to impact my business, what do I do about it? And that was all expressed somewhere in the contracts.

Kyle Myers:

Yeah. And I think that's a great example. And I think that really helps to underscore how important it is that you have a solution that can not only meet those needs of today, but can support your future needs and have some flexibility there. And so then lastly, where that brings us from a key benefits perspective is we've got organized, we figured out what we wanted to track. We've brought in thousands of agreements into the system. Now, how do we want to inform some of some decisions about our business going forward? I think a couple years from now, maybe you want to revamp your marketing website and, "Which contracts do I have that allow me to put their logo onto my website?" You can quickly go through and drill into publicity restrictions. You might be able to quickly find 30 contracts that will allow that.

Kyle Myers:

And then you can cherry pick the 10 logos that you really want to put onto your website. It's a lot faster than having to have a contracts admin skim through all of those and make sure that you can put their website or their information on your website. And then, thinking about the way you negotiate today, maybe for the last year, you found yourself continuously negotiating three or four pieces of your contract. You can now see how you've done that in the past and allow that to inform maybe some changes to the way your base template is configured. If you can cut out three or four days of red logging, how much better is that for your business to get the deal done more quickly so you can realize revenue much more quickly?

Kyle Myers:

That's another opportunity there. And then also comparing sensitive clauses. Think about limitation or liability or indemnification. It's really important to be able to see where your organization stacks up across different agreements, especially if you're starting to renew different agreements and be able to evaluate which of those clauses maybe deviate from what you feel comfortable with from a risk profile perspective. The use of AI really allows you to do all of those things very quickly without having to do extra work during the negotiation process. I think that's really what most of the legal minds in the world are looking for, is how can I get more value without doing any extra work? And so that's really where the beauty of AI comes in.

Jasmine Jenkins:

Thanks, Kyle. That was a... it's really good examples for people who are watching. Oftentimes when we talk to prospects, they'll say, "Oh, this all sounds fantastic, but how do I get started? I have hundreds, maybe even thousands of agreements, where do I even begin?" John, any insights there?

John Wagner:

Yeah. As I mentioned earlier, it used to be a pretty daunting task to get started with a CLM system, or even to upgrade from an old one to a new one. You could be looking at a 12 month project. You'll need a lot of different people from your organization to get involved, to get everything set up correctly. And I think what we see is has changed with the AI enabled tools is that it really is a different game now. You have this ability to get some fairly immediate value out of your CLM implementation. I think Kyle would probably tell you that literally within a few weeks, you can see people really understanding that they made a good investment because, just getting those legacy agreements uploaded and reconciled within the system to know what's there is very eyeopening and provides immediate value.

John Wagner:

Your team can now search even outside of your legal team, finance can now search and find things. And so you get huge value quickly. And then as Kyle touched on that the use of these machine learning models, whether it's an out of the box model that someone has prebuilt for generic contracts or for contracts in your industry, that is a huge accelerator. And as Kyle hit on, especially if you can do that in a combination where it's given to you out of the box for your industry, and then you can layer things about your business on top of that, that's really useful and you get even more value out of the platform. And all of that. If you can go fast very quickly, if you can get moving quickly and get value quickly, now you have time to take on some of the more complicated parts of the implementation, which is going to be true of pretty much any platform.

John Wagner:

You've got to get templates set up. You've got to think about how you're going to author. You have to think about some of the workflow, who gets involved, who's approving. There's no magic wand for that and so being able to leverage the AI on the reading and the understanding of a legacy agreements gets you that quick value. Now you have time to do the more cumbersome parts of getting your templates in which even that these days is fairly straightforward, given that Word is such a prevalent tool, most of these platforms work really well with it. So, getting moving quickly is definitely something you can do today. And I guess lastly, with integrations, because that always comes up, it's just easier these days to integrate, whether it's to an upstream CRM system or a downstream finance system, you get those integrations mostly out of the box and you start to find that your entire organization, sales, finance is now getting benefit from the CLM tool. It's not just something that sits down in procurement or in legal. It's something the entire enterprise benefits from.

Jasmine Jenkins:

Thanks, John. It looks like we have a few minutes for questions and I'm going to see here. I think we got a few of you sending in some questions. Okay. So we have one, "Are you aware of a CLM contract assembly slash management tool that can actually do the initial red lines of a contract? i.e., Understand what the company requires in terms of contract terms and make edits pursuant to a playbook or standard clause library?"

John Wagner:

Yeah, there are definitely a few out there that, top hat is one of their top strengths. And this is one of the interesting things you'll find when you go in and start to look for tools, you're going to see some that are really strong on the offering side. So maybe their mission in life is to help you author the agreement as quickly and as best as possible. And so to that question, there are a couple tools that I can think of that really have decided that's the part of CLM that, their territory, they're staking out. They want to do the drafting really quick. And so they let you kind of store your playbook, run your plays accordingly to get the first red line done. What we tend to see is that those that do that often don't do some of the workflow real well in terms of engaging other people in the organization in the decision making for a contract, and then also tend to fall apart on the reading of a new agreement.

John Wagner:

So, while again, they can be real strong with the offering side, the other pieces aren't necessarily there, but I think what is being asked for is absolutely there. And so, I think the right terms to search for when you're looking at products would be playbook, you'll see that use a lot. Red lines, but that automation of how you take that first draft, whether it's offer you a template or from the wild and make the edits that make sense per your playbook is absolutely something that's center played and I'd say if you look across the whole CLM space, probably a third of the products out there are offering that. So if that's important to you, you will definitely find it as you look around.

Jasmine Jenkins:

Thanks, John. And then we have time, I think, for one more and Kyle, it sounds like it'd be a good one for you, "What can be expected when implementing a CLM platform in terms of time and effort?"

Kyle Myers:

That's a great question. I think you'll find as you start to evaluate different solutions, you'll see some that are on the 8 to 12 week timeframe and then some that are upwards of 9 to 12 months. And it really just depends on sort of what their out-of-the-box solution is, how consultative they already in nature, which I think is really important when you're going out to find someone who's doing that, not just someone who's going to bolt on their tech to your process today, but how can you really transform your process using their tech to do that? You should definitely expect that you're going to spend a couple hours a week during that time process to really be able to dig in, make sure everyone understands what it is that you're looking for. What kind of questions you're going to want to ask.

Kyle Myers:

It really depends on the vendor, but I would say also, don't be afraid to start small. You can go out and find some quick wins with any CLM vendor, build some champions and some adoption internally, and then expand that use case out, especially if you're an enterprise organization and you just might not have time to take on that whole thing today. Whether it's just building out your contract repository or building out [inaudible 00:29:47] from one business unit and a few of the templates and really building some internal champions. We found that that really gives you a lot of success downstream.

Jasmine Jenkins:

Thanks. It looks like we are right at 1:30. I want to be mindful of everyone's time. Just want to thank you all for attending today. Hopefully, you're able to walk away with some helpful tidbits on how AI is really disrupting the CLM landscape. If you do have questions, please do get in touch with us. You can find us online @intelagree.com or you can email us, call us, find us on social media. We'd love to hear from you. And yeah, thank you for spending your afternoon with us and stay tuned for more content from us. Thanks everyone.

John Wagner:

Thanks Jasmine.

Kyle Myers:

Thank you.

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