Name: CLM Platform Tour - Better Contracts Faster with AI
Uploaded: 2025-11-19T15:16:05.197Z
Duration: 26 min 12 s
Description: CLM Platform Tour - Better Contracts Faster with AI

Transcript for "CLM Platform Tour - Better Contracts Faster with AI": Okay. I would say we maybe another twenty five seconds to join. I think we can I can see some people joining? Perfect. Okay. I'm okay. Let's get going. Welcome everyone to our CLM platform tour at Better Contracts Faster. Thank you so much for joining us today. Before I'm handing it over to our fantastic host, Anya, just a little bit of housekeeping. So for your best viewing, we recommend you close any other applications that you might have running in the background. We do recommend Chrome as a browser, but all the others should work fine as well. For the best sound quality, please use headphones. If you have any questions during the session, please, type them in the q and a, and Anja will get to your questions at the end of the session. If you're having any technical issues at all, please feel free to reach out to me in the chat. And then, also last but not least, you will receive a follow-up email with the recording of this webinar twenty four hours, after it finishes. Also, feel free to share it with your colleagues who might be interested in watching it. And, yes, I'm now handing it over to you, Anja. Let's go. Thank you, Andrea. Hi, everyone. A warm welcome also from my end. I'm Anja Feigrassil. I'm a solutions consultant here at Agiloft, and I'll be walking you through the demonstration today. Before we start, I wanna take a couple of seconds and talk about data and Archilof's approach to managing data, and then we'll dive into the demonstration. Here at Archilof, we believe that taking a data first approach to contract lifecycle management is essential to solve some fundamental problems that organizations face when it comes to contracting processes. If you have a look at the diagram here, so despite CLM being around for over twenty five years now, most organizations still look something like the the, picture on the left hand side here. So you have a spaghetti network of not or federated solutions that don't talk to each other, which then often resides into a lack of analytics or insights. You have departments that are working disjointed and in silos, and you also have tech that is either not enabled or it's not scalable across the organization. And what we are looking for or aiming for is more on the diagram on the right hand side here. The future state is a unified enterprise that is equipped with a connected CLM, and this then results in data driven decision making. You have an enterprise centric view of your contracts, and you also have automated processes which are cross departmental. But in order to achieve this, it really requires a strong data governance, and this then often leads to another problem because now you have CLM, which represents a new system of records that your business, needs to connect to. But no two organizations have the same set of tools and systems that they are using across the organization, and there's variability in your CRM, your ERP, your procurement, and IT tools. So when data changes, what you need to make sure that this change is reflected in all the systems that you are using, and brittle integrations are not something that you can rely on. The solution is that you need a webinar who has a deep understanding of the data that is involved in the contracting processes. This contains extracting data across different categories, mainly your contract content data. Those are the terms and clauses and standards within your active and legacy contracts. We have contract process data, which is created as part of workflow such as your intake for negotiation or approval processes, etc. We also have the contract performance tracking data. This is data on how your obligations and commitments are either kept or broken throughout the life of a contract. Then lastly, we have the contract decision support data, which is the data that really helps your business to make informed decisions as part of the contracting process. So we've seen that businesses have, various organizational structures that may care about different data types and they all need to be passed into the external systems. And the Agilev approach has always been control the solutions that they are building through a no code configuration. So when you are needing a new data field, if you're needing to change a clause or a template, you are able to manage that change by yourself without any empowering approach to the introduction of AI within our platform. Just a quick highlight here. ArchLoft covers the entire life cycle journey, but today, we are very much focusing on the AI side of things. Hereby, I'll be showing you how we are supporting different use cases with AI throughout the contract life cycle. We'll be focusing on four different scenarios, starting with contract analysis. So hereby, we are using our pre built AI models to help you convert any unstructured data from your agreements and documents into actionable data points. So hereby, we have the Archiloft AI trainer, which is used to identify key terms and clauses in your contracts and then extract those back into actionable data points into the contract record. Common use cases here are, for example, when you're reviewing or uploading a legacy contract, but also if you are negotiating on third party paper. Secondly, I will show you our screens and actions in in action. So those are used for contract review and helping you just to identify any standards in your third party agreements, but then aligning them with your internal preferred positions and helping you speed up the review and negotiation process. Then lastly, I'll be touching base on Ask AI and PromptLab, which help you to quickly find data that might be out of your usual use cases. As Andrea mentioned, if there are any questions throughout the presentation, please pop them in the chat and we'll get back to them at the end of the session. With that, we're going to dive into the demonstration. I'm going to start in the platform with a contract request. This is something that you can pass or allow your end users who are not familiar with the CLM workflows to do. They, for example, may have received a contract document, they can send it over to you in a request. Rather than keeping everything in email chains, they submit the request, you get instant visibility of where in the workflow those documents are, and then we can use AI to help us understand the content of those documents. Then first pass while we're uploading this file, it will run our pre trained labels against that agreement and then bring those back as actionable data points into the platform, helping us to autofill that contract request or intake form, but also extracting, for example, identifying any key terms that need an approval or a review workflow. As mentioned, your requesters will have instant visibility into that process. They'll get information was extracted is automatically added into the correct sections of our intake form. Then we also have a summary of all the extractions that have been identified alongside any of the clauses that were part of this agreement. If you want to see that within the document, we can also bring up the attachment. Here, you get a holistic view of the file that was uploaded alongside all the data points that have been analyzed or identified. They are highlighted in the tagging section here, and then you can quickly review what extractions have been identified. And then for your end users, also a great way, in helping to free up the legal team. So if they have any specific questions around this agreement that is not necessarily extractions. You see we have the Ask AI option here. This is a chart functionality where you can then come in and you can ask any question. For example, what are my termination rights? AI will now give you references if there's context provided within your document. You see here you get a quick answer, and then you can also see where the reference is coming from. A quick way of searching information across the agreement. Now, we also want to understand how we can use that document that the third party provided and bring it into alignment with our internal policies and standards when we are negotiating. This time, I'm going to go ahead and open this document in Microsoft Word, where we can then utilize our plugin. This allows us to compare the document against our internal standards while keeping everything in Microsoft Word, so your contract managers are probably very familiar with working in Word. You see, we have a set of screens that we can select from. And while this is running, I'm going to talk a little bit about the setup of screens and the standards. So conventionally, you had a clause by clause review when you're redlining an agreement. But essentially, what we're doing there is comparing a contract in a clause to a contract in a clause library, which is comparing unstructured data and really hard for the AI to make sense or understand that. So basically, what you're doing there is compare a to b and tell me what are the similarities or the differences. We are switching to a standard based approach here, whereby you can define a topic or a subtopic as a standard, and then you can individually see if those standards are met or failed. If you think about a limitation of liability clause, for example, you can, in your standards define, does it cover direct or indirect damages? For example, is there a fixed cap on the liability and so on. Within a standard, you can define or give additional instructions to the AI. Look at exactly the limitation of liability cap. If it's greater or less than, you need to mark it as failing or passing. This is how those results are then coming back into the platform. This is a good way for your legal joiners that are very new to the company, for example, also understand what kind of sections they can negotiate on, where they need to stand firm, and so on. We can review the standards either by topic, you can review them by risk classification, or you can focus on the ones that are failing. Then for example, we can look at our compliance here. We say our standard says that there needs to be an anti bribery compliance. If we go into the details page, you'll get some further information on how AI is addressing that standard. On the top of the screen, you see the definition that we are checking for. Secondly, we are getting an explanation. There is no language in this specific agreement, but you'll see that AI is then also suggesting some red lines that we can add. So wherever in the agreement, we are now placing the cursor, so let's say the end of this year, we can then just simply insert the red lines that were provided. If information has been found, for example, we can look at the governing law, you will see it will then take you into the section where we are reviewing. Again, we are getting an explanation. Here we are saying why this is failing, and then the AI reasoning is explaining exactly why it's failing. Then we can also use this to apply red lines on sections within the document that are already existing. You will also see that as part of the review, we can provide some user guidance which gives instructions to your new joiners or your existing legal team members that allow them to gauge how and when to use those specific standards. All of those changes that are made in an agreement are then also fed back into the platform. As we are saving them, they will be translating back as a new version into the platform, coming also back as the extractions, as data points into the contract record. One quick note on that as well. The standards and screens are managed by you. However, we are bringing you the best of both worlds. If you have a look at our community, you see within the screens community, there are some out of the box standards that are readily available for you to use, especially useful around obligation management. But then you can obviously also customize them or you can set up your own standards and policies to reflect what internal areas you need to review and check for. Let's go back into the contract record. You'll see if we refresh this agreement, we'll have a section here on the extracted data. Yeah. Minimize that. That is now showing us all the standards that have been extracted. Then from here, you can assign further reviews to those. A failing standard, for example, can then trigger an approval workflow or a review item for another internal stakeholder. This is screens on helping you speed up the negotiation process. The other use case that we have for screens is helping you understand the obligations that are sitting within a contract. This can be applied again through third party agreements once you finish the negotiation. It can also be applied to contracts that originating on your internal templates, or you could use that for any legacy contracts that you are uploading into the platform. Here, for example, I have a master service agreement that is already active within the platform. I can then come in here and we can go into the obligations tab and define what document, first of all, we want to analyze, if you have multiple versions. Then you'll see there is a set of obligation screens that we can run. Let's go for our commercial agreements here, and then we're going to just screen that document. Again, this takes a couple of seconds to run depending on the size of the extractions. Then once that's completed, we can see the analysis and then turn those obligations into tasks for your internal and external stakeholders. So if we give this another second. Here is now a list of all the obligations that have been identified. You will see within those, we get a topic subtopic. You also see what question was answered as part of the reference in the documents. So this pinpoint citation is stating where within your agreement this has been found. And then we can go ahead and further review those. So if it's something that doesn't need a follow-up, you can just mark it as reviewed or rejected. If it does need a task and someone needs to physically go in and manage this obligation, we can also assign it to a team or an individual to help us manage this going forward. All of that information then rolls up into dashboards, so you'll have a holistic view across all your contracts, all your outstanding obligations, who they belong to, and so on. Those were the first two use cases if we go back into our presentation for a second here. We looked at the screens negotiation and the contract analysis. My apologies, I wanted to go into the screen share here. Then next, I'll expand on the Ask AI and the prompt lab capabilities. We've already seen how we can use Ask AI on an individual document to ask some additional questions that have not been covered as part of the contract analysis. But we could also broaden this to ask across a set of documents or across the entire repository. Then lastly, I will show you a Prompt Lab example. So Prompt Lab is really a way for you to bring generative AI into configurable actions throughout the contract lifecycle management stages. We have released a number of prompt templates that can be leveraged out of the box to solve any problems that you are identifying across your organization. But you can then also build your own unique prompts to help solve problems that are specific to your workflows. I'll give you an example of both. But first, let's look at the Ask AI across multiple documents. Hereby, when I go back into the platform, we navigate into our details, our company's page, and then I'm going to find a supplier or third party where I know I have a set of contracts with. Those are all consulting agreements. If we have a quick look at the document itself, you will see most of them have a section where it combines what kind of fees we are paying for different personnel in our SoW. What I want to know is just see how those compare. We can come in here, we can ask AI a question, what are your personal fees? Then it tells you exactly and instantly across all the documents that include that. And again, you get a reference to each of the documents that contains that information. So you see Ask AI can be used to ask questions across a set of documents either on the company's level or also on a contract record. If you have a legacy agreement that you've uploaded which contains a set of documents, MSA, SoW, and so on, you could also ask a set of questions across those documents. Then lastly, you can also ask questions across your entire repository. If we go into our contracts repository, you can say, Show me all contracts for Blue Turtle, which is a company that end in 2026. It will then take that prompt, run an analysis across your repository, and get you a set of documents back that you can then either export or review one by one for further analysis. Here is my list, all of those are ending in 2026. Then the last use case I want to show you today is the prompt lab. I've set up a prompt in my service agreement or generally as part of the workflow. This is a service agreement, for example, that I know I maybe had something signed similar a couple of years ago, and I just want to quickly understand some key differences across the document here that I'm currently negotiating and the one that I know I've negotiated in the past. So we build, a compare action here using a Gen AI prompt. And here, I can then come in, select the document that I want to compare it to and run this action. Then in a couple of seconds, it will highlight all the key differences for me in a quick summary that I can then either share or pass on to someone else within my organization. So this is just a summary here. So it pulls out the specific differences and then again just gives you a high level summary at the end. Other scenarios where we've seen this used in my my KB here is, for example, checking signatures. So if you have legacy contracts and you wanna upload them, make sure that there is actually a signature attached to your agreement, we can use a GenAI prompt for that. Or our out of the box templates also include, for example, validating data so you can see if the data points that have been entered by your requesters are matching up with the content of your contract attachment and so on. Any problem that you are facing can probably be solved with a GenAI prompt. This then brings us to the end of this presentation. Let me just go ahead here. I'm going to give you a couple of seconds and look at the chat and see if there's been any questions. There's one question around what are the AI tools that we use, the underlying AI that prompts are using. So in our newest release, we switched to GPT four point zero and five point zero Mini. Screens action is running on OpenAI. But yeah, we're always evaluating the market and also bringing in new AI mechanisms into the platform when new models are available. Alright. Thank you so much for joining today. If there's any further questions, please reach out to us and I'll see you at the next webinar.