Transcript for "AI-Enhanced Obligation Management":
Awesome. Thank you so much. Hi, everybody. Austin here. I'm gonna go ahead and share my screen here, and we'll take a look at our agenda for our webinar today. So today, we're gonna be talking about how Agiloff can help you automatically extract obligations, from contracts using AI. But the really cool thing here is that we're not just going to be focusing on extracting those obligations. We're also gonna be focusing on the rest of sort of the life cycle of an obligation and how you can make that really useful, in your organization in multiple ways through assigning obligations as tasks to individuals, flowing them into reports and dashboards, and even sending that data to other enterprise systems using Agiloft's integration hub. So there's a lot of really cool stuff we're gonna see kind of all come together in concert in Agiloft today. So I think obligation management and, you know, how we're using AI to extract those obligations is a really great example of why Agiloft as a data first platform, is really the right way to go when it comes to CLM and managing your contracts. Now before we actually jump right into extracting obligations, I actually wanna kind of set the stage and give some context around this by reminding those who, you know, may or may not be familiar about screens, Agiloft's AI automated contract review. So screens allows me to take a playbook, and that might be my own playbook or a community playbook written by the community of experts that Agiloft has worked with to, develop a fantastic set of expert crafted playbooks. And I can use that playbook to help me analyze a contract and automatically bring it back into compliance with AI generated redlines. So in this quick example, I might take this, you know, payment terms extension playbook standard. You know, the standard in the playbook says the contract needs to provide the purchaser net forty five days or more. The AI identified this failed, reasoned why, and generated a red line for me that I can quickly and easily apply to my document. So screens is a fantastic and powerful AI engine that, you know, at first at Agiloft, we used for this great contract review and redlining, but now we are we are expanding the usage and application of screens into this obligation extraction and management. So I wanted to kind of start there with a quick reminder about screens to set the stage a little bit and help you understand that we're really using cutting edge AI technology to achieve this obligation management capability. Now I could run one of these obligation extraction playbooks or screens right from within the word the screens word add in here, and you can see we've got a number of people contributing to the community obligation screens, Maya from Agiloft, Christian, as and a a variety of other people here, which is really fantastic. We're we're building a nice library of obligation screens to help you extract from. But one of the cool advances that we've made around screens is that I no longer have to run those directly from within Microsoft Word. So if we switch over into my Agiloft system here, we'll be able to see that I have the ability to jump into my contract repository, open up an individual contract. You know, in this case, I've got a contract that's in signed status, and, you know, maybe now that I have a signed version, I'm ready to extract obligations. And I can actually run a screen from a button here in Agiloft. Now, full disclosure, this is coming in November, so y'all are getting a little bit of a preview today. But that ability to extract screens, I'm sorry, to run screens from within Agiloft either, you know, from a button or from an automated, an automated workflow is coming to Agiloft, and I can go ahead and click my extract obligations button. It's gonna run on this document, and that does take a minute or so so I get a little message letting me know that the screen is going to take a minute to run. In the meantime, we'll jump over to an example contract here where I already have some obligations extracted. And this is what the end result would look like once you extract those obligations from a contract by running a screen from within Agiloft. So as you can see, it's broken down by a few key bits of information. We have our topic and subtopics. So these are kind of the concepts of obligations that were extracted. And then we have the actual specific obligation itself. You know? So it's usually in the form of a question and answer. So does the document contain an an accrued rights and obligations clause? No. And then there's a pinpoint citation that identifies the actual text in the document for us, where this answer was sourced from. So I think that's a really powerful element of a lot of Agiloft AI is that it's all cited and sourced. We know a lot of people aren't feeling confident to just trust everything the AI tells them about these contract documents. So we love to be able to cite and source a lot of this information whenever possible. I'm gonna scroll down through some of my obligations here, and we can look at, you know, some of the really, common ones that people like to extract, specifically financial obligations. You can see I've got elements here around service level penalties. So, say, I asked, you know, are there service level penalties or credits contained in the agreement? Yes. And then, again, our pinpoint citation is highlighting specifically those items. What are the payment terms? Again, thirty days with our pinpoint citation. So there's a lot of really great answers and obligations that were found by screens when it did that obligation extraction. Now that's kind of that first step. Right? So we can use one of those community obligation playbooks to automatically extract those obligations from a document, and then they're populated into Agiloft as metadata. This makes them searchable, reportable, but it also makes them actionable. And that brings us to our second topic, which is how we would help you quickly triage and assign those obligations. So, again, you can see me, you know, reviewing them quickly and easily just by kinda scrolling through here. But if I wanted to assign these out to an individual to actually help them take action on them, I could select, you know, one or multiple obligations here, and I could create an actual to do for this obligation. So I'll go ahead and run that, and it's going to quickly create me some tasks that are now tracked against my contract. And I have the ability to either auto manually, in this case, I'm gonna do it manually, assign individuals. You know? So for our service level, you know, I might assign that to the finance team. For our payment terms, I might also assign that to the finance team or maybe the contract management team. I could also assign it to an individual person if I like, and I could set a due date as well. And this could be a one time or a recurring due date as necessary. So in this example, like I said, I manually set these tasks that were created from my obligations to specific teams and to due dates. But you could also, in the background of Agiloft, define based on topic and subtopic, what teams or individuals these automatically get assigned to. So, again, that automated workflow of Agile is going to enable us to make sure that, these get triaged, assigned, and that our assignees get the proper notifications and the ability to, you know, come in and mark these as complete, as necessary. So when my due date comes up, you know, my finance team might get a notification. Hey. Check to make sure that the uptime on the service was at a certain level, and if not, request an SLA, compensation for that. Now just to kinda pitch a little vision here that I find super interesting, there are much deeper ways to get cool benefits out of this. For example, if you did have a software as a service uptime SLA that was extracted as an obligation, Maybe you use a monitoring tool like Splunk, and you pull data from Splunk into Agiloft to determine if that, software was in fact available above or below, you know, your four nines or your five nines of availability. And in that way, Agiloft could rather than notifying someone to check to make sure you had a certain availability, it could actually tell you, yes or no, you fell above and below. And if so, here's what you are entitled to. So we're seeing a lot of great opportunity when it comes to integrating this information into other systems, and I'll actually go a bit deeper into that in just a moment. But, you do have the option to either have people manually assess whether you're, you know, owed something out of an obligation or you have to deliver something, or you could potentially look at some detailed integrations to automate that process. And that's where we really start to foresee, some more hard ROI out of CLM. You know, of course, we know it brings tons of fantastic efficiency to our contracting teams of our customers. But when we would have the ability to actually say, you know, we can make sure that you're getting x amount of dollars back on your SLAs that you would otherwise probably just not be chasing down, that starts to turn into some some really cool benefits coming out of CLM. Alright. So what we've seen so far is we took a contract document, we extracted obligations from it, and we turned them into tasks that we could assign to individuals. Agiloft, of course, does have detailed reporting as well. So all of those tasks could flow into an obligations management dashboard like we are seeing here, where we can easily see the obligation distribution by, you know, type. We can see we have a lot around early termination penalties, SLA penalties, and we can also have quick views into some of our key obligations and how they need to be actioned. So having that information available in reports and dashboards is super useful. And another great element of Agiloft reporting is the ability to automatically deliver scheduled reports right to people's inboxes. So you could have your list of the top 10, you know, upcoming due obligations come to your inbox, you know, every week automatically in a set and forget manner. So being able to turn those obligations into structured data really enables our ability to report and dig into that content. From here, we'll jump back into our agenda, and we'll look at our next topic, the ability to send key obligation data to other enterprise systems. Now as I mentioned before, I kind of already sketched out one example of how you could use data from a monitoring tool like Splunk to determine if you are going to, actually deserve, an SLA credit. Maybe if the uptime uptime of a certain service wasn't high enough. But there are other, great approaches you could take. I'll give you another example that I've got here, and I'll jump into my other Agiloft instance here. Here, we're looking at the Agiloft Integration Hub, which is powered by Workato. The Integration Hub allows me to create no code integration recipes with, other enterprise systems by utilizing the 1,400 plus prebuilt connectors, and that makes it very, very easy to connect to other systems. So, of course, there's a prebuilt connector for Agiloft. There's connectors for other systems as well. And you can always go to workado.com/integrations to search all of those connectors available to to help understand what type of, systems Agiloft can connect to through the integration hub. In this example, I have another simple integration recipe where we're saying, you know, when a new obligation with an SLA policy is created in Agiloft, then we can create a corresponding SLA policy in ServiceNow requiring an appropriate response time. Right? So, you know, maybe we sign, a contract with someone, and in that contract, we write that we need to respond to their tickets within twenty four hours. So using an integration like this, we can recognize that, response SLA in the contract. And in our ServiceNow, we can create an SLA policy that will help to make sure that that response time requirement is actually followed. So, again, we really see this integration approach as the way to operationalize these elements of the contract, which far too often kind of go unnoticed or kind of just are allowed to to pass by or, you know, only become noticed when you kinda get punished for them after the fact. Here, we are proactively operationalizing them via integration. So that's a really cool, another opportunity to take those SLAs we're automatically extracting and kinda bring them into the real world. Finally, we're gonna look at our last topic, which is, you know, how do we get this working really quickly? And that comes back to those community screens. So one of the fantastic elements of screens is this wide array of community screens that are available through a variety of different, contract categories. So you can see those across the top here, you know, whether it's vendor contracts, m and a, commercial real estate, or health care. There's a wide variety of categories that these are available to, and you can always come to screens.ai/community to kinda take a look at these available community screens. So I might, you know, jump to some of my commercial real estate ones, but, you know, for the purpose of our conversation today, there are some fantastic obligation screens that are available. And you can see they're broken down by kind of the type of obligations you're gonna be pulling out of a document, whether it's delivery, insurance, regulatory obligations. And at any time, you can kinda come in here and open up one of these and see the details of this screen, written by Priyanka from our team, and you can see what it was designed for, kinda break down the purpose, some of the limitations, and I really think that being provided that information about those obligations, you know, really gives you that comfort and understanding of what the goal is and and what you can expect that to extract as you are going through. So just to recap on everything that we have covered today, Agiloft is making powerful new use of our Screens AI Automated Contract Review to break out of just the, you know, AI redlining into a deeper obligation extraction use case. Screens is used to extract those obligations and to populate that data automatically into Agiloft, where it can be operationalized in a number of ways. It can be operationalized by assigning those obligations to individuals and setting due dates. It can be, brought to the forefront through reports and dashboards, including automatically scheduled and delivered reports and dashboards. And it can be easily sent to other enterprise systems using the Agiloft Integration Hub powered by Workato. All of this is built on a great library of community screens, that support extracting a wide variety of obligations as well as, of course, all of the other community screens that can be used for redlining and reviewing contracts. So with that, Agiloft has really brought to bear a comprehensive approach to obligation management from extraction to execution in order to make sure that, you know, when you sign a contract and commit to doing something, you have the technology to ensure that you're actually getting that done. Alright. Thank you all so much for joining our obligations, management webinar today. If there are any questions