Read Webinar Transcript Below
Stella (Host): Hello and thank you for joining us today for our webinar “AI for Legal Teams, Cut through Contract chaos and navigate Complex Business Relationships” presented by Pramata and today’s general counsel. Our speakers today are James Cho, Contract Management Solution Expert at Pramata, and AC Agarwal, VP of Product at Pramata.
James: Thanks, Stella and welcome everyone to today’s webinar on AI for Legal teams. We will be discussing how your organization can use AI to cut through contract chaos and navigate complex business relationships.
Agenda & Poll Results
James: Let’s quickly go over our agenda for today. We’ll start with discussing the actual contract chaos challenge. We coined this term, but it should be pretty evident what we’re talking about as a lot of organizations face this ongoing contract chaos challenge.
Then we will dive into the three different use cases showing how AI can be used to cut through the complexity of contract chaos and how you can navigate these complex business relationships.
Finally, we will end by talking about how you can turn the chaos into a competitive advantage. For a lot of legal folks here, for general counsels, the goal is to not just automate and become more efficient, but really turn the legal department into a strategic asset for your organization.
[Poll conducted asking what slows down legal teams the most when working on complex business relationships]
James: The poll results show that 43% roughly said that comparing terms across multiple agreements often slows down your legal team. What’s common across all of these is that there’s just so much manual work involved. That’s really where the opportunity comes in with AI.
The Contract Chaos Challenge
James: Let’s discuss what the contract chaos challenge is. I outlined 6 manual processes that create contract chaos across the lifecycle. If you think about your internal processes, how does the contract move from the initial request, drafting, negotiation, the back and forth of redlining and all the way to getting it executed and then what do you do with that contract after you’ve gotten all the signatures?
Here are 6 manual processes along the way:
- Initial request and drafting – gathering all relevant information before you can even get to work
- Negotiation – researching and thinking about what we agreed to in the past with certain counterparties
- Approvals – getting buy-in from stakeholders, finance team, CFO approval for multi-million-dollar deals
- Execution – making sense of which version has all the redlines and approvals
- Obligation management – ensuring both parties fulfill their obligations post-signature
- Analysis and reporting – creating dashboards and executive level reports
The main thing is that we’re just having to do all this manual upfront work before we can even get to that task at hand.
A little personal anecdote – In one of my past roles, I actually went through our entire filing cabinet of contracts and literally read line by line, every contract and recorded it in a spreadsheet: this is the counterparty, this is the contract, these are the obligations we have to uphold. I’m sure a lot of folks are doing that now (or have done this) in some degree or fashion. These are really ripe opportunities for AI to help us augment the work that we’re able to do as a legal team.
The Reality of Contract Chaos
James: Contract chaos creates three main issues:
- Wasted effort and time – All this detective work takes away from time that could be spent on strategic priorities and initiatives. You just don’t have the manpower you need to tackle strategic initiatives because honestly, you’re just treading water at this point.
- No single source of truth – If your contracts are everywhere, you don’t have the centralized repository. Without AI we have to piecemeal all of this information manually and that creates even more chaos.
- Costly implications – Because your legal team must handle every single request, you can’t get to the strategic projects. Sometimes we do have to unfortunately resort to outside counsel to offload some of this work.
Use Case 1: High Stakes Enterprise Deal Under Pressure
James: This is one of the common questions your legal team might be facing: “They want us to review their contract, but it’s on their paper. Where do I start?”
Traditional Method vs AI-Driven Approach
James: The traditional method involves a lot of manual review, especially if this is a contract your organization has never seen before. Your lawyers will spend a lot of time manually reading through line by line and getting stakeholder input on the actual risks.
After reviewing the contract manually, you usually go through some kind of clause library. I’ve worked with Excel spreadsheets where your legal team has created what I call “a bible of clauses” – every single permutation of different clauses you’ve seen as an organization over the years.
The AI driven approach speeds up the review because the AI is going through and reading the contract. If you have a playbook or template or negotiation guidelines, it’s bouncing that new agreement against your organization’s negotiation strategies in real time.
Demo Walkthrough
AC: Let me show you how customers use Pramata to tackle this challenge.
[AC demonstrates the AI contract review process, showing how it compares third-party contracts to templates, identifies material risks, and provides auto-redlining suggestions]
AC: “I like to joke that when my kids do their math problems, if they don’t show their work, it’s hard to trust whether they are going to be able to trace their answers. So this is going through that thought process.”
[Demo shows AI analyzing contract differences, highlighting risky clauses, and providing redlining suggestions with explanations]
AC: Rather than spending hours or days trying to figure out what’s changed between this and your agreement, you’re now getting a sense of what are all the changes really quickly.
Use Case 2: Consolidating Multiple Contracts
James: For Use Case #2, the common question you might be facing is: “Hold on, we just did an inventory of our agreements with a certain customer, but we’re noticing we have two active MSAs with the customer. Which terms apply? Do any of the terms supersede each other? Can we reconcile the differences?”
You might be thinking, how does this situation even happen? Ideally, you would have one governing MSA – you shouldn’t really have two of the same MSA governing your relationship. Here are a couple of scenarios where that might happen:
- Post merger acquisition – You had an existing relationship with a customer, the company you’re acquiring or merging with also had a separate relationship, so you have two MSAs that are active with the same customer
- Legacy system issues – Different departments creating separate agreements over time
- Organizational changes – Restructuring that creates duplicate relationships
For today we are going to focus on a post-merger acquisition scenario – you had an existing relationship with a customer, and the company you’re acquiring also had a separate relationship, so you have two MSAs active with the same customer.
As a legal team we’re often trying to mitigate risks and ensure compliance. The risks include conflicting obligations, potentially putting that customer on unclear terms, and possible legal action if you don’t reconcile differences.
Traditional vs AI-Driven Approach
James: The traditional method involves manual reviewing of both MSAs, making decisions on the best clauses from both documents, and researching customer history. A lot of manual work goes into this request before you even get to the fun part of drafting that new consolidated agreement.
I remember my first instance comparing two documents – I actually printed them out. There are online text-based editors for word comparisons, but those only surface basic changes. All this manual work has to go into reviewing not just one document, but when consolidating agreements, you have to manually review both documents.
With an AI-driven approach, the system helps by:
- Removing manual steps
- Reviewing agreements line by line automatically
- Taking account history into consideration
- Extracting the best clauses from each MSA
- Drafting a new consolidated MSA
Demo Walkthrough
AC: Before you can get into these higher order use cases, having a clean repository is really important. At Pramata, we spend time up front getting all agreements from customers into a clean repository without them doing the time-consuming manual work.
[AC shows contract repository with two MSAs from Cloud Pro and acquired Cloud E Corp]
AC: You can see the lineage of how all those agreements have come together – MSAs with order forms, amendments, and their own end dates. Both are coming up for renewal in 6-7 months, so you need to consolidate them at the next renewal opportunity.
The AI is going to only be as good as what information you supply. The old “garbage in garbage out” still applies.
Instead of opening this 11-page MSA and 15-page MSA separately, you can get a quick difference comparison between these two agreements to help you get a very quick analysis of where the key changes are.
[AC demonstrates comparing two MSAs, showing key differences, and generating a consolidated agreement based on playbook recommendations]
AC: Rather than trying to change all terms, if we’ve already agreed to something in the past with this customer under two separate agreements, let’s keep those the same because that might cause more problems. Where there are discrepancies, let’s try to push for something more in line with our playbook.
[AC instructs the system to generate consolidated MSA that can be downloaded]
AC: This is meant to be a draft – we fully expect you’ll go through iterations and put it through redlining and workflows for internal approvals. But that’s a key example of how to use AI to speed up what is traditionally a very lengthy process.
Use Case 3: Amending Existing Customer Contracts
James: This third use case doesn’t really need an introduction, as it’s the bread and butter for a lot of legal teams. You have existing relationships and as these relationships grow over time, you keep executing new contracts whether it’s new amendments or new order forms. How do you keep this all straight and how do you make sure that anything new that you draft is compliant with your negotiation strategies, playbooks, and guidelines.?
The issue that AC was able to visually show in the previous example, is that as your business scales and goes through a lot of contracts with a customer, what inevitably happens is that you get this pile of files. The challenge for a legal person handling any kind of new additions or trying to make changes is that you have to go through all this manual work trying to piece together how these relationships all relate to each other.
You have this one MSA but then how do the terms in any subsequent order forms, SOWs, amendments mesh with each other? Things like amendments might override or supersede some original terms in the MSA. You might have changing payment terms, you might have modified language based on new regulations.
That is the constant contract chaos challenge that legal folks face when dealing with existing relationships – how do you keep this all straight and ensure anything new you draft is compliant?
Traditional vs AI-Driven Approach
James: Like before, we have the traditional method that involves a lot of manual investigative work to:
- Piece together the relationship history, things like parent-child relationships
- Make sure we have the most current applicable terms affected by amendments
- Often having to draft things from scratch
With an AI-driven approach, not only can we use AI to help draft the new contract, but more importantly, AI can make sure that we have all the information that we need so that we can actually tackle the task at hand.
AC: Let me show you how we handle amending existing contracts in the Pramata platform.
In this example we’ll be looking at an account inside our repository that has an MSA, amendments, and order forms. The typical scenario is: you’re being asked to add another set of users or licenses to a particular product. How can you do that quickly, but also make sure you are able to research the most prevalent and prevailing terms for that particular relationship?
Piecing that together is typically very hard to do. Here I’m going to use Pramata’s AI drafting assistant to help me generate that amendment that adds those licenses.
[AC demonstrates using AI to draft an amendment for adding licenses, showing how the system researches existing terms and pricing automatically]
AC: It’s asking if there’s any particular document I want to use as my template, so I could use the language in the MSA as my template. Let’s use the master agreement. It’s giving me a heads up – there’s also amendments in there.
Rather than having to go through and understand how these different terms apply to each other and what the order of precedence is, it’s bringing that in here for me.
[Demo shows AI asking intelligent questions about pricing, payment terms, and contract duration based on existing agreements]
AC: It’s asking: “Will the pricing for these additional licenses follow the same rate as the original cloud store order, which was $100 per user per month, or would it be different pricing?” Let’s use the same rate.
“Do you want to use the same payment terms, which is 30 days?” And “what about the term? Is it co-terminate with the existing or specify a different term?”
This is a really interesting question because as you can see we’ve got different orders ending at different times. The MSA ends in June 2026. So let’s use the same payment terms, but let’s have this order co-terminate with the master services agreement.
Just being able to quickly see that and have that flexibility makes many of those manual researching steps go away.
[System generates the amendment draft]
AC: So now it’s going to take all that information into effect and generate a new draft – because we already have a First Amendment and a Second Amendment – it’s now going to generate a Third Amendment that talks about these additional licenses, and it’s also going to make sure that it co-terminates with the MSA. As you work through this, if there’s additional elements that you want to make sure that you’re always including or format preferences, you can always customize the AI to do that, so that the first drafts come out even closer to what you want the final output to look like.
AC: Additionally, we also have this capability built into Pramata’s AI Negotiator for MS Word.
Key Benefits and Conclusion
James: What you’re able to do with AI is cut through that contract chaos and solve complex business relationships. These are some of the benefits that legal teams have experienced:
- Augment the work that we’re able to do as a legal team
- Focus on strategic priorities instead of manual detective work
- Make sure that the legal department becomes a strategic asset for your organization
- Transform from keeping the lights on to driving business value
About Pramata
Pramata is a full end-to-end contract AI solution to help you with all your contract management needs. If you have any questions, feel free to schedule a demo or reach out. Thank you!
