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a recent episode. If your company is like most, after a contract is negotiated and signed, congratulations, it gets filed away in a lawyer's office, not to be consulted again until it has run its course and needs to be renewed. But what if your contracts could be a roadmap to running your business the first step in managing your relationships with your suppliers and clients. That's the function of Syrion, a contract life cycle management software for in house legal departments.
It's recently been upgraded with generative AI functionality designed to make it something the whole company can use, not just its legal staff.
It gives you real time information on how to run your business and gives you the insights you need to pivot so that you can make the best decisions with the best data at the right time.
That's Gordon Thompson, Syrian's executive vice president of pre sales and business strategy. He and his colleagues are planning a future where contracts are integrated into a company's day to day operations. From IBM and Bloomberg Media Studios, this is the ROI Rules of AI and I'm your host Edward Adams. On this podcast, we're exploring how companies of all sizes are using AI to remake their operations, increasing their return on investment and that of their customers. Syrian has been
in business for a decade. It enables companies to digitize their universe of contracts into a single database, extracting metadata like specific clauses and obligations to make them searchable. It also helps lawyers draft new contracts, making sure that the terms limit their company's risk, and it enables a general counsel's office to measure the company's performance against those contracts. Unlike a lot of companies, Syrion has a long history of working with artificial intelligence.
From our inception, we were using natural language processing. As the technology evolved over the years, we started using machine learning. We've built over six hundred small and medium sized language models that are purpose built to extract very specific pieces of data out of contracts at a very high precision rate.
But when it wanted to improve its contract review function and turn contracts into a device to run a business, needed to employ generative AI. It had to move from simply copying and pasting an approved clause in place of a riskier one to rewriting the proposed contract as a lawyer might do. That meant using a large language model or LM. The problem is.
Lms are expensive to build and maintain, so we wanted to create a solution that was scalable, that was cost effective.
To understand Syrian solution, it helps to first understand what contract lawyers actually do. Lawyers call reviewing a contract redlining, and like a lot of things in the world of law, it's got some history to it. For those of the audience who are not lawyers, what does redlining a document mean?
So redlining got its name from when we used to do paper contracts, right, so you would send a paper contract over to your counter party. They would literally take a red pen or red pencil, and that they would review it and strike through it and make changes. That name has really persisted into the digital age. Ultimately, redlining it boils down to making sure that you identify issues, risk, compliance, terms and conditions that meet your ultimate goals as an organization.
When lawyers redline by hand, it can be a slow process.
On average, it takes about thirteen minutes to review a page of a contract. Some of these contracts can be several hundred pages long. When you automate it using jenerative AI that goes from days weeks down to minutes and seconds.
To speed up the process, Syrian used an open source LLM and a series of proprietary playbooks which spell out a company's default positions on a variety of legal issues. Can you give me an example of what you mean by a playbook?
Your preferred position might be I want payment in thirty days. The counterparty says that payment terms might be sixty days, So you want to be able to identify that very quickly and make sure that you put in your preferred language.
Syrian can redline even some of the longest contracts in a matter of minutes, and while human lawyers still review the software as changes. Companies have found they can go from first draft to final agreement thirty to eighty percent faster. Thompson says, once those contracts are signed, it took another form of AI to open up a company's database of
contracts to the wider organization. IBM's conversational search technology allows designated people in a company's lines of business to ask questions of the Syrian database and they get answers in plain English.
If you're a chief procurement officer and you want to understand a certain piece of data around your supplier contracts, it empowers you to go out and get that information versus having to rely on legal to go get that information for you.
That makes contracts into a tool to help manage the business. Thompson says the ability to use and open source LM, something not every AI platform enables, was one of the reasons Sirion chows IBM, Thompson says, So was its strength in conversational search and the fact.
That IBM is a pioneer in the I space, which is a big plus for us. They bring a lot of thought leadership to AI and generative AI. We were looking at tools that we could implement on We decided that Watson X was one of those tools that we could really go to market with and build a relationship with IBM. They are open architecture, so we can use other types of technologies inside of the Watson platform and governance with using their Watson X platform.
For its part, IBM is not just supplier to Syrion, but a client of the company too, using its enormous database of contracts to help run its operations.
IBM they have over half a million contracts that we've ingested into the solution. So from a scale standpoint, making sure that our generative AI solution can stand up to that scale has been a great journey that we've gone on with IBM.
The lesson for other companies like Syrion companies which are neither startups nor enormous enterprises, but part of the vast middle of American businesses, is that they probably have the data they need to get started with generative AI, says Garghie Dasgupta, director of Product Management and AI leader at IBM.
The lesson that we can learn from Syrians experience is that there is data that already exists, and it's super critical to democratize access to that data. What because it unlocks business value and business insights that were not possible before.
While small companies tend to outsource AI projects, and large enterprises have teams of engineers who can take them on. Mid sized companies generally need to upscill their employees to take on an AI project.
Desk Grouped says, I would say Syrian is an exception to that rule because they've been native AI for yours.
When it comes to advising other companies on what to look for in AI partner, Thompson says, depth of experience is a vital consideration.
AI is not just one type of technology, right. There's lots of different ways to implement AI. Organizations that don't have that historical background typically have a myopic view of how to leverage AI. You want to make sure that they understand all the options available to you and apply the appropriate technology or sub discipline of AI to your business case.
But don't let the challenge of deploying generative AI convince you to put off the work.
Thompson warns, start small, think big, go fast. My advice would be, don't be afraid of the technology. Make sure that you're investigating it, doing your homework, getting smart around what the use cases are that generative AI can deliver to you. But stick your toe in the water. Generative AI is moving literally at the speed of light. We
see new innovations coming out literally every day. So if you're a laggard and you don't start looking at how you can use generative AI, you're going to fall so far behind that it'll be tough to catch up.
This has been the ROI Rules of Ai, a podcast from IBM and Bloomberg Media Studios. If you like what you hear, subscribe and leave us a review. I'm Edward Adams, Thanks for listening.
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