What if your contacts 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 Sirion, a contract lifecycle management software for in-house legal departments that’s recently been upgraded with IBM generative AI technology.
What if your contacts 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 Sirion, a contract lifecycle management software for in-house legal departments. It’s recently been upgraded with generative AI functionality from IBM designed to make it something the whole company can use, not just its legal staff. It’s so promising that IBM itself will be using the software to integrate contracts into the daily operations of the company.
Edward Adams: If your company is like most, after a contract is negotiated and signed . . .
. . . 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 Sirion, a contract lifecycle 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.
Gordon Thompson: 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.
Edward Adams: That’s Gordon Thompson, Sirion’s Executive Vice President of Pre-Sales and Business Strategy. He and his colleagues are planning a future where contacts 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.
Sirion 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 contacts.
Unlike a lot of companies, Sirion has a long history of working with artificial intelligence.
Gordon Thompson: 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 600 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.
Edward Adams: But when it wanted to improve its contract review function and turn contracts into a device to run a business, it 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 LLM. The problem is . . .
Gordon Thompson: LLMs are expensive to build and maintain. So we wanted to create a solution that was scalable, that was cost effective.
Edward Adams: To understand Sirion’s 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 the law, it’s got some history to it.
Edward Adams: For those of the audience who are not lawyers, what does redlining a document mean?
Gordon Thompson: So redlining got its name from when we used to do paper contracts, right? So, you would send a paper contract over to your counterparty, they would literally take a red pen, a red pencil, and 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.
Edward Adams: When lawyers redline by hand, it can be a slow process.
Gordon Thompson: On average, it takes about 13 minutes to review a page of a contract. Some of these contracts can be several hundred pages long. When you automate it using generative AI, that goes from days, weeks, down to minutes and seconds.
Edward Adams: To speed up the process, Sirion 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.
Edward Adams: Can you give me an example of what you mean by a playbook?
Gordon Thompson: Your preferred position might be, "I want payment in 30 days." The counterparty says that payment terms might be 60 days. So you wanna be able to identify that very quickly and make sure that you put in your preferred language.
Edward Adams: Sirion can redline even some of the longest contracts in a matter of minutes. And while human lawyers still review the software’s changes, companies have found they can go from first draft to final agreement 30 to 80 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 Sirion database. And they get answers in plain English.
Gordon Thompson: If you're a chief procurement officer and you wanna 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.
Edward Adams: That makes contracts into a tool to help manage the business, Thompson says.
The ability to use an open source LLM – something not every AI platform enables – was one of the reasons Sirion chose IBM, Thompson says. So was its strength in conversational search, and the fact that . . .
Gordon Thompson: IBM is a pioneer in the AI 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 watsonx 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.
Edward Adams: For its part, IBM is not just a supplier to Sirion, but a client of the company too, using its enormous database of contracts to help run its operations.
Gordon Thompson: IBM, they have over half a million contracts that we've ingested into the solution. So from a scale standpoint and 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.
Edward Adams: The lesson for other companies like Sirion – 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 Gargi Dasgupta, Director of Product Management and AI Leader at IBM.
Gargi Dasgupta: The lesson that we can learn from Sirion's experience is that there is data that already exists and it's super critical to democratize access to that data.
Edward Adams: Why?
Gargi Dasgupta: Because it unlocks business value and business insights that were not possible before.
Edward Adams: While small companies tend to outsource AI projects and large enterprises have teams of engineers who can take them on, midsize companies generally need to upskill their employees to take on an AI project, Dasgupta says.
Gargi Dasgupta: I would say Sirion is an exception to that rule because they've been native AI for years.
Edward Adams: When it comes to advising other companies on what to look for in an AI partner, Thompson says depth of experience is a vital consideration.
Gordon Thompson: 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 wanna make sure that they understand all the options available to you and apply the appropriate technology or subdiscipline of AI to your business case.
Edward Adams: But don’t let the challenges of deploying generative AI convince you to put off the work, Thompson warns.
Gordon Thompson: 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 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 gonna fall so far behind that it'll be tough to catch up.
Edward Adams: This has been “The ROI Rules of AI,” a podcast from IBM and Bloomberg Media Studios.
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I’m Edward Adams, thanks for listening.