Speaker 1: Since you're a subscriber to this Bloomberg podcast, we thought
Speaker 1: you'd be interested in a new four episode sponsored podcast
Speaker 1: called The ROI Rules of Ai, produced by IBM and
Speaker 1: Bloomberg Media Studios. It explores how business leaders are thinking
Speaker 1: about the return on investment of artificial intelligence projects. You
Speaker 1: can subscribe wherever you listen to your favorite podcasts. Here's
Speaker 1: a recent episode. If your company is like most, after
Speaker 1: a contract is negotiated and signed, congratulations, it gets filed
Speaker 1: away in a lawyer's office, not to be consulted again
Speaker 1: until it has run its course and needs to be renewed.
Speaker 1: But what if your contracts could be a roadmap to
Speaker 1: running your business the first step in managing your relationships
Speaker 1: with your suppliers and clients. That's the function of Syrion,
Speaker 1: a contract life cycle management software for in house legal departments.
Speaker 1: It's recently been upgraded with generative AI functionality designed to
Speaker 1: make it something the whole company can use, not just
Speaker 1: its legal staff.
Speaker 2: It gives you real time information on how to run
Speaker 2: your business and gives you the insights you need to
Speaker 2: pivot so that you can make the best decisions with
Speaker 2: the best data at the right time.
Speaker 1: That's Gordon Thompson, Syrian's executive vice president of pre sales
Speaker 1: and business strategy. He and his colleagues are planning a
Speaker 1: future where contracts are integrated into a company's day to
Speaker 1: day operations. From IBM and Bloomberg Media Studios, this is
Speaker 1: the ROI Rules of AI and I'm your host Edward Adams.
Speaker 1: On this podcast, we're exploring how companies of all sizes
Speaker 1: are using AI to remake their operations, increasing their return
Speaker 1: on investment and that of their customers. Syrian has been
Speaker 1: in business for a decade. It enables companies to digitize
Speaker 1: their universe of contracts into a single database, extracting metadata
Speaker 1: like specific clauses and obligations to make them searchable. It
Speaker 1: also helps lawyers draft new contracts, making sure that the
Speaker 1: terms limit their company's risk, and it enables a general
Speaker 1: counsel's office to measure the company's performance against those contracts.
Speaker 1: Unlike a lot of companies, Syrion has a long history
Speaker 1: of working with artificial intelligence.
Speaker 2: From our inception, we were using natural language processing. As
Speaker 2: the technology evolved over the years, we started using machine learning.
Speaker 2: We've built over six hundred small and medium sized language
Speaker 2: models that are purpose built to extract very specific pieces
Speaker 2: of data out of contracts at a very high precision rate.
Speaker 1: But when it wanted to improve its contract review function
Speaker 1: and turn contracts into a device to run a business,
Speaker 1: needed to employ generative AI. It had to move from
Speaker 1: simply copying and pasting an approved clause in place of
Speaker 1: a riskier one to rewriting the proposed contract as a
Speaker 1: lawyer might do. That meant using a large language model
Speaker 1: or LM. The problem is.
Speaker 2: Lms are expensive to build and maintain, so we wanted
Speaker 2: to create a solution that was scalable, that was cost effective.
Speaker 1: To understand Syrian solution, it helps to first understand what
Speaker 1: contract lawyers actually do. Lawyers call reviewing a contract redlining,
Speaker 1: and like a lot of things in the world of law,
Speaker 1: it's got some history to it. For those of the
Speaker 1: audience who are not lawyers, what does redlining a document mean?
Speaker 2: So redlining got its name from when we used to
Speaker 2: do paper contracts, right, so you would send a paper
Speaker 2: contract over to your counter party. They would literally take
Speaker 2: a red pen or red pencil, and that they would
Speaker 2: review it and strike through it and make changes. That
Speaker 2: name has really persisted into the digital age. Ultimately, redlining
Speaker 2: it boils down to making sure that you identify issues, risk, compliance,
Speaker 2: terms and conditions that meet your ultimate goals as an organization.
Speaker 1: When lawyers redline by hand, it can be a slow process.
Speaker 2: On average, it takes about thirteen minutes to review a
Speaker 2: page of a contract. Some of these contracts can be
Speaker 2: several hundred pages long. When you automate it using jenerative
Speaker 2: AI that goes from days weeks down to minutes and seconds.
Speaker 1: To speed up the process, Syrian used an open source
Speaker 1: LLM and a series of proprietary playbooks which spell out
Speaker 1: a company's default positions on a variety of legal issues.
Speaker 1: Can you give me an example of what you mean
Speaker 1: by a playbook?
Speaker 2: Your preferred position might be I want payment in thirty days.
Speaker 2: The counterparty says that payment terms might be sixty days,
Speaker 2: So you want to be able to identify that very
Speaker 2: quickly and make sure that you put in your preferred language.
Speaker 1: Syrian can redline even some of the longest contracts in
Speaker 1: a matter of minutes, and while human lawyers still review
Speaker 1: the software as changes. Companies have found they can go
Speaker 1: from first draft to final agreement thirty to eighty percent faster.
Speaker 1: Thompson says, once those contracts are signed, it took another
Speaker 1: form of AI to open up a company's database of
Speaker 1: contracts to the wider organization. IBM's conversational search technology allows
Speaker 1: designated people in a company's lines of business to ask
Speaker 1: questions of the Syrian database and they get answers in
Speaker 1: plain English.
Speaker 2: If you're a chief procurement officer and you want to
Speaker 2: understand a certain piece of data around your supplier contracts,
Speaker 2: it empowers you to go out and get that information
Speaker 2: versus having to rely on legal to go get that
Speaker 2: information for you.
Speaker 1: That makes contracts into a tool to help manage the business.
Speaker 1: Thompson says the ability to use and open source LM,
Speaker 1: something not every AI platform enables, was one of the
Speaker 1: reasons Sirion chows IBM, Thompson says, So was its strength
Speaker 1: in conversational search and the fact.
Speaker 2: That IBM is a pioneer in the I space, which
Speaker 2: is a big plus for us. They bring a lot
Speaker 2: of thought leadership to AI and generative AI. We were
Speaker 2: looking at tools that we could implement on We decided
Speaker 2: that Watson X was one of those tools that we
Speaker 2: could really go to market with and build a relationship
Speaker 2: with IBM. They are open architecture, so we can use
Speaker 2: other types of technologies inside of the Watson platform and
Speaker 2: governance with using their Watson X platform.
Speaker 1: For its part, IBM is not just supplier to Syrion,
Speaker 1: but a client of the company too, using its enormous
Speaker 1: database of contracts to help run its operations.
Speaker 2: IBM they have over half a million contracts that we've
Speaker 2: ingested into the solution. So from a scale standpoint, making
Speaker 2: sure that our generative AI solution can stand up to
Speaker 2: that scale has been a great journey that we've gone
Speaker 2: on with IBM.
Speaker 1: The lesson for other companies like Syrion companies which are
Speaker 1: neither startups nor enormous enterprises, but part of the vast
Speaker 1: middle of American businesses, is that they probably have the
Speaker 1: data they need to get started with generative AI, says
Speaker 1: Garghie Dasgupta, director of Product Management and AI leader at IBM.
Speaker 3: The lesson that we can learn from Syrians experience is
Speaker 3: that there is data that already exists, and it's super
Speaker 3: critical to democratize access to that data. What because it
Speaker 3: unlocks business value and business insights that were not possible before.
Speaker 1: While small companies tend to outsource AI projects, and large
Speaker 1: enterprises have teams of engineers who can take them on.
Speaker 1: Mid sized companies generally need to upscill their employees to
Speaker 1: take on an AI project.
Speaker 3: Desk Grouped says, I would say Syrian is an exception
Speaker 3: to that rule because they've been native AI for yours.
Speaker 1: When it comes to advising other companies on what to
Speaker 1: look for in AI partner, Thompson says, depth of experience
Speaker 1: is a vital consideration.
Speaker 2: AI is not just one type of technology, right. There's
Speaker 2: lots of different ways to implement AI. Organizations that don't
Speaker 2: have that historical background typically have a myopic view of
Speaker 2: how to leverage AI. You want to make sure that
Speaker 2: they understand all the options available to you and apply
Speaker 2: the appropriate technology or sub discipline of AI to your
Speaker 2: business case.
Speaker 1: But don't let the challenge of deploying generative AI convince
Speaker 1: you to put off the work.
Speaker 2: Thompson warns, start small, think big, go fast. My advice
Speaker 2: would be, don't be afraid of the technology. Make sure
Speaker 2: that you're investigating it, doing your homework, getting smart around
Speaker 2: what the use cases are that generative AI can deliver
Speaker 2: to you. But stick your toe in the water. Generative
Speaker 2: AI is moving literally at the speed of light. We
Speaker 2: see new innovations coming out literally every day. So if
Speaker 2: you're a laggard and you don't start looking at how
Speaker 2: you can use generative AI, you're going to fall so
Speaker 2: far behind that it'll be tough to catch up.
Speaker 1: This has been the ROI Rules of Ai, a podcast
Speaker 1: from IBM and Bloomberg Media Studios. If you like what
Speaker 1: you hear, subscribe and leave us a review. I'm Edward Adams,
Speaker 1: Thanks for listening.
Speaker 2: Why You'll love me