By Jan Van Hoecke, VP of AI Services, iManage
AI has been with us for decades, but the arrival of large language models (LLMs) has fast-tracked the ability to actually put it to use – and that’s good news for knowledge workers in a range of fields, from finance and legal to human resources and other professional services.
The result is that AI use cases are expanding in a variety of knowledge work settings – accelerating daily workflows and enabling professionals to do their jobs smarter, faster, and safer.
Streamlining side letters
Let’s start with an example from the private equity world. At its core, a private equity company’s mission is to invest in companies using money that has been entrusted to them by investors.
However, that money is rarely handed over with no strings attached. The investors might include restrictions on what type of companies their money can be invested in – for example, no fossil fuel companies, or no tobacco companies.
These restrictions are detailed in the side letter that gets appended to each investor’s investment contract. For a large private equity firm, that means hundreds or even thousands of different side letters detailing specific investment restrictions and requirements.
When the private equity firm gets ready to place a new investment by buying a new company, they need to carefully review those side letters to see which specific “pots” of money they can use to fund the investment, as part of ensuring that they are not violating any of the restrictions from their various investors.
Today, this review is largely a manual process that requires an outside law firm to analyze hundreds of documents and prepare a report. It’s not just a time-consuming and expensive task, it’s also a tedious one for the people performing the work.
AI offers the ability to streamline and automate this process.
Imagine if the investment agreements and side letters were all neatly categorized and organized in a centralized location, and the firm used a ChatGPT-type interface to ask the AI to identify any restrictions that were relevant to the investment the private equity firm was about to make.
The results would be served up quickly, generating huge time and cost savings while enhancing the ability to uncover any red flags hiding in the side letters. As a result, private equity firms can better ensure they are in compliance with their contractual obligations to their clients, minimizing overall risk.
A gamechanger for contract review
We can see a similar dynamic at play in a corporate legal department. The general counsel and their staff will need to review negotiated contracts between the company and their customers – as well as their vendors – to check for the presence of certain specific items in those contracts.
For example, in a lease agreement review, they might be most interested in what the legal use of the space is. AI can automatically summarize this information from the document (“the premises may be used and occupied only by entity XYZ, and only for the purposes of ABC”) and present it to the legal team, so that they can make sure there are no issues that require further attention.
A human resources context also provides fertile ground for AI. Imagine that a corporate human resources professional is tasked with looking at the last six months of employee agreements and reporting on salary trends and whether they include a termination clause.
The HR professional can run a search for employee AND agreement, then filter by appropriate subsets and date ranges. This gets them a collection of documents, but completing the assignment means reviewing them one by one.
Again, AI can step in here, automatically extracting salary, determining whether or not there is a termination clause, and then summarizing the termination clause. From there, it’s easy to export the results, creating a highly accurate and comprehensive report for review by colleagues.
Goodbye “grunt work”, hello high value
What we see in these examples is that AI offers a tremendous opportunity to automate some of the more routine aspects of knowledge work so that professionals can focus on higher-value activities.
There’s a historical echo here of the arrival of calculators in the engineering world. Calculators helped greatly accelerate tasks that were largely “grunt work.” Engineers generally don’t want to spend their day performing calculations – they want to think about the problem they’re trying to solve and how best to go about it.
Similarly, AI offers a way for knowledge workers to streamline and accelerate manual and time-consuming aspects of their work so that they can focus their time and energy elsewhere. In this way, AI provides a path towards deeper, more productive, and – ultimately – more rewarding knowledge work.
