Nobody reads contracts for fun. Research published by the New York Times found the average length for a computer software contract is over 74,000 words, which is around the same length as the first Harry Potter book.
And unlike Harry Potter, software contracts don’t tend to have a colourful cast of characters and a series of magical antics to keep you entertained. Instead, contracts tend to contain a hefty dose of “legalese”, which can be taxing to read and often easy to misinterpret. Not exactly a page turner.
Analysing contracts is undoubtedly one of the most cumbersome tasks that business owners must undertake, but the job itself is also hugely important. Close scrutiny helps businesses to reduce risk, improve relations with partners, and understand exactly what they are agreeing to with every collaboration and investment.
This is why contract analysis machine learning has been so revolutionary. We’re going to take a closer look at some of the main challenges presented by manual contract analysis and see how automated analysis can help to resolve them.
What are the challenges of manual contract analysis?
Of course, one of the greatest issues involved in contract analysis is time. There’s no getting around the fact that combing through great volumes of very dry text can take hours, if not days, and takes a huge amount of concentration and willpower.
And the cost of these efforts isn’t just based on time. Research published by Commitment Matters found that for long and complicated agreements, the resources involved in analysing contracts can run up costs of up to hundreds of thousands of pounds.
It’s all too easy for agreements to be held up by contract reviews and analyses, issues that can ultimately stifle profits and revenues.
No matter how thorough or dedicated you are, at the end of the day we are all human, and contracts are extremely vulnerable to human error and bias. This means that the results of any analysis may not provide their expected business value if carried out manually.
Understanding contract analysis machine learning
Over the past few years, we’ve seen a huge rise in the use of technologies like artificial intelligence in business. This has offered huge potential for contract management and analysis, with companies like Cortical.io offering cutting edge contract intelligence solutions to forward-thinking organisations.
Artificial intelligence, or AI, doesn’t just refer to machine learning of course. It is a full spectrum of intelligent machines and machine functions that are capable of taking on tasks usually performed by humans. Machine learning refers specifically to a machine’s ability to use algorithms and models to learn new functions. With each new result, the functionality improves, rather than relying on software updates.
The benefits of contract analysis machine learning
Machine learning has the potential to support business contracts at every stage, from creation to analysis. Contract drafting and negotiation relies heavily on sophisticated understanding of risks and opportunities on both sides of the agreement. It requires the skills to craft a document that covers all the necessary areas. In the future, machine learning can assist by leveraging the results of previous contracts to optimise the language used and the terms outlined. As this develops, machine learning can create stronger contracts over time with minimal time and effort required.
Time and effort are the key terms here, as this is exactly what machine learning analysis aims to save you, all while providing highly accurate results which are free from human error. Machine learning helps us extract better insights from contract reviews and make suggestions which can support the creation of greater benefits from future agreements.
