With its claims to disrupt and transform many areas of our lives, AI is now part of our day to day vocabulary.
With its claims to disrupt and transform many areas of our lives, AI is now part of our day to day vocabulary. In the world of commercial contracts, solutions based on automation, machine learning and other forms of AI promise to strip out inefficiencies and increase accuracy and value in the contracting process – but to what extent is this technology already in use?
In the last couple of years there has been an explosion of products onto the market promising to transform the contracting process. For example, AI can assist with contract drafting and negotiation; it can review and extract data from large numbers of contracts and then analyse that data to present a picture of contractual risks and opportunities across the organisation, as well as the fundamentals such as document storage and automated alerts for key dates and obligations during the contract life cycle. The speed and accuracy with which these tasks can be carried out is exciting and should make the contracting process more efficient, freeing up time for legal departments and contract managers to be more productive and focus on higher value activities.
Apart from selecting the most appropriate solution (no mean feat, given the sheer number of products in this area) the main challenges of making the transition to AI are the initial investment and the amount of effort and data needed to 'train' the technology. Depending on how narrow the domain is for which the AI system will be used (e.g. just property leases, rather than many different types of contract) a limited data set might consist of 500+ contracts. Small to medium-sized organisations may simply not have this volume of contracts so the ability of the technology to learn what you consider to be a risky contract provision, for example, could be limited; although contract analysis is just one aspect of the tools available.
For organisations with very large volumes of contracts, it’s a no-brainer. JP Morgan made headlines in 2017 by developing and implementing its own machine learning software to review and interpret thousands of loan agreements, which it does in a fraction of the time it took to do manually. The large number of agreements available as a data set plus the low variability in contract type and complexity was clearly enough to justify the bank’s substantial investment in this technology.
Of course, this is not to say that AI has nothing to offer the small to medium size organisation. There are so many products out there and the technology is improving all the time. Estimates vary but many believe the next five years will be the tipping point in terms of widespread adoption of AI technology.
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