In the legal-tech world, the mantra is simple; be an agent of positive disruption, and that usually means delivering products that accelerate productivity or save the profession money, but preferably both! However, as AI solutions have become ubiquitous, the disruptors have been disrupted and from a buy-side perspective this is really exciting. Consider the following scenario;

A GC is evaluating a contract analysis solutions and sees options priced from $7 to $700 a month. Meanwhile, Chat GPT’s Contract Review platform provides the key analysis features you are looking for without additional cost. The question becomes; is the premium charged for contract lifecycle management over contract analysis worth-while?

The answer will depend on the demands of the user, but one thing is clear, every legal team needs to be continually scoping out and testing solutions both to understand what is available but also to gauge whether their premium services are delivering price-performance. This is a difficult enough task but it is made all the more challenging as innovations and our own collective learning continually improve our ability to extract value from AI. A good example of this is the ability to train large language models (LLMs) though learning basic prompt engineering. Just as we learned how to get the most out of a Google search, we are now doing the same with generative AI because we are getting smarter at understanding how neural networks “think” and thereby asking better questions.

The highly dynamic nature of these forces is the source of great disruption because many tech providers who have invested heavily in building technology in-house and/or acquiring companies to fill the gaps, are often left with expensive solutions that don’t have market fit. The take-away from these forces is what legal-tech solution providers are reluctant to share namely; for most use-cases, AI is cheap and is getting cheaper!

Where is the Value in Paying for AI?

AI is not productive in a vacuum but is a tool to help you get work done so the value is not in paying for search capability but in deploying AI tools that are integrated into workflows that add value. For example, contract summary capability may not be something you want to pay for as a standalone product, but as part of a due diligence workflow in an M&A solution, the combination can be very compelling, particularly if the output is read by, and fine tunes, the next steps in the workflow. Similarly, in contract life-cycle management a well-trained clause analyzer can offer real benefits but that value rockets when the output links indemnification clause analysis with internal policies to determine anomalies and suggest remedial action.

Legal teams can get ahead of these challenges by appointing team members who spend some dedicated time each week, testing AI solutions and brainstorming results and here’s where there is a very exciting opportunity. As a generation of young lawyers are about to be marginalized by AI, there is no better fight-back plan than in helping legal teams harness the power of AI. Everyone knows its coming and wants the benefits, but at the end of the day, we will require legal engineers, op’s managers (and species yet to be invented!) to guide the choices and drive deployments.

The timing for this transition is now. The legal-tech fraternity generally provide free sandbox environments for testing and even when they don’t, they find it hard to turn down a genuine enquiry for a Proof of Concept deployment, because at the end of the day, the best way to sell a tech solution is to provide unequivocal proof that it will add measurable value. So, what are you waiting for?

This article is authored by John Matheson, Co-Founder, Quant LegalTech Pte Ltd. The article may not necessarily deal with every aspect of the topic. It is not created to provide legal or other advice. The views expressed are of the expert(s) in the article and do not necessarily reflect LawWiser’s or their employees’ opinion.