Please enjoy this blog posted on behalf of the author, Jack Shepherd, Legal Practice Lead, iManage.
2018 perhaps marked the peak of the first wave of interest in AI in the legal industry. Much of these efforts were focused around contract review and due diligence, but extensive discussion also took place around the relevance of these tools for knowledge management. That year marked a decision point for many law firms: what was their long-term knowledge strategy? At least three types of strategies were pursued.
The first was to get seduced by AI. Vendors would come in with messages that AI could solve all your knowledge management problems, delivering a Google-like search that would mine your firm’s data and deliver your firm’s crown jewels on a platter. Of course, when these kinds of messages are delivered, people listened. But understanding of the underlying technologies was often vague, and people often confused AI with magic. As a result, these firms are largely nowhere further now than they were in 2018.
The second was to ignore the AI. This might be because firms did not have a culture of pursuing new innovations, because they had been burned in the past, or because there were other risks that meant this was simply not a viable option. These kinds of firms continued to build knowledge repositories in the same way as before, with no real plan to change. These firms are, by definition, in the same place they were in 2018.
The third was to understand what effect AI might have, where it might add value and act accordingly. Firms in this camp largely reached the conclusion that the key use cases of AI in knowledge management were largely related to capturing, structuring and finding knowledge. They realised that the success of AI tooling depended on having well-organised and labelled content: “data hygiene”. These firms have made significant advances since 2018 because they have done the hard work that can unlock the power of modern technologies.
Move forward to 2023, and we are at another similar decision point because of language learning models such as GPT, which appear to be a huge step-change in AI capabilities.
But things have changed since 2018. Since then, firms in the first group who fell to the seduction of AI might have been burned by failed implementations and realised their mistakes. Firms in the second group might have experienced a renewed focus on innovation, thanks to cloud infrastructure or new priorities in their organisations.
What’s changed since 2018 is that many firms have understood the key components of a successful knowledge management strategy, namely: (1) culture (to inspire people to share and find), (2) process (to share and find content), (3) content (the vehicle through which knowledge is delivered), and (4) metadata (the means through which content is organised).
Whereas in 2018, conversations were largely pivoted around AI capabilities, more and more conversations are now centred around these four aspects, and how AI can support them.
There are undoubtedly organisations who are still in the first group. Often, those in this group do not have the resources to implement the four components of a knowledge management strategy. As a result, messages such as “AI can solve everything for you” often land well. But whether or not these messages become true or not is a different question.
The smartest organisations are spending time developing content and using industry best practices around structuring it properly. They are focused on the business outcomes knowledge management delivers, and are engaging with firm leaders to ensure their culture supports this. Over time, we will see fewer demands around things being “AI-powered”, and more demands of specific processes and needs being met in an optimal way – whether AI-powered or not.