Our recent publication The Tech-Enabled Lawyer: The evolution of the legal function discussed in-house legal teams’ changing attitudes to technology. As the world recovers from the pandemic, and industries return to business as usual, in-house teams will continue their transformation journeys. We are entering a period of accelerated innovation, with 82% of in-house legal teams increasing their adoption of legal technology; although, adoption will be a staged approach as lawyers increase their digital capability. Initial investments will focus primarily on productivity and reporting improvements. However, as technology becomes more widespread, lawyers become more tech-savvy, and data more readily available, this focus will shift. Innovation will become one of the main drivers for technology adoption and in-house teams will focus on more complex technologies, such as AI and machine learning.
Investing in core applications and process automation software will help in-house teams minimise manual processes, vastly improving the efficiency of legal operations. And because processes are digitised, reporting should become much easier and data more accessible. Process efficiencies and ease of reporting frees up departmental resources to focus on other, more value-added tasks. One of these tasks could be to analyse data sets, which helps an in-house team become more insights-led.
AI, machine learning, and big data have the potential to transform in-house legal processes. The data that an in-house team has access to is a goldmine of insights. However, most departments lack the tools or skills to extract value from this data. Digital transformation sets a foundation for in-house legal teams to use AI and machine learning to further improve operational efficiency and deliver more value to the business.
Machine learning is already being used by the legal profession, but we’ve barely scratched the surface of what is possible. Initial steps have been made to use the technology in areas such as legal research, e-discovery, and document preparation. However, we are far from achieving its full potential. For example, the field of natural language processing (NLP) is evolving rapidly, enabling machines to better understand the meaning and context of written language.
In-house legal teams can use NLP to more effectively and efficiently respond to e-discovery requests, analyse the effectiveness of wording in contracts, and prepare frequently used documents. In the future, machine learning approaches can be used to measure individual lawyer’s effectiveness, not just in total, but in different situations. The results can help optimise the types of legal work each lawyer is assigned and highlight areas for staff training. If there is sufficient input data – for example, where an organisation faces frequent, high-volume litigation – AI can be used to predict the outcome of different claims.
While AI, machine learning and Big Data promise much, delivery will rely on many factors. Cultural factors should not be dismissed. Many staff will have genuine concerns that AI will replace rather than augment their roles, so initial deployments should be handled with care. Others may be sceptical that AI can live up to its hype, and will need to be convinced through early results of analytics projects. By its nature, an insights-based approach to legal practice challenges the decades-old status quo, where decisions are made based on a lawyer’s previous experience, knowledge, and intuition. It’s therefore important to prepare teams for a different approach, where junior data scientists can challenge senior legal staff based on their analysis.
Data quality is a huge issue that must be addressed. The old adage of garbage in/garbage out holds true. Consequently, in-house teams must focus on redefining business processes so that good quality data is recorded and made accessible.
Of course, a team of staff must be on hand to analyse this data. Few lawyers will have extensive experience of data analysis, or legal data analytics software. Consequently, there is a significant skills gap that must be bridged. Larger organisations may do this by creating legal operations teams that include data specialists, but this is a luxury that smaller organisations cannot afford.
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