Solving AI’s legal skills conundrum

Imagine a world where lawyers use AI to handle routine tasks, freeing them up to focus on complex legal challenges. Sounds ideal, right? But there's a catch. As AI assists more and more with these repetitive tasks, how do lawyers keep their essential skills sharp? At Pinsent Masons, we're tackling this conundrum head-on. In this edition of AI in Action, we explore why AI needs to complement the wider skillsets lawyers must develop as their careers progress.

Join us as we delve into the practical implementation of AI in business, the hurdles we face, and the innovative solutions we're developing to navigate this new landscape.

The essential skills for lawyers

Lawyers need a wide range of deep skills that they need to be able to execute flawlessly – the consequences of misreading a detail in a document or missing a court filing date are enormous. They need deep expertise to navigate legal technicalities in documents or to spot flaws in legal arguments. This day-to-day expertise in how law works is essential. ?

But in an age where we are increasingly delegating routine tasks to artificial intelligence (AI), how do they keep those skills sharp? Indeed, how do they develop them in the first place?This is a challenge all employers of lawyers now face, whether that is companies or law firms. At Pinsent Masons it’s prompting a complete rethink of how we train our lawyers – we need to rely less on the kind of almost-passive training-by-osmosis of the past and develop a more conscious, deliberate and structured approach.

"As we embrace AI to enhance our efficiency, it's crucial that we also focus on developing and maintaining the essential skills that define our profession. At Pinsent Masons, we're committed to a structured and deliberate approach to training, ensuring our lawyers are equipped to navigate both the opportunities and challenges of an AI-driven future."? Tim Dale, Director of Knowledge, Pinsent Masons

Traditional vs. modern learning

The legal industry has traditionally relied on what you might generously call experiential learning – early in their career a lawyer would sit with and soak up information from more senior lawyers. They would hear how to handle difficult calls, be in a meeting taking notes to find out what good and bad meeting management looked like, and would be able to lean over a desk and ask someone how to do something.

Of course, there is significant investment in formal training and assessment at Pinsent Masons and elsewhere. But these less formal means are how those early in their careers ?come by many of their technical skills such as using particular research or analysis tools; how to create and file particular kinds of documents, or how to execute a particular process.

Their skills are created and kept sharp in two ways: by leaning on the more experienced people around them for guidance and help; and through the enormous repetition of the tasks in the early years of their career.

Post-lockdown working means the first condition is not always met – even if they are physically in an office, our ways of working are more distributed than ever. Someone might be working on a project with colleagues based elsewhere, some of those colleagues might even be freelance or on-demand resource.

And AI means the second condition may not be being met for this generation of early career lawyers either. As we delegate repetitive, research, analysis and document-creating jobs to AI we are gaining efficiency, speed and consistency, but this might be at the expense of skills development.

AI compounds that challenge in other ways. The use of AI for these tasks means that we need skilled checkers of its output more than ever. So AI risks creating the conditions for erosion of skills at the same time as it creates greater demand for those skills.

Developing new training approaches

It is therefore important that ?we find new ways to develop, retain and sharpen those skills, and that’s exactly what we’re investigating at Pinsent Masons.

We are moving towards a more deliberate and considered approach to developing skills in lawyers’ early careers, and keeping those skills sharp well into their working lives. That means working hard now to develop the content, training, mentoring and skills development they need in a more structured way than ever before.

That can be through simulated transactions; through classroom and experiential learning, and through using technology to deliver and assess ongoing training.

This is an up front resource burden on any organisation taking this approach but I don’t see how it’s optional. Not taking this approach carries risks for the future.

Moving away from the more informal, osmosis-based training of the past has other advantages. Perhaps some people got taken under a senior colleague’s wing more than others, perhaps they got access to better tasks or work which developed them better for reasons that weren’t wholly rational. Leaving training almost to chance risks giving conscious and unconscious bias too much latitude to influence early careers, for good and for bad.

AI training for all lawyers

There is a wholly separate AI legal training conundrum – the lawyers need to learn about AI tools to improve and refine their work. This is not an early career lawyer challenge; it is one for everyone working today.

We work hard to train our people on when an AI tool might be helpful, which tool might be helpful, and how to get the best out of that tool. We help them understand there is nothing such as an overall, general ‘AI’, and that as in any other sphere of work choosing the right tool and knowing how to operate it are crucial.

So, we have firmwide mandatory AI training; we’re building up banks of prompts to perform common tasks and building communities of users who can learn and discover together, supporting each other along the way.

We’re also making bigger investments in creating pools of deep expertise to support all our experts in making the most of the latest tools.

How do you think AI should be integrated into skill development for lawyers? Have you experienced any challenges or successes with AI in your own work? Share your experiences and join the conversation in the comments below.?

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