Two legal professionals may have very similar career paths and still practise in very different ways. As AI makes certain tasks easier, those differences may become even more visible.
Artificial intelligence is advancing rapidly across the legal profession. Research, document analysis, contract drafting and review, summarising… more and more tasks can already be supported by AI.
We often ask how far AI will go. But another question is just as worth asking: if these tools allow more legal professionals to perform certain tasks faster, what will still make the difference?
Two legal professionals may have a similar education, the same number of years of experience and sometimes very similar career paths. On paper, they may look alike. In practice, often far less so.
What a CV does not show
Those differences often become apparent when discussing real situations. The business wanted to move ahead despite a legal risk. A negotiation had reached an impasse. A decision had to be made without having all the information. These are often the situations that reveal most clearly how someone works and what they actually bring.
Autonomy, for example, does not necessarily mean making every decision alone. It can also mean being able to move forward, take a position and know when it makes sense to test your reasoning with someone else.
Being close to the business does not mean simply agreeing with what it wants either. It can help you understand its objectives, identify the risk that really matters and look for a way forward without losing sight of the law.
These situations often tell us more about a candidate’s qualities than the sometimes overly generic terms found on a CV: pragmatic, business-oriented, autonomous.
That makes the boundary between technical skills and soft skills less straightforward than it may seem.
Is it really ‘soft’?
These kinds of qualities are generally grouped under the label soft skills. The term was notably used and formalised in work carried out by the US Army around the early 1970s. At the time, it referred to professional skills involving little or no interaction with machines.
The term has evolved considerably since then. But in the legal profession, I sometimes find the boundary with so-called technical skills difficult to draw.
Identifying the applicable legal rule is clearly a legal skill. But deciding which risk should genuinely prevent a transaction from moving forward, and which one can be accepted or managed — is that still purely technical?
The same applies to negotiation. Knowing contract law is essential. But knowing how far to push a position, or how to find a way forward when negotiations stall, is also part of the job.
And this is where AI becomes interesting
Imagine two legal professionals using the same tool. Both can ask it to summarise a contract, identify certain risks or produce an initial analysis. They may even receive very similar results.
The differences tend to emerge afterwards. An answer may seem convincing enough to one person, while another will notice that something is missing, that an assumption still needs to be tested, or that the result does not sufficiently take the context into account.
AI can help identify a risk. But it does not decide whether that risk should be accepted, mitigated, negotiated or treated as a deal-breaker.
Saying that AI automatically makes soft skills more important feels too simplistic. They were already important. AI mainly makes certain differences between legal professionals even more visible.
What this changes in recruitment
Legal recruitment naturally pays close attention to criteria that are relatively easy to identify: education, years of experience, areas of expertise, professional environment, transactions and languages.
And rightly so. These elements help establish whether a candidate has the necessary foundations. But they do not tell us everything about how that person works.
A CV may show that a lawyer has negotiated many contracts, but it is much less likely to show how that person reacts when a negotiation gets stuck. The same applies to autonomy: mentioning it is one thing; understanding how someone acts when they have to make a decision on their own is another.
These differences often emerge when discussing real situations. A difficult negotiation, a decision made with incomplete information or a disagreement with a client can give a much more concrete sense of how someone works.
The aim is not to look for the perfect answer, but to better understand how someone approaches a real situation.
AI reinforces an existing trend: placing less emphasis on the sheer volume of experience and more on how that experience has actually been used. Having handled many contracts or matters remains relevant. But what a lawyer or in-house counsel actually did with that experience is more revealing.
In the end, AI forces us to look more closely at what has long been part of legal competence.
