This article is inspired by the article “AI’s Real Promise for Law Firms: Transforming Associate Training Through Deliberate Practice and Data-Driven Feedback,” written by Jeanine Conley Daves, Office Managing Shareholder and Board Member at Littler Mendelson; Michelle Gomez, Shareholder and Senior Director of Engagement & Development at Littler Mendelson; and Abdi Shayesteh, Founder and CEO of AltaClaro.
For the past few years, most conversations about artificial intelligence in law have focused on automation.
Can AI draft faster than an attorney? Can it review documents more efficiently? Can it reduce the time lawyers spend on routine tasks? While these are questions worth asking, they may not be the most important ones.
In the legal industry, the real opportunity for AI might not be its ability to do the work for lawyers, but its ability to help lawyers learn how to do the work better.
In other words, AI has the potential to transform legal training, and the framework that makes this possible is deliberate practice.
There’s a chicken and egg dilemma in legal training.
Lawyers are expected to master complex professional skills effectively, such as conducting depositions, examining witnesses, drafting persuasive motions, managing discovery strategy, and negotiating with opposing counsel. But opportunities to practice these skills are often limited.
Client matters are expensive and high-risk. Partners cannot easily hand critical responsibilities to inexperienced attorneys simply for the sake of training. As a result, much professional development happens through a mix of observation, occasional feedback, and learning by trial and error.
This approach leaves gaps.
Associates may go long periods without practicing certain skills. Feedback can be inconsistent, and the opportunity to repeat a task and improve upon it is rare.
In other high-performance professions, this model would be unthinkable.
Elite athletes do not learn by occasionally appearing in games. Musicians do not prepare for concerts by simply observing others perform. They train through deliberate practice.
The concept of deliberate practice was popularized by researcher K. Anders Ericsson, whose work on expertise showed that mastery in fields like music, athletics, and chess comes from structured, purposeful training rather than passive experience.
Deliberate practice has three defining characteristics:
For example, a violinist might isolate difficult passages in a piece and practice them repeatedly while receiving feedback from a teacher, and a professional athlete might review performance data and run targeted drills to correct weaknesses.
Over time, these cycles of practice and feedback build mental models — internal frameworks that allow experts to process complex situations quickly and make better decisions under pressure.
This is how expertise develops. And until recently, it has been extremely difficult to replicate this model in legal training.
Artificial intelligence is often framed as a productivity tool. But in the context of professional development, it can serve a different purpose: creating scalable training environments.
AI-powered simulations can allow lawyers to practice complex legal tasks in a controlled environment that resembles real client work.
For example, attorneys can:
Because these exercises are simulated, lawyers can repeat them as many times as necessary, refining their approach and improving performance.
Equally important, AI systems can generate structured feedback immediately after each session, evaluating performance across defined competencies such as:
This combination of repetition and feedback is the foundation of deliberate practice.
When deliberate practice principles are applied to legal training, three elements become essential.
Practice environments must resemble the complexity of real legal work.
Effective simulations introduce ambiguity, competing priorities, and unexpected challenges, such as an aggressive opposing counsel or an evasive witness. These conditions force attorneys to think critically and adapt their strategy in real time.
Training should push lawyers beyond familiar territory.
Junior attorneys might focus on foundational skills like structuring deposition questions. Mid-level associates might tackle more complex witness dynamics. Senior attorneys may use simulations to rehearse difficult cases or refine advanced techniques.
The goal is not comfort. It is progressive challenge.
Improvement requires visibility.
Objective evaluation helps attorneys understand where they are improving and where they need more work. Structured feedback also allows mentors to focus their coaching on the areas that will have the greatest impact.
The legal profession has long relied on an apprenticeship model in which expertise develops slowly through exposure to client matters. AI creates an opportunity to complement that model with something more structured.
By incorporating simulations, purposeful challenge, and data-driven feedback into training programs, firms can create development pathways that are more deliberate, measurable, and effective.
The future of AI in law will not be defined solely by automation. Its greatest impact may come from something more fundamental: helping lawyers practice, improve, and master their craft.