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P E E R T O P E E R M A G A Z I N E · S P R I N G 2 0 2 6 63 5 Practice- and Role-Tailored Training. Resist the temptation to deploy one-size-fits-all training programs. I am a big believer that, like politics, all change and innovation is local. Learning to exercise judgment on the use of AI cannot be abstract. A litigator assessing AI-assisted research output applies differ- ent filters and faces different judgment demands than a transactional attorney reviewing an AI-drafted contract clause. Likewise, a first-year associate drafting a contract with the assistance of AI has different judgment demands than a general counsel evaluating an enterprise AI platform. Effective training programs segment by practice group, seniority level, and/or func- tion and build practice-specific modules that situate reasoning in recognizable professional contexts that resonate with learners. 6 Measuring Judgment Maturity. As the management principle states, we should measure what matters. KPIs for AI training should move beyond indicators on whether users are accessing AI tools to tracking metrics that demonstrate proficiency and maturation in the exercise of professional judgment, such as: • Citation defect rates • Review protocol compliance • Audit documentation of AI-assisted workflows • Practice-specific AI standards adoption 7 Cultural Shift to AI-Enabled Talent. Finally, the ultimate, and perhaps longer-term, success of building AI judgment frameworks will be evidenced by a mindset shift. As AI-assisted workflows become the norm, traditional views on professional identity and legal team roles are evolving. "Human- in-the-Loop" will take on a new and very tangible meaning. This shift may look something like: Historical Roles Senior lawyer = drafter Associate/ paralegal = researcher Emerging Roles Lawyer = risk calibrator AI = draft accelerator Human = validator In the emerging model of AI-enabled talent, the speed of content generation is likely not the top indicator of premium skill. The focus will be more on context and risk calibration, error detection, ethical reasoning, and supervi- sion of AI outputs, informed by legal subject-area expertise. THE SKILL MACHINES CANNOT REPLACE Early on, the AI race was about getting access and standing up use cases. The next phase, which is critical to sus- tainability in legal, is about judgment and defensibility. Courts have sanctioned AI misuse, and bars have clarified overriding duties. Clients are asking questions about AI governance. The organizations that will lead are those that move beyond teaching legal professionals to prompt better, to teaching them how to decide better.

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