We humans love to categorize.
Life is chaotic and unpredictable even in the best of times. Creating tidy boxes and neatly tucking the data points we discover into them can bring a lot of comfort: it inspires a sense of control and a framework for understanding that help equip us to confront the unknown.
Most of us do this intuitively, but we also learn all kinds of systems for it in school and, later, at work.
For example, upon hearing my elementary school teachers’ descriptions of what it means to be left-brained or right-brained, I knew, deep in my arts-and-craftsy little heart, that I belonged in one camp.
Then there are the personality assessments and exercises that have helped me articulate my natural working styles and get to know my colleagues over the course of my career (shout out to my fellow INFJs! I believe in you!).
But this psychological habit can also make it easy for large teams to become siloed or individual skills to fade.
In the legal profession, skill balancing is crucial. Fundamentally, this space requires all the parts of one’s brain to work in symphony: logical thinking must evaluate data and weigh possible outcomes, while creativity pulls inspiration from those analyses to build narrative strategies and deliver impactful arguments.
Forgive the cliché, but: in the age of legal AI, this balance is more important than ever.
Comprehensive Cognitive Skills Supporting Legal Work
Let’s—you guessed it—categorize legal tasks for a moment to illustrate what I mean.
Sometimes, legal data intelligence work requires analytical, linear thinking. When you’re focusing on repetitive tasks like data or time entry, or even more methodical efforts like citation checks and compliance reviews, this sort of order-of-operations-based cognitive work is essential.
Other responsibilities are more creative and open-ended, requiring a unique synthesis of big-picture thinking and detail-orientation that builds brilliant legal arguments.
AI helps automate and streamline many tasks, which means less of the rote work may fall on human shoulders. It can also help augment and accelerate creative efforts by assisting with brainstorming, drafting, and strategic analysis and fact building.
This feels like a win-win: suddenly, you have tools to help you go faster across the board and really focus on the good stuff. But is there reasonable concern that leaning on AI in the wrong ways can let its power weaken an individual's skills?
Think about it this way. Though we traditionally measure aptitude for linear skills based on output (e.g., number of documents reviewed correctly), their real value is that they teach legal professionals how to think—both analytically and creatively. When rote tasks fall to early-career legal professionals, they function as a sort of on-the-job cognitive training. Document review offers exposure to concepts of relevance and privilege, risk patterns, and narrative assemblies. Research is about learning how legal reasoning is constructed and the insights that the rich history of this field have to offer to modern teams and matters.
When AI takes on more of the routine execution, that developmental pathway begins to shift. As one recent Bloomberg Law analysis put it, junior lawyers in the age of AI may spend less time reviewing documents for designations and more time “excavating insights” from them. This can be a meaningful upgrade in their opportunities to grow as professionals and have real impact sooner—but their cognitive skills must be well-developed to capitalize on it.
What you don’t want is to end up with professionals who are excellent at interpreting outputs, but less confident building the logic needed to generate them—or those who focus more on prompt engineering and less on transforming AI’s conclusions into the memorable, strategic, and persuasive legal arguments that only humans can best deliver.
So, a key to optimizing your use of AI—because, make no mistake, it is incredibly useful—is to see it as an opportunity to not just accelerate, but synthesize your efforts.
Instead of categorizing one-off tasks and punting them to AI where you can, be thoughtful about your end-to-end daily workflows and how AI can plug in more meaningfully.
From Split-Brain to Integrated Thinking
In a recent article on lateral thinking, we talked about the importance of stepping outside linear problem-solving to find unexpected solutions.
You’ll notice when you try this out that lateral thinking without a strong analytical backbone risks becoming untethered—thought provoking, sure, but not always repeatable or defensible. And the inverse is just as risky: purely linear thinking, unsupported by curiosity or creativity, tends to produce safe answers to the wrong questions.
Similarly, viewing AI as an easy button—because, increasingly, it really is getting simpler and more user-friendly all the time—for offloading cognitive effort, rather than a tool or a creative partner for accelerating that effort, can undermine your internal skill-building and collaboration over time.
This is where the “left brain vs. right brain” framing must fall away: all at once, and back-and-forth, you need to use analytic, linear skills to build exceptional prompts based on how you know your AI operates, and creative, lateral skills to ask the right questions and assemble what you uncover into the strategies that will move your projects forward.
Truthfully, legal work has never really operated in separate, tidy cognitive lanes. Highly skilled litigators, investigators, and analysts layer these modes. For example, they might use structured reasoning to test creative theories and employ creative thinking to challenge structured assumptions. Most enchantingly, they move between these exercises so fluidly that the distinction becomes kind of irrelevant.
Building Integrated Expertise in Practice
So what does it actually even mean to “be intentional” in how you use the (approved!) AI tools at your disposal?
A few practical habits can help get you on the right track.
#1: Don’t skip the structure-building step.
Though AI can assist with mapping out an argument or research path, try sketching your own first. Compare the two. A lot of learning happens in the gap between them.
As you review, ask yourself how the AI surprised you, what citations it used to back up its conclusions (and, crucially, whether you can verify them), and what new questions it uncovered for you. Use this exercise to refine and structure your preferred path forward.
#2: Treat outputs as hypotheses, not answers.
AI-generated summaries, categorizations, and arguments should be pressure-tested. Ask: What’s missing? What assumptions are being made? And again: is this conclusion verifiable?
When you’re using AI for nuanced and strategic work, you must remember that all the accountability rests with you. Many experienced practitioners suggest treating AI like a junior member of your team: delegate for speed and training, but always carefully review and verify deliverables.
#3: Stay close to the underlying data.
When AI surfaces new insights, take time to trace them back to source material. Pattern recognition is only valuable if you understand what the pattern is built on.
Enterprise data itself is incredibly valuable for the insights it can offer—but to draw more on its value than its risks, you need to understand it intimately. Don’t let your use of AI obfuscate your ability to explore and interact with data yourself.
#4: Make space for different types of thinking.
When workflows become more efficient, the default is to do more work, faster. Resist that instinct occasionally so you can reinvest time into big-picture growth.
For instance, spend a few minutes exploring alternative theories or ideas that wouldn’t have fit into a tighter process; engage in innovation exercises with your team; or attend some trainings that you otherwise couldn’t have made time for.
#5: Don’t lose sight of the team.
Human collaboration layered onto AI has been shown to improve outcomes, so be careful not to use it in a vacuum. Ask for advice on how to use it well, and opinions on the work product you generate with its help. Keep lines of communication and collaboration wide open.
For me, it’s helpful to talk through some early ideas with my favorite chatbot before sitting down for a brainstorming session with colleagues. I like to feel prepared and ready to articulate my thoughts. But I’m trying to make a habit of bringing the tech into that bigger room, too, to help broaden horizons beyond my own monitor.
The Evolving Shape of Legal Expertise
While I love a good compartmentalization exercise when I’m feeling overwhelmed, it becomes clearer every day that the most substantive work the legal community does requires a flowy combination of so-called right- and left-brain thinking. All of these skills grow more quickly when used in tandem.
For legal teams in the age of AI, major professional and strategic advantages come from our ability to compose cognitive symphonies where analytical and creative thinking work in concert with one another. Even when AI changes how we work, we can guard ourselves from letting it erode how we think.
Graphics for this article were created by Kael Rose.
