What This Looks Like in Practice
Part 5 of the AI Knowledge Series
Theory is one thing. Practice is another.
I've described knowledge structures, control architectures, and skill systems in abstract terms. But what does this actually look like when you're facing a filing deadline, juggling multiple matters, and trying to practice law while also building AI capabilities?
Here's a realistic picture of how I use these tools day-to-day.
Morning: Research Request
A partner asks me to research whether a specific lease provision constitutes a valid Pugh clause under Texas law. It's a nuanced question -- the provision has some characteristics of a Pugh clause but uses non-standard language.
Without AI infrastructure: I'd start from scratch. Pull up Westlaw, search for Pugh clause cases, skim through results, hope I find something on point.
With AI infrastructure: I query my knowledge base: "Cases analyzing non-standard Pugh clause language." The system returns three cases I've previously analyzed with notes on their key holdings. It also identifies two contracts in my document repository with similar provisions.
I still need to analyze the specific question, but I'm starting from a foundation of curated knowledge rather than a blank search results page.
Time spent on initial research: about 20 minutes instead of an hour.
Midday: Drafting a Motion
We need to file a motion to compel production on a tight deadline. The underlying facts are case-specific, but the legal framework is familiar -- I've filed dozens of these.
Without AI infrastructure: Pull up a similar motion from a previous case, manually adapt it, research any case law I don't remember.
With AI infrastructure: I invoke my "motion to compel" skill. The AI asks structured questions about the case: What categories of documents? What objections were raised? What's the discovery deadline? Based on my answers, it generates a first draft using my preferred structure and pulling from my library of relevant authorities.
The draft isn't perfect. I spend 30 minutes refining the fact section and sharpening the argument. But the framework is solid, citations are accurate, and I didn't have to reconstruct standard legal analysis.
Time spent drafting: about 45 minutes instead of two hours.
Afternoon: Contract Review
A client sends a lease for review before signing. Standard Texas oil and gas lease, but with some unusual provisions in the pooling and unitization clauses.
Without AI infrastructure: Read the entire lease, flag issues based on memory of problem provisions, research any clauses I'm uncertain about.
With AI infrastructure: I run the lease through my contract analysis skill. It compares each provision against my database of standard provisions, flagging deviations. For the unusual pooling clause, it identifies similar language in other leases I've reviewed and notes how those provisions were interpreted in subsequent litigation.
I still need to exercise judgment about which flags matter and how to advise the client. But the AI has done the comparison work that used to eat up hours.
Time spent on initial review: about 40 minutes instead of 90 minutes.
The Reality Check
I want to be honest about what this system doesn't do:
It doesn't replace thinking. Every AI-assisted task still requires my professional judgment. The AI accelerates the mechanical parts; the analytical parts are still mine.
It doesn't work perfectly every time. Sometimes the AI misunderstands a query. Sometimes my knowledge base is missing a crucial piece. I've learned to verify before relying.
It didn't build itself. The knowledge base represents hundreds of hours of curation. The skills represent dozens of iterations. This infrastructure exists because I built it, deliberately, over time.
It's not finished. Every week I find gaps. Cases I should have included. Procedures I should have encoded. The system is always under construction.
The Investment Equation
Is it worth it?
Here's my rough math: Building and maintaining the system costs me maybe 3-4 hours per week. Using it saves me 6-8 hours per week. Net gain: 3-4 hours of higher-value work.
But that's the immediate calculation. The longer-term value is harder to quantify:
- Knowledge that would have evaporated is now persistent
- Procedures that lived only in my head are now documented and improvable
- The system gets better every week as the knowledge base grows
The compound effect I described earlier isn't theoretical. I can feel it. Tasks that took hours six months ago now take minutes -- not because the AI got smarter, but because my infrastructure got deeper.
What I'd Do Differently
If I were starting over:
Start with high-frequency tasks. I initially tried to build comprehensive coverage. I should have focused on the 20% of tasks that consume 80% of time.
Build knowledge incrementally. Instead of trying to load everything at once, I should have added knowledge as I used it. Organic growth beats forced curation.
Accept imperfection. Early on, I didn't use the system because it wasn't "ready." There's no ready. Use it, find gaps, fill them, repeat.
Document failures. I learned more from what didn't work than from what did. Keeping a log of AI mistakes helped me improve the system faster.
The Daily Reality
Most days, I don't think about "using AI." I just work. When I need to find a case, I query the knowledge base. When I need to draft, I invoke a skill. When I encounter something new, I add it to the system.
The infrastructure has become part of how I practice -- not a separate tool I consciously choose to use, but an extension of my own capability.
That's the goal: not AI as novelty, but AI as natural augmentation of professional practice.