Getting Started: A Practical Path Forward

Part 6 of the AI Knowledge Series

You've read about knowledge structures, control architectures, and skill systems. Maybe you're convinced this approach makes sense. Maybe you're still skeptical.

Either way, you might be wondering: where do you actually start?

Here's a practical roadmap based on what worked for me and what I'd recommend to colleagues.

Week 1: Choose Your Battleground

Don't try to transform your entire practice at once. Pick one specific, recurring task that:

• Happens at least weekly
• Involves research or drafting
• Currently takes 1-2 hours
• Follows a roughly consistent process

Examples from litigation practice:

• Drafting discovery responses
• Researching a specific type of motion
• Reviewing contracts for specific provisions
• Summarizing depositions

This becomes your test case. Everything you learn here will transfer to other tasks.

Week 2-3: Document What You Actually Do

Before you involve AI, document your current process for this task. Be specific:

• What information do you gather first?
• What sources do you consult?
• How do you structure the output?
• What makes a good result vs. a mediocre one?

Most of us have never written this down. We just do it. But making it explicit reveals patterns, dependencies, and decision points you didn't consciously recognize.

This document becomes the skeleton of your first AI skill.

Week 4: Build Your First Knowledge Asset

For your chosen task, identify the key authorities and templates you rely on:

• The cases you cite most often
• The contract provisions you compare against
• The brief sections you reuse
• The checklists you mentally follow

Gather these into a single, organized collection. Don't worry about sophisticated structure yet. Just get them in one place, clearly labeled.

This becomes the seed of your knowledge base.

Week 5-6: First AI Integration

Now bring in AI. Start simple:

1. Give the AI your process document
2. Give it access to your knowledge collection
3. Ask it to perform the task on a real (but not urgent) matter
4. Compare its output to what you'd produce

The first attempt will disappoint you. That's fine. Note what went wrong:

• Missing knowledge it needed?
• Steps it skipped or misunderstood?
• Judgment calls it got wrong?

Each gap tells you what to add or clarify.

Month 2: Iteration

Spend a month refining this single task:

• Add missing knowledge to your collection
• Clarify ambiguous steps in your process
• Build verification checkpoints
• Document patterns in errors

By month's end, the AI should handle this task reliably enough that its output needs editing, not rewriting.

Month 3: Expansion

With one task working, add another. Choose something related -- it probably shares knowledge and process elements with your first task.

As you add tasks, you'll notice patterns:

• The same authorities appear across tasks
• Similar decision points recur
• Skills can call other skills

Start organizing around these patterns. Your ad hoc collection becomes a structured knowledge base. Your process documents become interconnected skills.

Ongoing: The Maintenance Habit

Build a weekly habit (30-60 minutes):

• Review AI outputs from the week
• Note recurring errors or gaps
• Add new knowledge from cases you've worked
• Refine skills based on experience

This maintenance is how the system improves over time. Skip it, and the system stagnates.

What Not to Do

Avoid these common mistakes:

Don't buy a platform first. Figure out what you need before evaluating tools. Your workflow requirements should drive tool selection, not vice versa.

Don't try to do everything. Depth beats breadth. One task done well teaches more than five tasks done poorly.

Don't expect immediate ROI. The first month is investment. Returns compound later.

Don't work in isolation. If colleagues are interested, share knowledge. Collective infrastructure benefits everyone.

Don't keep it secret from clients. Transparency about AI assistance builds trust. Hiding it creates risk.

The Realistic Timeline

Based on my experience and watching others:

• Month 1-2: Learning and initial setup. Net time cost.
• Month 3-4: Breaking even. AI saves roughly what maintenance costs.
• Month 5-6: Net positive. Clear time savings on covered tasks.
• Month 7+: Compound growth. Each new task is easier; existing tasks get better.

Most people who quit, quit in month 2. They expect faster results. The investment period is real, but so is the payoff.

The Bigger Picture

This isn't just about efficiency. It's about what kind of lawyer you want to be as the profession changes.

Lawyers who build knowledge infrastructure will:

• Work faster without sacrificing quality
• Accumulate institutional knowledge systematically
• Adapt as AI tools improve
• Offer clients capabilities others can't match

Lawyers who don't will:

• Compete on hours alone (a losing game)
• Watch expertise walk out the door with departing lawyers
• Adopt tools without knowing how to use them well
• Slowly lose ground to more adaptive competitors

The choice isn't whether to use AI. It's whether to use it deliberately and well.

Final Thoughts

I started this series by explaining why I stopped waiting for AI to mature. Six months in, I'm more convinced than ever that the waiting approach is a mistake.

Not because the current tools are perfect -- they're not. Not because the approach I've described is the only way -- it isn't. But because the fundamental challenge -- making institutional knowledge accessible and actionable -- doesn't go away by waiting.

Start small. Build deliberately. Iterate constantly. The compound effect will do the rest.

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This concludes the AI Knowledge Series. Questions or thoughts? Get in touch.