You’re in a requirements meeting. Someone describes a problem. You take notes.

What happens next used to be predictable: hours digging through documentation, tracing business rules across systems, building mental models of how things connect. Then more hours validating that what you found matches reality.

That cycle hasn’t disappeared. But the people winning with AI have compressed it.

The Workflow

Here’s what I’ve been doing:

1. Capture the context.

Meeting ends, notes in hand. Instead of opening documentation blindly, I feed my notes to an AI coding assistant. Not for answers—for orientation.

“Here’s what the business needs. Here’s what I know about the system. Help me understand what I’m looking for.”

The response is rarely the solution. But it surfaces the right questions. What tables? What business rules? What edge cases should I check?

I load only what’s relevant for that query. AI helps you find the “right” answer (spoiler it’s 42)—but you need to explore whether that’s the right question, and whether the information supporting it is quality.

2. Point the AI at your systems.

This is where most people stop. They ask questions, get generic answers, and conclude AI isn’t useful for real work.

But if you can point the AI at your actual context—table structures, stored procedures, business rule documentation—something shifts.

The AI becomes a research assistant with access to your materials instead of generic training data.

I pull information from my system—table schemas, relevant code, business rules documentation—and feed it in.

“Given these tables and these rules, where would the logic for X live?”

3. Let the AI find patterns you’d miss.

The value isn’t that AI is smarter than you. It’s that it reads faster than you.

A 2,000-line stored procedure. A data model with 47 tables. A rules engine with logic scattered across config files.

You could trace it manually. Or you could ask the AI to surface the relevant sections while you focus on judgment.

4. Refine together.

The first answer is rarely complete. But now you’re iterating on specifics instead of starting from scratch.

“Show me how this connects to the downstream calculation.” “What happens if this condition isn’t met?” “Walk me through the edge cases here.”

Each question narrows the gap between “I think this is how it works” and “I know.”

Why This Works

Research from CMU and Stanford (October 2025) found something counterintuitive: AI augmentation improved efficiency by 24%, but end-to-end automation actually decreased efficiency by 18%¹.

The difference? Verification overhead.

When you replace humans entirely, someone still has to check the output. That checking takes longer than the work would have taken with a human in the loop from the start.

But when humans stay in control and AI speeds up specific steps, the verification is built in. You’re not trusting the AI. You’re using it.

Klarna learned this the hard way. They replaced 700 customer service agents with AI. Lower quality service followed. Now they’re rehiring humans². The chatbot handled predictable interactions fine, but the complex 70%—the judgment calls, the nuance—fell apart.

Gartner predicts 50% of companies that cut customer service staff for AI will rehire by 2027³.

McKinsey went the other direction. 25,000 AI agents alongside 40,000 human consultants. They saved 1.5 million hours in search and synthesis work⁴. Back-office output increased 10% with 25% fewer people.

But client-facing roles grew 25%. The speed gains in research freed up time for the work that needs judgment.

What Changes in Practice

The biggest shift isn’t the tools. It’s the workflow design.

Old approach:

1. Get requirements
2. Research manually
3. Build solution
4. Review and refine

Augmented approach:

1. Get requirements
2. Use AI to orient and surface relevant context
3. Point AI at your actual systems (schemas, docs, code)
4. Iterate on specifics with human judgment throughout
5. Final answer is yours. AI helped you get there faster.

The difference isn’t replacing step 2 with AI. It’s redesigning the whole process around human-AI collaboration.

The Trap

Not every task benefits from this. And honestly? Some shouldn’t.

If the work is truly routine—predictable, programmable, no judgment required—automation might be the right answer. Checkout kiosks. Automated data pipelines. Manufacturing robots.

But most knowledge work isn’t routine. It looks routine until you actually do it. The edge cases, the context, the “this worked last time but the requirements changed” moments.

The trap is assuming AI can handle complexity because it handles examples well. Demos are controlled. Reality isn’t.

Actually, that’s not quite right. The real trap is subtler: AI handles most complexity well enough to feel safe. It’s the remaining 10%—the weird edge cases, the context nobody documented, the rule that changed last month—that breaks you.

The other trap: using AI to generate answers instead of accelerate understanding.

If you’re pasting requirements into ChatGPT and copying the output, you’re not augmenting. You’re outsourcing judgment. The result might look right. Might even be right. But you won’t know why, and you won’t catch when it’s wrong.

What to Try

If you’re not already using AI this way:

Start with orientation, not solutions.

Before diving into a new codebase or system, describe what you’re looking for to an AI with access to your materials. See what questions it asks you.

Point it at real context.

Generic AI advice is everywhere. AI that’s read your documentation, seen your table structures, understood your business rules—that’s actually useful.

🚨 Caution

Avoid connecting open agents directly to production systems without proper safeguards, guardrails, or following your company’s security policies. Feed information deliberately, not through live integrations.

Keep the judgment layer.

Every AI output is a draft. Your job is to verify, refine, and decide. The time savings come from faster iteration, not skipped steps.

The companies that figure this out aren’t asking “what can AI replace?” They’re asking “where does AI make my people faster?”

So long, and thanks for all the fish.

References