Amanda is a CPA in Austin. She’s been running her own practice for years. And last October, she texted me after her first week with an AI inbox manager I’d set up for her through Lindy.
“It still feels weird. Like the wifi is broken or something.”
She meant it as a compliment. I think.
What “Wifi Is Broken” Actually Means
Before the agent, Amanda’s inbox was running in the background of everything. Not just open in a tab. Actually running. The mental check-ins between client files. The “let me just see if anything came in” that breaks a 45-minute focus block into two 20-minute ones.
Tax season is brutal for CPAs. The work itself isn’t the problem. It’s that the work happens inside a constant buzz of emails, CPE requirements, team coordination, and client questions that feel urgent but usually aren’t.
The Lindy agent I built for her does a few things. It auto-categorizes CPE emails and adds the trainings to her calendar. It forwards team-related emails to the right people. It handles inbox routing for the stuff that can be handled without her.
First week, she did three hours of uninterrupted deep work on tax returns.
Three hours. During tax season.
That “wifi is broken” feeling? That’s the inbox being silent for the first time in years. Not because nothing came in. Because the agent handled it.
The Part I Didn’t Expect
A few weeks later, Amanda called me. She wanted to tell me something.
She’d started building her own thing.
A custom GPT that could read a tax return and flag checklist errors before they hit her desk. She’d spent about 90 minutes iterating with ChatGPT. Got a working version, even if it wasn’t perfect yet.
“I actually tried to build a custom GPT that could read a tax return and analyze it for certain points,” she told me. “It asked me to drop the return in there, redacted, and I was working on building it. I was able to do that because Lindy is saving me time. I actually have more time to iterate with GPT.”
She built a second AI tool. On her own. Without me.
That’s not something I planned for when we started. It’s what happens when the first automation actually works.
The Compounding Effect Nobody Talks About
Most people think about AI automation as a time savings calculation. If this takes me 30 minutes every day and the agent can do it, I get 30 minutes back. That math is real.
But there’s a second effect that’s harder to quantify: when you get your attention back, you start seeing differently.
Amanda wasn’t just saving time. She was getting back the mental bandwidth to ask “what else is possible?” And she had enough slack in her day to actually experiment.
This is what I mean when I say start small and build from there. The email automation wasn’t the end goal. It was the foundation that made the next thing possible.
How to Actually Build This
Here’s a technique I use with clients that almost nobody talks about.
If you want to build an AI agent that replicates your own expertise — like Amanda’s tax review GPT — screen record yourself doing the work.
Not just capturing the screen. Narrating out loud. Talk through exactly what you’re looking at, what you’re noticing, what flags you’re catching and why.
Do it two or three times on different cases.
Then give the recordings and the transcripts to ChatGPT. Tell it: based on how I think through this work, help me codify this into a system. A checklist. A custom GPT. A review process.
Your own narration becomes training data. The AI learns your judgment from watching you work, not from a prompt you wrote trying to explain it.
This works for tax reviews. It works for sales calls. It works for any expertise that’s hard to put into words because you’ve been doing it so long it’s automatic.
What the $65K Error Taught Her About AI and Human Judgment
One more thing Amanda told me, and it stuck with me.
A client came in saying they owed $90,000 in taxes. Should have been around $25,000. Before she even pulled up the return, she knew something was wrong. Her gut immediately went to three possible places it could be.
She was right. A bookkeeping error that had passed through three different team members. Nobody caught it.
AI can handle the repetitive, rules-based work. The sorting, the routing, the formatting. But that pattern recognition she has after years of seeing thousands of returns? That’s not automatable yet. And it’s worth protecting.
That’s the right framing for AI in a professional services business. Not: AI replaces the expertise. It: AI clears the path so the expertise can actually be used.
When Amanda isn’t sorting CPE emails, she’s catching $65,000 errors.
That’s the trade worth making.
Curious what it looks like to set up an AI inbox manager for your own practice? I run one-day workshops where we build these systems live. Or reach out directly if you’d like to explore a custom setup.
