Amanda runs a tax practice. She started it because she genuinely liked the work — helping clients navigate complex financial situations, building long-term relationships, solving problems that required real expertise.
At some point, she stopped liking it.
Not in a dramatic way. More of a slow drift. The work that actually required her got crowded out by everything else: routine questions she'd answered dozens of times, standard reviews that didn't need her judgment, emails that could have been handled by anyone. She was busy every day. But less and less of it felt like the work she'd set out to do.
When we met, she had nine employees and was thinking about hiring more. The work kept expanding to fill the capacity she had.
We took a different approach.
What We Built
The core was a custom GPT trained on Amanda's firm — her processes, her way of thinking through tax scenarios, her communication style with clients, her internal standards. Not a generic AI assistant. Something specific to how her firm operates.
The goal wasn't to automate everything. It was to handle the work that didn't require Amanda.
When a client had a question about a standard tax scenario, the GPT could answer it — accurately, in Amanda's firm's voice, drawing on how she'd handled similar situations before. When her team needed to know how to process a specific type of return, they could ask the GPT instead of her.
She stopped being the first call for everything.
Her outsourced team in India noticed immediately. Her team lead asked where Amanda had gotten the training because the GPT was handling complex scenarios with a level of precision they hadn't seen before. (Amanda was strategic about not sharing all of it. It had become a competitive advantage.)
The Result Nobody Anticipated
Here's what I measure when working with clients: time saved, tasks automated, capacity freed up. These are real and worth tracking. Amanda went from nine employees to four while maintaining the same client volume and quality — that's a significant operational shift.
But the thing Amanda said that stayed with me wasn't a number.
“I've been using ChatGPT so heavily, and those custom GPTs — I can get it done in a way that doesn't feel heavy. It's highly effective, the clients are responding with fantastic results. And it's fast and timely. They're happy. So I feel happy again.”
Happy again. That's a different category of outcome.
Real Work vs Fake Work
There's a distinction I come back to a lot with clients: real work versus fake work.
Real work moves things forward. It's the work you're uniquely positioned to do — your judgment, your relationships, your expertise applied to problems that actually need it. It's why most professionals got into their field.
Fake work is motion without that progress. It creates the feeling of being busy and the reality of going nowhere strategically. It's answering the same question for the tenth time. It's being the bottleneck for things that don't need you. It's the administrative weight that accumulates around the edges of a job and eventually starts crowding out the center.
For most professionals, the ratio shifts over time. Not deliberately. Just because fake work expands to fill available attention. You hire someone, and they help, and then they generate new coordination overhead. You take on more clients, and more of your day goes to maintaining the work rather than doing it.
AI is very good at handling the fake work. The routine queries. The standard processes. The things that follow a pattern but don't require human judgment.
What I didn't fully anticipate when I started doing this implementation work was what happens when the fake work actually comes off someone's plate. The math is obvious — less time on routine tasks, more time for strategic ones. But the emotional dimension surprised me.
People get back something they didn't realize they'd lost.
What “Enjoying My Work Again” Actually Means
Amanda said she'd gotten into accounting because she liked helping clients with complex problems. When I asked what most of her day looked like before we started working together, it wasn't complex problems. It was everything around the complex problems.
When the GPT started handling the routine layer, Amanda got back the work she'd become a CPA to do. The clients who needed her actual expertise. The situations that required judgment. The relationships she'd built over years.
The work didn't feel heavy anymore.
This is the outcome that's hardest to measure and most worth having. Nobody puts “I started enjoying my work again” in a business case. But it's what drives people to keep using the systems they build, to invest in them further, to recommend them to colleagues. It's what makes the difference between an AI implementation that gets used and one that gets abandoned.
The Implication for Your Own Work
If you're a professional — a consultant, a lawyer, a doctor, a financial advisor, anyone doing complex knowledge work — there's probably a version of this pattern in your own day.
Work you became excellent at, crowded out by work that just needs to get done.
The question worth asking isn't “how do I automate my business?” It's closer to: what is the work that actually requires me? And what's in the way of doing more of it?
AI can help answer the second question in a concrete way. The fake work — the repeatable, the routine, the answerable — is increasingly handleable. The real work, the kind that took years to become good at, is where you still need to be.
Getting that ratio right is what Amanda found. It's a quieter outcome than “10x revenue” or “saved 40 hours a week.” But for the people who experience it, it tends to be the one that matters most.
I help professionals and service business owners build AI systems that free them up for the work that actually requires their expertise. If you're feeling buried by the operational layer of your business, reach out or check out my AI consulting and workshop programs.
