The most grounded answer I keep hearing across accounting and finance is false.
There’s a phrase that keeps circulating in conversations and panels lately: AI won’t replace you–people who know how to use it will.
That realization is what ultimately led me here–and why Python Muse exists.
If you’re an accountant or finance leader today, this probably feels familiar.
We already operate in a world of constant change: regulations, systems, data, people. Now add AI and automation to the mix, and there’s a new question we’re all quietly asking:
Where do I invest my time, money, and mental energy?
None of those are unlimited.
Like many professionals, I started experimenting. I explored:
And to be clear–these tools are useful. They absolutely can create value.
But they also shared the same friction points.
No matter the platform, I kept seeing the same challenges:
Every tool has a learning curve There’s no avoiding it–especially when your processes are real, imperfect, and evolving.
Every tool comes with a cost Subscription fees are only part of it. The bigger investment is time: research, setup, implementation, and change management for processes that–while clunky–already work.
Durability was always the open question I kept asking myself: How long before I have to relearn this tool, rework the process, or migrate everything again?
That question mattered more to me than finding the flashiest solution.
After a lot of digging, learning, listening, and conversations with others in this space, I made a deliberate decision:
I went back to the basics and started learning Python.
Not because it was trendy–but because it was foundational.
Python isn’t a product. It isn’t tied to a single vendor. And it isn’t going away.
Python is:
The next question I always get is:
“Okay–but how does that actually help in accounting?”
That’s where this journey became very real for me.
Over time, I moved from experimenting to using Python alongside–and often instead of–Excel. Not because Excel can’t solve the problem–it absolutely can–but because doing so often requires complex formulas, multiple tabs, and manual steps that slow performance and increase the risk of crashes. Losing hours of work during a busy month-end is more than frustrating. With Python, those same steps can be automated once, then rerun with the click of a button–producing consistent, reliable results without repeating the same process every time.
What truly accelerated this shift was combining Python with AI copilots and cloud-based coding environments.
Suddenly:
All I really needed was access, curiosity, and a relatively small monthly cost–one that delivered disproportionate value.
For finance professionals who already understand the business, this combination is powerful. It removes friction between ideas and execution.
One resource that helped me immensely when getting comfortable with Python syntax was Python for Everybody.
What stood out to me:
It reinforced something important: Python is what you reach for when spreadsheets stop being enough.
Another unexpected part of this journey was the community.
Seeing the energy around Python–from conferences to groups like PyLadies–was inspiring. These are people building real things, sharing openly, and helping others move from curiosity to confidence.
One of the PyLadies sessions from December especially resonated with me. It offered a grounded look at how Python is used to turn generative AI ideas into real, responsible, practical projects–not hype.
All of this–experimentation, frustration, learning, and inspiration–led to Python Muse.
This site is where I plan to:
This is not about becoming a software engineer. It’s about giving finance professionals durable skills and clearer choices in an AI-driven world.
If this resonates with you, I invite you to explore the site and follow along. This project is just getting started, and the conversation will continue through 2026 and beyond.
Because AI may not replace your job– but learning how to work with it just might redefine it.
By Svetlana Toohey Published March 2026