Excel has been there for 40 years, and it continues to stand the test of time. The real question is, Is it worth learning in the Age of AI? I decided to explore what it is by building a mini “M-Pesa Wrapped” (Spotify Wrapped but for my money). Turns out a few formulas could quickly tell me more about my spending habits than a full month of “just checking my balance.”
Spotify Wrapped Ruined Boring Statements For Me
Every December, Spotify tells me my top artist, my “listening age,” and how many minutes I spent listening to the same three songs on repeat. It’s fun. It’s personal. It makes numbers feel like a story.
Then I open my M-Pesa statement, a lot of transactions without any narrative. Every once in a while, after a fun adventure of ‘kurudishia mwili pole’ I always wonder: where did my money go?
What Excel Actually Is (For Anyone Who’s Never Opened It)
It’s a giant table, like a notebook page made entirely of boxes. Each box is called a cell. You can type a number into a cell, a word, a date or a little instruction that tells Excel to do math for you.
That last part is the magic trick. Say you type =B2-C2 into a cell. Excel doesn't just do random calculations, it remembers what’s in that cell and works with it. So if you go back and change what's in box B2, that cell updates itself instantly. No retyping, no redoing the math by hand. Everything connected to it just...updates. That's really the entire reason spreadsheets took over from paper ledgers back in the 1980s, one change and the whole sheet recalculates itself like magic.
Okay, But Is It Outdated?
Short answer: no, but it’s had to evolve.
The honest truth is that Excel isn’t competing to be the most powerful tool anymore, it’s competing to be the most available one. Python and R can do more, but they need setup, syntax, and patience. Power BI and Tableau look slicker, but they require time to learn. Excel is already on your laptop (probably thanks to some cyber in some corner) and you have probably played around with it before.
There’s also a quieter reason it survives: almost every other tool still exports to it. Bank statements, government portals, POS system still hand reports as spreadsheets. Even brand-new AI-powered analytics tools are being built as Excel add-ins rather than Excel replacements, which says something about who actually has the market share.
So not outdated, just no longer lonely at the top.
Three Real-World Uses
1. Financial reporting
Using my M-Pesa statement, I turned a month of scattered transactions into a single monthly summary, including my highest-spend days and what ate my money most(hint: me). This is exactly what accounting software like QuickBooks and Zoho Books do for a business, just automated and prettier. They pull every transaction, group it, and generate a monthly report. Underneath the polish, it’s the same Excel logic: SUMIF, group by category, find total in each group, done! Even the mpesa monthly statement above is built this way.
2. Spotting patterns
Once the data was summarized, one category quietly ate more of my money than anything else. That’s the exact move retailers make when deciding where to cut costs or invest more. Platforms like Jumia’s seller dashboards run this same logic behind the scenes to flag best and worst performing products, just with a live database instead of a spreadsheet.
3. Trend analysis over time
Grouping transactions by day showed me exactly which days I spend the most (weekends are a disaster for my wallet). This is the same logic behind some business decisions you’ve probably seen, e.g. “Terrific Tuesday” probably exists because someone analysed how low Tuesday Pizza sales were and decided to boost it with a deal.
Getting started is very easy, here are very simple functions that helped derive the story from the data:
(Bonus function: conditional formatting to automatically highlight your single biggest transaction of the month in red)
Weekend Projects If You Want to Try This Yourself
Here are some ideas on what you can get started on:
- M-Pesa/bank statement Wrapped — the one from this article. Great starter project, real data, immediate payoff. There is a very nice step-by-step guide to setup the Mpesa Analysis Project here
- A small-business or side-hustle tracker: if you sell anything on the side, track orders, costs, and profit margin the same way a big business would.
- A job-application tracker: dates applied, status, follow-up reminders using conditional formatting to flag anything gone quiet for 2+ weeks.
My Reflection
To track my spending, a pivot table took about three minutes to show me something a full month of casually checking my balance never did, without setting up extra tools. That’s the real case for Excel in 2026, not that it’s the most advanced tool in the room, but that it turns “I think” into “I can see” almost immediately.
Relevant or outdated? After this experiment, I think it is still relevant
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