Excel in the AI era: still valuable or outdated?

10 min read

A logistics analyst once shared a meeting with their boss: “We need to learn Python. Excel is a tool from the previous generation.”

That's not wrong. But it's not accurate enough to guide a decision on what to learn next.

This article doesn't tell you to “keep using Excel” or “drop Excel and learn Python”. It offers a clearer thinking framework to answer that question in the context of your actual work — in an AI era that is changing how every tool works.

Ask the wrong question, and every answer will miss

“Is Excel still valuable?” is the wrong question. It's like asking “is the hammer still valuable in the age of power drills?” — the answer depends entirely on whether you're driving nails or boring holes.

What Excel actually teaches you

When people say “knowing Excel”, they usually mean a set of functions: VLOOKUP, SUMIF, INDEX/MATCH, Pivot Table. But that's the surface layer.

What Excel forces you to build — if you use it seriously — is a specific kind of thinking: decomposing a business problem into verifiable calculation layers.

Take a concrete example. A real payroll file is not just “enter hours worked, get salary”. It has to handle, simultaneously: progressive personal income tax across seven brackets, social insurance contributions capped at the salary ceiling, company-specific allowance coefficients, per-department bonus/penalty rules, and edge conditions for probationary employees. Each variable needs to sit in the right place in the file's architecture, be traceable back to its source, and stay unaffected when someone edits an unrelated cell.

SaaS is not Excel's competitor — they solve different problems

This is what the “Excel vs. modern tools” debate usually overlooks.

When a business moves from Excel to a dedicated HRM, accounting, or inventory system, they are not “upgrading Excel”. They are moving from a flexible thinking tool Switch to operational system with fixed workflows. These are two fundamentally different things.

SaaS is strong where the process is standardized, many people work on a single data source, there is an audit trail, and it scales. Accounting software like MISA or Fast handles journal entries and tax reporting better than any Excel file.

But SaaS has a hard limit: it only answers questions the software designer already thought of.

When the boss asks “if we raise prices by 5% but cut credit terms from 60 days to 45, what's the impact on next quarter's cash flow?” — no software answers that instantly. Because it's a scenario-analysis question, not an operational one. And that's exactly the territory where Excel still has no worthy rival.

The tool triangle: know which tool solves which problem

In reality, most SMEs run all three layers in parallel — whether they realize it or not:

The problem for many SMEs today is using Excel for all three layers — out of convenience, habit, or lack of SaaS budget. The result: Excel files grow bigger, more fragile, and ever more dependent on the one person who understands their structure.

The right answer is not “replace Excel with SaaS”. The right answer is placing each tool in its correct layer.

AI inside Excel — what actually changes

Microsoft 365 Copilot and Google Gemini in Sheets can now write formulas from natural language, spot anomalies in a dataset, summarize trends, and generate pivot tables on demand. This is real acceleration.

But something is happening that few people notice: AI compresses execution time, not judgment time.

Testing Copilot on a real payroll file gave an interesting result. The gross salary formula: correct. The social insurance contribution formula at 8%: arithmetically correct. Contextually wrong — because Copilot didn't know that month applied a reduced rate under Resolution 68, and the phone allowance for that position is not part of the insurance base under the current decree.

The same happens with AI-integrated SaaS. Accounting software can now auto-categorize expenses, suggest journal entries, even summarize finances in Vietnamese. But when it miscategorizes — and it will, on edge cases — the user needs enough background to spot and fix it. AI works the same way in every tool: it amplifies the capability of people who already have foundations, and creates hidden risk for those who don't.

Excel's real weaknesses

Excel has real structural weaknesses, and understanding them matters more than debating whether Excel is still usable.

But these weaknesses are all problems of usage, not of the tool itself. Version control can be handled through disciplined naming and backup. The scale issue can be addressed by knowing when to switch tools — or by pulling data from SaaS into Excel for analysis instead of keeping everything in one place.

Three skill tiers — where are you?

Most people at Tier 1 think they're at Tier 2. This is the most common blind spot — and the reason the question “what to learn next” is usually asked before the question “where am I really” is answered.

Back to that analyst. After more conversation, it turned out their team's problem wasn't that Excel was weak — it was that they were using Excel for all three layers at once: operations, analysis, and a chunk of manual automation were all living in the same file. Python wouldn't solve that. Layered thinking solves it.

The real question isn't Excel, Python, or SaaS. The question is: which layer of problem are you solving, and which tool was designed for that layer?

Frequently asked questions

Is Excel still worth learning in the AI era?

Worth it — but learn the right things. Not more functions, but how to design a file with clear architecture: separating data input, calculation layer, and output display. Excel users at Tier 2-3 will be the most effective AI users, because they have the mental model to check AI output. Users who only know the surface will accept AI results without catching errors.

When should I use Excel, and when should I use SaaS?

Use SaaS when: many people need access to one data source, you need an audit trail, or transaction volume is high (daily sales, inventory, accounting). Use Excel when: asking ad-hoc questions, modeling “what-if” scenarios, or analyzing data pulled from SaaS. Rule: SaaS is the source of truth, Excel is where you ask questions — don't mix the two roles in one place.

Is Microsoft 365 Copilot in Excel actually effective?

Yes — provided you know how to check the results. Copilot writes formulas from natural language, summarizes trends, and builds pivot tables fast. But it doesn't know the edge conditions of your real problem: the latest social insurance decree, your company's specific bonus rules, or exceptions in each customer's contract. Users with a strong Excel foundation will gain a lot from Copilot; those without will create errors that are hard to spot.

Should I learn Python or Excel for data analysis in a small business?

Both, but in order and for the right problem. Excel first — to build the thinking muscle of decomposing a business problem into verifiable calculation layers. Python later — when the problem requires bulk processing, repetition, or connecting multiple data sources. In an SME of 5-50 people, most analysis problems still belong in good Excel — Python is most needed in the automation layer, not the analysis layer.

Our small business uses Excel for both operations and analysis — where do we start separating layers?

Start with the operations layer: pick 1-2 of the highest-transaction processes (sales, inventory, or accounting) and move them to a SaaS that fits your budget. Once SaaS is stable and is the trusted data source, use Excel to pull data out for analysis — at that point Excel will play the analysis role properly instead of doubling as operations. You don't need to do it all at once — separate layer by layer, process by process.

References: Microsoft — Microsoft 365 Copilot in Excel (2024) · Joel Spolsky — How Microsoft Lost the API War (essay, 2004) · Tiago Forte — Building a Second Brain (2022) · Peter Drucker — The Effective Executive (1967)