For a long time, “Excel training” meant something very specific: walking people through features, showing them where things lived, and making sure everyone saw the same buttons and completed the same exercises. That model made sense when Excel itself was simpler and when organizations mainly needed proof that training had occurred.
Attendance mattered more than outcomes. Coverage mattered more than judgment.
But the way Excel is actually used today has changed.
Excel is no longer just a spreadsheet. It’s a modeling surface, a decision-support tool, and often the last mile between data and leadership. Outputs from Excel shape budgets, forecasts, and strategic choices. The cost of getting it wrong is no longer cosmetic.
And yet, much of the Excel training ecosystem still behaves as if nothing has changed.
What’s emerged is a quiet but important divide between two very different kinds of work that happen to use the same tool: traditional Excel training, and what I’d call Excel judgment.
Why legacy Excel training becomes a box-checking exercise
Most traditional Excel training exists to satisfy an organizational requirement rather than to change how people think.
That requirement is rarely stated explicitly, but it’s easy to recognize:
We need to be able to say we trained them.
Large, big-box training providers are optimized for this kind of requirement. They depend on standardized syllabi, predefined exercises, and interchangeable instructors. If one trainer can’t deliver, another steps in and the experience remains effectively the same. What the client is buying is consistency and coverage, not individual perspective.
Success in this model looks familiar:
- everyone attended
- the deck was covered
- the labs ran
- the post-course survey scores look good
These metrics aren’t meaningless. They’re just cosmetic. They measure comfort, not capability.
The interchangeable trainer problem
Interchangeability isn’t a flaw; it’s a requirement for scale. But it has consequences.
Interchangeable trainers can’t optimize for strong points of view, nuance, or judgment. They can’t linger in tradeoffs or say “don’t do this” too often. Those introduce variance, and variance breaks box-checking systems.
So nuance gets flattened. Judgment gets replaced with procedure. Training becomes smooth, pleasant, and safe… and often leaves behavior unchanged.
Excel judgment lives in a different incentive system
Excel judgment shows up in the choices people make. It’s reflected in whether a feature is used at all, how it’s applied, and how its effects ripple downstream. When I work with analysts, conversations naturally move away from buttons and toward decisions.
We talk through things like:
- when Power Query genuinely simplifies a workflow, and when it adds drag
- when a PivotTable clarifies thinking, and when it hides assumptions
- when Copilot accelerates reasoning, and when it creates noise
- when Python in Excel is the right abstraction, and when it’s overkill
These aren’t syllabus topics. They’re judgment calls.
And judgment is taught through explanation, context, and experience… not slides.
Where judgment changes the work
The most visible difference between these two approaches shows up in the room.
Excel judgment often introduces discomfort. It surfaces assumptions that were previously implicit. It sometimes says, calmly and clearly, “This looks impressive, but it answers the wrong question.”
Those moments don’t always feel good. They don’t always produce glowing reviews. But they do change behavior.
Over time, teams start building simpler models. They choose tools more deliberately. They explain their work more clearly and defend it more confidently. The work holds up better under questioning, because the thinking behind it is sound.
That’s the shift legacy training rarely produces: not because it’s bad, but because it isn’t designed to.
To make the distinction concrete, a comparison table follows.
| Dimension | Box-checking Excel training | Excel judgment |
|---|---|---|
| Primary goal | Proof training occurred | Better decisions |
| Trainer role | Content deliverer | Interpreter & guide |
| Replaceability | High | Low |
| Teaching unit | Steps | Mental models |
| Comfort with friction | Low | Necessary |
| Success signal | Survey scores | Capability shift |
A quick note on feedback and accountability
Of course I want feedback.
I want to know what landed, what didn’t, and what could be done better. That input helps me communicate more clearly.
But as data people, we also know that post-class reviews are self-reported data collected at a moment in time. They’re noisy, biased, and heavily influenced by comfort and mood. Useful, but limited.
In judgment-based Excel work, success shows up elsewhere:
- analysts explain why they chose a method
- assumptions are surfaced instead of hidden
- models get simpler and more defensible
- teams reach clarity faster with less rework
Those are observable outcomes. They just don’t fit neatly into smile sheets.
Same tool, different economics
One of the reasons Excel training gets compared purely on price is that very different kinds of work are often treated as if they’re the same thing.
They’re not.
There are two distinct economic models at play, even though the software is identical.
| Excel work type | How it’s priced | What you’re paying for |
|---|---|---|
| Legacy training | Per day / per seat | Coverage & consistency |
| Judgment-based work | Outcome-based | Reduced decision risk |
Legacy Excel training is priced by time and attendance because the product is delivery. The organization is buying a consistent, repeatable experience: a defined syllabus, familiar exercises, and the assurance that everyone was exposed to the same material. That model works when the primary goal is baseline familiarity or compliance. Per-day and per-seat pricing are reasonable proxies for that kind of value.
Judgment-based Excel work is priced differently because the product is different. The value isn’t that training occurred — it’s that fewer wrong decisions happen afterward. Teams spend less time overbuilding models, less time unwinding brittle logic, and less time defending work that doesn’t hold up under scrutiny. Clarity arrives sooner, and mistakes get caught earlier, when they’re still cheap.
This is why pricing often feels confusing when these two models get lumped together. One optimizes for coverage and consistency. The other optimizes for decision quality and downstream impact.
The tool may be the same. The economics are not.
Final thought
Excel hasn’t become simpler. It got more powerful.
And power changes the nature of the work. It raises the cost of mistakes. It increases the importance of judgment. It shifts value away from familiarity and toward discernment.
Organizations that recognize this invest differently. They hire differently. And yes, they pay differently. Not because they’re elitist, but because they’ve learned, often the hard way, what it costs to get decisions wrong at scale.
If this reflects the kind of Excel work your team needs, you can learn more about how I work with organizations here.