Why following steps is not the same as knowing what you’re doing
By Hiran de Silva
There is a subtle but decisive difference between being able to follow instructions and being able to solve a problem.
Most modern software training—especially in Excel and analytics—leans heavily toward the former. Watch a video. Click these buttons. Choose this option in the wizard. Paste this formula. Adjust these arguments. Done.
And to be fair: many people can follow that sequence perfectly.
But that is not aptitude. That is compliance.
The illusion of competence
Social media tutorials, YouTube videos, and even many paid courses optimise for demonstrability, not understanding. They are designed to be reproducible on screen, not transferable to real work.
The pattern is always the same:
- Start with a finished outcome already known to the instructor
- Reverse-engineer a sequence of clicks and formulas
- Package it as “How to do X in Excel”
- Measure success by whether the viewer can recreate the same screenshot
The learner appears productive. The platform gets engagement. Everyone feels good.
Until the moment the context changes.
The cascading dropdown example
Take a classic use case: cascading dropdowns.
Search for it and you’ll find hundreds—possibly thousands—of tutorials. Almost all of them teach the same thing:
- Named ranges
- Helper columns
- Dynamic array formulas
- INDIRECT, FILTER, XLOOKUP, or similar
- Careful structuring so the demo works inside one workbook
If you follow the steps carefully, you will get cascading dropdowns.
But notice what is not being taught:
- Why cascading dropdowns exist in the first place
- What business problem they are solving
- Whether the dropdowns are part of a one-off task or a system
- What happens when the hierarchy changes
- How this behaves with hundreds of users, multiple files, or live data
- Whether the logic belongs in a worksheet at all
The learner has learned how to assemble a mechanism, not how to design a solution.
Product demos masquerading as education
What most people are consuming is not training—it is product demonstration.
Product demos answer the question:
“What buttons do I press to make this feature work?”
Outcome-focused thinking answers a very different question:
“What must exist for this outcome to be reliably achieved?”
Those two questions lead to completely different mental models.
The first produces technicians.
The second produces architects.
Why this matters in the real world
In real organisations, problems are rarely presented as neat tutorials.
Nobody says:
“Please apply this formula with these arguments in cell G14.”
They say:
- “This needs to work for 400 locations.”
- “Finance and operations must see the same numbers.”
- “No one should have to run a consolidation.”
- “It must still work next year when the structure changes.”
- “We can’t afford it to break when one person leaves.”
If your training has only taught you to follow steps, you are stuck the moment the steps don’t exist.
Outcome-focused thinkers start somewhere else
Outcome-focused training does not begin with tools or formulas. It begins with:
- Responsibility
- Scale
- Reach
- Longevity
- Ownership of data
- Where logic belongs
- What must be shared vs what must be local
Only after those questions are answered does it make sense to ask:
- Should this be a formula?
- A dropdown?
- A table?
- A database?
- A service?
- Or something else entirely?
In other words: the tool is chosen last, not first.
The uncomfortable truth
Someone who can follow a hundred Excel tutorials may still be helpless when asked to design a solution from scratch.
And someone who truly understands outcomes may use surprisingly few formulas—sometimes none at all.
This is why organisations keep experiencing “Excel Hell” even though their staff are highly trained.
They’ve been taught what to click, not how to think.
The real skill gap
The gap is not between beginners and advanced users.
It is between:
- Tool-focused learning and
- Outcome-focused thinking
Until that gap is addressed, more tutorials will simply produce more beautifully executed answers to the wrong questions.
And the cycle will continue—slick demos, high engagement, and very little proof that anything meaningful has actually been solved.
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