For many of the teams I work with, Excel isn’t an occasional tool. It’s the foundation of how decisions get made. Forecasts, budgets, scenario plans, capacity models, pricing analysis, investment cases… they all run through spreadsheets at some point.
That’s fine. Excel is flexible, transparent, and accessible. The risk isn’t that Excel is a bad tool. The risk is that it can quietly produce numbers that feel trustworthy even when their decision value has deteriorated.
You won’t see this risk show up in a green error indicator. You won’t get a circular reference warning. That’s why it’s hard to spot until it actually hurts you.
In this post I want to unpack an idea I’ve been thinking about a lot lately: the difference between accuracy and what I call decision-readiness. Understanding that difference will help you spot the places where Excel models can mislead even experienced analysts and leaders.
Accuracy vs. decision-readiness
When people talk about analytics quality, they almost always start with accuracy. It makes sense: if the math is wrong, everything built on it is suspect. So we check formulas, reconcile totals, compare outputs to expectations.
But accuracy is only part of the story.
What really matters for decisions is whether the model is appropriate to use in the current decision context. That’s what I mean by decision-readiness. A model can be accurate in a technical sense but still be inappropriate or risky to use for a particular decision.
Here’s a simple way to think about it:
Accuracy vs. Decision-Readiness Matrix
| High Decision-Readiness | Low Decision-Readiness | |
|---|---|---|
| High Accuracy | Decision-Ready Analysis: Sound math and clear context | False Confidence: Correct numbers, unclear fit for the decision |
| Low Accuracy | Fragile Insight: Good intent, weak implementation | Known Brokenness: Errors are obvious |
Decision-ready analysis is what you want: outputs that are correct and appropriate for the decision at hand.
False confidence is the quadrant that gets people in trouble. The math looks fine, so the numbers carry weight. Meanwhile, assumptions, ownership, and context aren’t clear, and the model is being asked to do more than it was designed for.
What decision-readiness looks like in real Excel work
Decision-readiness isn’t abstract. It shows up in very practical, very Excel-specific ways.
It starts with assumptions. In many spreadsheets, key assumptions live inside formulas, helper tabs, or hard-coded values that haven’t been touched in months. Growth rates, thresholds, exclusions, seasonality adjustments… they’re technically visible if you dig, but they’re not top of mind. When assumptions are explicit and easy to point to, they can be revisited when conditions change. When they’re buried, they quietly shape outcomes long after their original rationale has faded.
Ownership matters just as much. I don’t mean ownership of the file in the sense of “who last saved it,” but ownership of the logic. Someone should be able to explain why the model is structured the way it is and why that structure makes sense for the decision it supports. If the only people who really understand the logic have moved teams, left the company, or simply stopped thinking about the model day to day, accuracy alone doesn’t buy you much safety.
Context is another common failure point. A forecasting model built to support monthly planning gradually becomes the number everyone quotes in exec meetings. A scenario analysis meant to explore ranges turns into a single point estimate that anchors decisions. The spreadsheet hasn’t changed, but the decision stakes have. Decision-ready analysis makes those boundaries clear instead of letting usage drift silently.
Review also has to go beyond “does it still calculate.” In Excel terms, that means more than refreshing queries and checking that formulas didn’t break. It means stepping back and asking whether the logic still fits the decision being made. Accuracy can be tested with reconciliations and spot checks. Decision-readiness is tested by asking whether the model still reflects current reality.
How risk actually sneaks in
When people talk about “Excel risk,” they often picture a typo in a formula. In my experience, that is rarely the biggest issue. The more serious problems usually come from patterns like these.
Inputs get overridden at the last minute to “make the numbers work,” and those overrides become permanent. Logic gets copied forward month after month, even as the business changes. Assumptions that were once discussed explicitly are now just “how the model works.” A file that lived on one analyst’s desktop becomes a shared reference point across teams.
In each case, Excel continues to do exactly what it is told to do. There is no error message. The outputs still look reasonable. That is what makes this kind of risk so persistent.
| Area | How risk enters | Why it’s hard to detect |
|---|---|---|
| Inputs | Manual overrides, external data, ad-hoc adjustments | Refresh cycles and totals still work |
| Transformations | Pasted logic, layers of calculation | Output still “balances” |
| Assumptions | Hard-coded thresholds, implicit business rules | Nobody reviews them anymore |
| Usage | Reuse beyond original purpose | The model “seems fine” in a new context |
In each case, Excel continues to produce results. There is no red flag. The numbers don’t betray anything obvious. That’s what makes this so insidious.
A model built for one use case becomes a go-to reference for another. A file that lives on someone’s desktop gets shared and repurposed. An assumption that was explicit six months ago is now “just
how we do it.”
Why confidence erodes quietly
When decision-readiness declines, it rarely collapses in a dramatic way. Instead, confidence drifts.
You might hear:
- “It’s directionally right.”
- “This has always worked before.”
- “We just need something to anchor discussions.”
Those phrases are subtle signals that decision-readiness has weakened.
Teams often compensate with process (reviews, reconciliations, parallel models, etc.) but that doesn’t fix the root issue. It just adds friction.
The real opportunity is to make judgment explicit again: what assumptions really matter, who is accountable for them, and when a model needs reevaluation.
This is not primarily a tooling problem
One of the common reactions when confidence erodes is to blame the tool: Excel is the culprit, or so the thinking goes. But the same patterns I describe here show up in BI dashboards, planning tools, and custom software.
Changing tools does not address the underlying issue: the way judgment and ownership are handled in analysis workflows.
Accuracy is easy to automate and test. Decision-readiness is social and contextual.
What decision-ready Excel analysis looks like
Excel workflows that reliably support decisions share a handful of characteristics:
| Dimension | What it looks like |
|---|---|
| Assumptions | Documented, revisited regularly |
| Ownership | Clear responsibility for logic and context |
| Context | Defined use case and limits |
| Transparency | Anyone can explain why the model guides this decision |
| Review | Focuses on relevance, not just math |
When these elements are present, leadership can challenge assumptions, adapt to new conditions, and trust the outputs where appropriate. When they’re absent, decisions rely on numbers that feel authoritative but lack grounding.
A more effective starting point
If you want to reduce Excel-related decision risk, the first question shouldn’t be “what’s the next tool we adopt?” or “how do we tidy this spreadsheet?” Instead, start with questions like:
- What decisions depend on this analysis?
- What assumptions drive the outputs?
- When were those assumptions last checked?
- Who can explain the logic today, not six months ago?
This shift in focus from accuracy alone to decision-readiness helps organizations treat models as tools for informed judgment rather than comforting precision.
Closing thoughts
Excel is a durable, capable platform. The real challenge is not the calculations. It is making sure the numbers we produce are still appropriate to guide the decisions we are using them for.
If this post is ringing a bell, and you are wondering whether hidden risk might be building up in your own Excel-based workflows, get in touch for a discovery call:
I would be glad to talk through how this shows up for your team and where it might make sense to dig deeper.