Here's the situation I was trying to fix.
Every Monday I had to turn a spreadsheet into a brief that a director would read in five minutes and use to make a call. The spreadsheet was fine—accurate, complete, updated by a process everyone trusted. The problem was the translation. Going from rows to a decision-ready paragraph is not a formatting task. It's a judgment task, and I was doing it badly under time pressure.
I'd tried automating it. I'd also tried doing it manually forever. Neither worked. What worked was a specific division of labor: the model does the reading, I do the deciding, and the output is a document designed for a decision rather than a dataset designed for a record.
This workflow is for anyone who has to turn a spreadsheet—or any structured data—into a brief that someone else will act on.
Why This Translation Fails
Three reasons, and they're all about the gap between data and decision.
A Spreadsheet Answers "What," a Brief Answers "So What"
The spreadsheet says revenue was X, churn was Y, tickets were Z. The director doesn't need those numbers—they need to know which ones changed, which ones matter, and what they should do. The spreadsheet has no opinion. The brief needs one.
Most bad briefs are bad because they're summaries. A summary lists what's in the data. A brief takes a position on it.
The Model Will Confidently Summarize the Wrong Thing
If you hand a spreadsheet to a model and ask for a summary, you'll get an accurate description of what's in the data and almost no help deciding anything. It'll surface the largest numbers, not the most meaningful deltas. It'll describe the average and miss the distribution. It'll tell you what the data says and never what it implies.
That's not a model failure. It's a prompt failure. You asked for a summary, and it gave you one.
Judgment Can't Be Delegated, but It Can Be Structured
The judgment step—deciding what matters—has to stay with you. But the inputs to that decision can be assembled by the model. If you know what a decision-maker needs, you can have the model gather exactly that, and then you make the call.
The workflow below is built around that split.
The Workflow
Five steps. The model does three of them. You do two, and they're the two that matter.
Step 1: Define the Decision Before You Touch the Data
Before the spreadsheet, before the model, answer three questions in writing:
Who is reading this? A specific person, with a specific role, who will do something with it.
What decision are they making? Not "staying informed." A real choice—invest more, cut, reallocate, escalate, or wait.
What would change their mind? Which numbers, if they moved, would flip the decision?
If you can't answer all three, you're not ready to build a brief. You're about to build a summary, and summaries get skimmed and forgotten.
This step takes five minutes and it determines everything downstream.
Step 2: Have the Model Assemble the Signal
Now the model earns its place. Don't ask for a summary. Ask for the specific inputs to the decision you defined.
A prompt structure that works:
Here is a spreadsheet with [columns]. The reader is a [role] deciding whether to [decision]. The numbers that would change their mind are [metrics]. Identify:
The three biggest changes since last period, with the delta and the base
Any metric moving in an unexpected direction relative to its recent trend
Any anomaly worth a second look—outliers, missing data, sudden reversals
Do not write a narrative. Return a structured list.
The key move: you're asking for signal, not story. Structured lists are harder to fudge and easier for you to judge. A narrative draft invites you to accept it. A list invites you to check it.
Step 3: Do the Human Pass
This is the step that cannot be automated, and the one that makes the brief worth reading.
Read the model's list and ask, for each item:
Is this actually a change, or noise? A 2% move in a metric that swings 15% weekly is not news.
Does this connect to the decision? If the reader is deciding whether to escalate, a cost variance that doesn't affect the escalation is a distraction.
What's missing? The model can only see the data you gave it. What do you know that isn't in the spreadsheet—a known customer issue, a seasonal pattern, a recent hire—that changes how a number should be read?
What's the story? This is the hard part. Two metrics moved. Are they related? Does one explain the other? The model can suggest correlations; you decide which are real.
The output of this step is not a document. It's a set of judgments: which signals matter, what they mean, and what the reader should do about them.
Step 4: Write the Brief in Decision Order
Now structure it. The standard structure that works, and why each part is there:
The call. One or two sentences. What do you think the reader should do, and why. Lead with this. Don't make them hunt.
The three signals. The minimum evidence needed to support the call. Not everything—the three that matter.
The caveat. What would change the recommendation. One line. This is the honesty step.
The appendix. Everything else, labeled as optional. If the reader wants to verify, it's there.
Notice the shape: recommendation first, evidence second, uncertainty third, data last. This is the inverse of how the spreadsheet is organized, and that inversion is the entire value of the brief.
Step 5: Run the Five-Minute Check
Before you send, read the brief as the reader. Ask:
Could I make the call from this alone? If not, the call isn't clear enough.
Do the signals actually support the call? If there's a gap, either add evidence or soften the call.
Is the caveat real, or is it hedging? A caveat that would never change your mind is noise. A caveat that would is the most valuable line in the document.
Would I forward this? If the reader forwards it, does it stand on its own?
The check takes five minutes and catches most of the ways a brief fails.
The Before and After
Same data, two versions.
Before (the summary):
Weekly metrics attached. Revenue was $412K, up 3% week over week. Churn was 2.1%, down from 2.4%. Support tickets at 340, roughly flat. Pipeline coverage at 2.8x, down from 3.1x. See spreadsheet for details.
After (the brief):
Recommendation: Hold the Q3 hiring plan; don't add the two SDRs yet.
Why: Pipeline coverage dropped from 3.1x to 2.8x this week—the first drop below 3x in eleven weeks. Revenue is up 3%, but the mix shifted toward renewals, and new-business bookings were flat. The coverage trend, not the revenue number, is the signal.
What would change this: If coverage returns above 3x next week, the drop was timing and the hire is fine.
Detail: Full metrics in appendix.
The first is accurate. The second is usable. The second one took about fifteen minutes, and about ten of those were the human pass.
What Still Needs You
The parts of this workflow that don't get automated, and shouldn't:
Defining the decision. The model doesn't know what the reader is deciding. If you don't know either, the brief will be a summary no matter how good the tooling is.
Judging signal from noise. The model surfaces changes. You decide which ones are meaningful in context. This is the core judgment step, and it's the reason the brief has value.
Knowing what's not in the data. Context lives in your head, not the spreadsheet. A number that looks alarming may be expected. A number that looks fine may be hiding a problem. Only you know.
Making the call. A brief that doesn't recommend anything isn't a brief. The model can't make the call, and shouldn't—it doesn't carry the consequences.
Writing the caveat honestly. The line about what would change your mind is the one that keeps the brief from becoming a pitch. It has to be real.
The model reads and assembles. You decide and recommend.
The Ninety-Minute Version
If you have a recurring spreadsheet-to-brief task:
Before the next one, write down the reader, the decision, and the metrics that would change their mind.
Have the model assemble the signal—the biggest changes, the anomalies, the surprises. Ask for a list, not a narrative.
Do the human pass: which signals matter, what they mean, what's missing.
Write it in decision order: call, signals, caveat, appendix.
Run the five-minute check before sending.
Do it once. Then do it again next week, and see whether the second one is faster. The workflow compounds—the definition of the decision gets sharper, the model's signal-assembly gets better with feedback, and the human pass gets quicker because you're no longer reading the whole spreadsheet to find the three numbers that matter.
Better work first. More options next.
Make the workflow earn its place.
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