Turning a regression output into a stakeholder memo with an AI drafting pass

You ran the model. The coefficients are significant, the diagnostics are clean, and the notebook cell prints a tidy summary table. Then a product VP asks, "So what should we do?" and the distance between your statsmodels output and an answer to that question is where most analytics work quietly dies.

An LLM can close a lot of that distance in a first draft. It cannot close the part that matters most: deciding what is true and what is worth saying. Here is a workflow I use and teach that keeps those two jobs separate.

Start by writing the interpretation yourself, in one paragraph

Before any AI touches this, open a scratch file and write the finding in plain language. Not the memo, just the claim. Something like: "Holding tenure and plan tier constant, each additional support ticket in the first 30 days is associated with a 1.8-point drop in the 90-day retention score. The effect is largest for month-to-month customers."

This forces the two decisions no model makes for you. First, which coefficients actually matter to the decision at hand, and which are controls you included for correctness but nobody needs to hear about. Second, the causal register: "associated with" versus "drives" versus "predicts." If you let the LLM pick that verb, it will almost always overclaim, because confident causal language is over-represented in its training data. Pick it yourself.

Feed the model structured facts, not the raw table

Pasting a full regression summary into Claude or ChatGPT and asking for a memo is the common move, and it produces plausible garbage. The model will invent a business rationale, round your numbers in the wrong direction, and confidently describe the reference category backwards.

Instead, hand it a small, curated block you have already checked. I keep it in a fenced structure so the model treats it as data:

FINDING (verified):
- Outcome: 90-day retention score (0-100)
- Key driver: first-30-day support tickets
- Coefficient: -1.8 points per ticket (95% CI: -2.4 to -1.2), p < 0.001
- Controls held constant: tenure, plan tier, region
- Effect modifier: month-to-month customers show ~2.6 pts/ticket
- Sample: 41,200 customers, Jan-Apr 2026
- What this is NOT: not a controlled experiment; observational

AUDIENCE: VP Product + retention PM. Decision on the table:
whether to fund proactive outreach after ticket #2.

Everything in that block is something you confirmed. The model is now doing translation, not analysis. That is the boundary that makes AI drafting safe here.

Prompt for a structure, not prose

My first pass never asks for the finished memo. It asks for scaffolding:

"Using only the facts in the FINDING block, draft a one-page memo in this order: (1) the decision and your recommendation in two sentences, (2) the single most important number and what it means in business terms, (3) the caveat that most limits how far this should be pushed, (4) two concrete next steps. Do not add any statistic that is not in the block. Where you would normally add supporting detail you don't have, insert [NEEDS: ...] instead of inventing it."

Two things earn their keep here. The recommendation goes first, because a stakeholder memo is an argument, not a lab report, and the inverted-pyramid order is what gets it read past line three. And the [NEEDS: ...] instruction turns the model's hallucination tendency into a to-do list. When it writes "[NEEDS: churn cost per customer to size the opportunity]" it has told you exactly what would make the memo land, without fabricating the figure.

The verbs are where you edit hardest

Read the draft once looking at nothing but the verbs and quantifiers. LLM prose drifts toward certainty: "drives," "proves," "will increase," "significantly" used in the English sense rather than the statistical one. On observational data, downgrade relentlessly. "Is associated with" is not weasel language; it is the honest description of what a regression on non-experimental data supports.

Watch three failure modes in particular. Reversed reference categories, where the model says a group is higher when your dummy coding means it is lower. Confidence intervals silently dropped so a range becomes a point estimate. And the classic conflation of statistical significance with practical size, where a tiny but precisely estimated effect gets described as important. The model will not catch these because it never saw the underlying data. You are the only reviewer who can.

Make it re-runnable

If you produce these memos regularly, the FINDING block is worth automating. I have seen teams write a small helper that takes a fitted model object and emits the block: pull coefficients, format the CI, and leave the causal-language and "what this is NOT" fields as manual entries you fill each time. In a notebook environment like Hex or Deepnote you can drop that straight beside the model cell. The point is not full automation. It is that the numbers reach the prompt without being retyped, because retyped numbers are where transcription errors enter.

Snowflake Cortex or a similar in-warehouse function can run the drafting step without data leaving your environment, which matters if the outcome variable is anything sensitive. That constraint should drive the tool choice more than model quality does; for a one-page memo, every current frontier model is more than good enough.

A quick before-and-after

A raw model line reads: tickets_30d coef -1.84, std err 0.31, P>|t| 0.000. The finished memo sentence, after the AI pass and your edit, reads: "Early support friction is one of the few retention factors we can act on directly. Among comparable customers, each additional ticket in the first month tracks with roughly a 1.8-point drop in the 90-day retention score, and the pattern is sharpest for month-to-month plans. This is observational, so treat it as a strong signal to test, not a settled cause." The model wrote a version close to that. You supplied the word "tracks," the last clause, and the decision to lead with actionability.

What to hand off, and what never to

Hand the LLM the layout, the tightening, the plain-language restatement, and the discipline of flagging gaps. Keep for yourself the choice of which numbers matter, the causal verb, the caveat that limits the claim, and the recommendation. If a memo goes out and someone challenges it in a review, the sentences you will have to defend are exactly the ones you should have written by hand. Draft fast with the machine; own every claim.

None of this is a substitute for a proper causal design when the decision warrants one. An AI drafting pass makes an observational finding readable, not causal. If the outreach program gets funded off this memo, the honest next line in it is the request for the experiment that would actually prove the effect.