What your team should know before agents read the data
Once a model can summarise the numbers, its summary becomes the version people quote. Three things worth establishing before that happens.
By Joris van Huët, Founder & CEOUpdated 3 min read
Once a model can summarise your measurement, its summary becomes the version people quote. Three things are worth establishing before that happens rather than after.
The three
| Establish | Why |
|---|---|
| The summary is a draft, and who edits it | Otherwise nobody owns the claim |
| Uncertainty language is not optional | A summary that drops the interval overstates by default |
| The source file is the record, not the summary | Disputes must resolve against data, not prose |
Why the ownership question comes first
A model-written summary that nobody edits is a claim with no author. When it turns out to be wrong, there is nobody who decided it, which sounds like nobody is to blame and functions as nobody being accountable.
Name the editor. That person reads the underlying fields, edits the recommendation, and puts their name on it. The tool changed the assembly, not the responsibility.
Uncertainty language
Models drop hedges under summarisation pressure, because a crisp sentence reads better than a qualified one. If the confidence interval is in the data and absent from the summary, the summary is more confident than the evidence.
The fix is a prompt rule and an editorial one: every channel mentioned carries its range, and a channel with an interval spanning break-even is described as unresolved rather than as underperforming.
The source file as the record
When two people disagree about what the read said, the resolution has to be the structured output rather than the prose. Keep the file, link it from the summary, and make it normal to open it. Otherwise the summary becomes the artefact of record and its errors become permanent.
The habit sits alongside the decision log described in keeping the attribution record in your own workspace.
What this changes about team skills
Less assembly, more reading. The scarce skill becomes noticing when a summary is more confident than the fields behind it, which is a specific and teachable thing. A team that can do it gets faster; a team that cannot gets faster at being wrong.
The related dynamics of shared measurement are in what an attribution channel does to a team.
The plumbing
The €99 one-time read on a Google Analytics upload produces the same structured fields, refundable within 30 days, no questions asked.
The sentence worth saying once
The model writes the draft, a person signs it, and the file is the record.
Related answers
Frequently asked questions
Who is responsible for a model-written marketing summary?
Name an editor explicitly. A summary nobody edits is a claim with no author, so when it turns out to be wrong there is nobody who decided it. The tool changed the assembly, not the responsibility.Why do AI summaries overstate attribution findings?
Because models drop hedges under summarisation pressure, since a crisp sentence reads better than a qualified one. If the interval is in the data and missing from the summary, the summary is more confident than the evidence.What should disputes about a read resolve against?
The structured output file, not the prose summary. Keep the file, link it from every summary, and make opening it normal, or the summary becomes the record of account and its errors become permanent.
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Keep reading
Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Confidence IntervalConfidence Interval is a statistical range of values that likely contains the true value of a metric. In marketing analytics, it quantifies uncertainty around estimates, indicating the precision of an outcome or causal effect.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- ShopifyShopify is an ecommerce platform for creating online stores and selling products. Attribution modeling shows which marketing channels drive traffic and conversions within Shopify.