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Define a business metric before testing AI answers

Agree on one metric, connect an approved data slice, and evaluate twenty questions against analyst-verified answers.

Setup time

4-8 hours

Time saved

Measure in a pilot; savings depend on the task

Best for

Enterprise data teams

Tools

Notion, AtScale for AI, Google Sheets

Quick answer

How does the “Define a business metric before testing AI answers” workflow work?

AtScale for AI is aimed at governed enterprise metrics. This is a small evaluation, not a full platform rollout: choose one metric and use a supported data source. Data engineering and vendor access are prerequisites; teams without a warehouse can begin with a metric dictionary and manual answers. It takes about 4-8 hours, uses Notion, AtScale for AI, Google Sheets, and follows 4 documented steps.

Published by GPTNavi Editorial TeamLast materially updated

Built from public product information and practical workflow-design patterns. Verify current pricing, features, and policies with each provider.

Overview

AtScale for AI is aimed at governed enterprise metrics. This is a small evaluation, not a full platform rollout: choose one metric and use a supported data source. Data engineering and vendor access are prerequisites; teams without a warehouse can begin with a metric dictionary and manual answers.

When to use this workflow

Revenue-definition consistency
Internal metric question answering

Tools you need

Notion

Workspace

Freemium

Workspace for docs, databases, calendars, SOPs, and team knowledge bases.

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Paid

Enterprise AI capabilities built on a semantic layer for shared business definitions, governed computation, and answer evaluation. Useful when teams disagree about what a metric actually means.

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Google Sheets

Spreadsheet

Free

Spreadsheet tool for lists, planning, lightweight reporting, and repeatable review workflows.

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Step-by-step workflow

1

Approve one metric definition

Ask the metric owner to define formula, source, grain, currency, time zone, exclusions, and reporting period. Include examples such as refunded orders and late records, then sign off the definition.

Tool used

Notion

Expected output

An approved metric contract

2

Model an approved data slice

With the data team and AtScale, confirm supported connectors, access restrictions, and the appropriate modeling and computation capabilities. Implement the metric over a small approved slice and reconcile totals with the existing report.

Tool used

AtScale for AI

Expected output

A reconciled metric model

3

Prepare independent questions

Write twenty questions covering normal totals, comparisons, ambiguous dates, empty slices, and unauthorized dimensions. Have an analyst calculate expected answers, units, and permitted access independently of the model.

Tool used

Google Sheets

Expected output

A labelled evaluation set

4

Evaluate and fix the source of errors

Evaluate the selected AtScale answer path against the fixed questions. Compare exact figures, period interpretation, clarification behavior, and access. Assign failures to metric definition, data quality, permissions, or question interpretation; rerun after changes and retain the previous baseline.

Tool used

AtScale for AI

Expected output

An evaluation report with owners and a baseline

Prompt templates

Prompt to adapt

For [metric definition] and [question], identify the requested period, grain, units, and access scope. Ask for clarification when these are missing. Use only approved computed values [results]; cite the metric version and do not invent missing figures.

Automation ideas

  • Rerun the fixed evaluation when the model, metric definition, or source schema changes.

Common mistakes

  • Changing the metric formula to make an answer look correct
  • Testing only easy aggregate questions

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