Good AI Task

AI compatibility

Migrating donor JSON to a flat CRM CSV is exactly the kind of data work AI handles cleanly.

Good fit

AI can handle this.

Average across 1 submission.

88
avg / 100

The honest read

This is a well-scoped data transformation task with clear inputs, deterministic success criteria, and low ambiguity. The aggregation logic is straightforward, address standardization is a solved problem, and the output format is fully specified. The main risk is data loss or misalignment during transformation, which is easily caught with row-count and sum validation checks.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The transformation logic is structurally identical for every record: flatten nested donation history, compute four aggregate fields, and standardize address formatting. This runs the same way every quarter with no judgment variation.

Ambiguity Tolerance

High

Success criteria are concrete and verifiable: 1,100 output rows, correct aggregation totals, consistent address format, and no dropped records. A script can self-validate against the source data.

Data & Tool Availability

High

The agent needs only the exported JSON file and a Python or similar scripting environment — no external APIs, live systems, or special permissions required. Everything needed is self-contained.

Error Cost

Medium

Errors like miscounted gifts or wrong totals could corrupt donor records in the new CRM, which matters for fundraising accuracy. However, the migration is reversible — the source JSON is preserved and output can be validated before import.

Human Judgment Required

Low

Address standardization follows well-established rules (USPS formatting, regex normalization), and missing phone numbers can be flagged rather than guessed. No subjective or relational judgment is needed.

What an agent would need

  • Access to the exported JSON file containing all 1,100 donor records and nested donation history
  • A defined address standardization spec or target format (e.g., USPS standard, title case, abbreviated state codes)
  • Clear rules for handling missing phone numbers (e.g., leave blank, flag with a sentinel value)
  • A Python or scripting environment capable of reading JSON, performing aggregations, and writing CSV output
  • A validation step comparing row counts, gift counts, and total amounts between source JSON and output CSV

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