Good AI Task

AI compatibility

Cleaning messy survey budget data into bins is a clean win for AI.

Good fit

AI can handle this.

Average across 1 submission.

82
avg / 100

The honest read

This is a well-scoped data cleaning and binning task with clear output criteria and a finite set of transformation patterns to handle. An AI agent can parse messy budget strings, apply deterministic rules, handle edge cases like written-out numbers and ranges, and produce both the cleaned dataset and documented transformation logic. The main risk is a small tail of truly ambiguous entries that need a human spot-check, but that's manageable with a flagging mechanism.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The transformation logic — parse currency strings, convert written numbers, map ranges to bins — is structurally identical across all rows and future surveys. Once rules are codified, the agent can reapply them deterministically.

Ambiguity Tolerance

High

The four output bins are explicitly defined and the task asks for documented rules, so success criteria are crisp. Blanks get flagged rather than guessed, which removes the hardest judgment call.

Data & Tool Availability

High

The input is a CSV export from Google Forms — a standard, accessible format. The agent needs only file access and a Python/pandas environment, both of which are routine.

Error Cost

Low

Miscategorized rows affect market research analysis but not irreversible decisions. The documented transformation rules make errors auditable and correctable before the data is used downstream.

Human Judgment Required

Low

Most entries follow parseable patterns; genuine edge cases (e.g., 'a lot' or 'not sure') should be flagged for human review rather than guessed. The core work is pattern matching, not subjective interpretation.

What an agent would need

  • Access to the exported CSV file with the raw 'budget' column
  • A Python/pandas execution environment with libraries like `word2number` or equivalent for written-out numerals
  • Clear instruction on how to handle blanks and truly unparseable entries (flag vs. assign a default bin)
  • A defined output format: cleaned CSV with new bin column plus a separate transformation rules document
  • Optional: a sample of known-correct mappings to validate the agent's parsing logic before full run

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