AI compatibility

NPS analysis with 120 responses is a clean, well-scoped job for an AI agent.

Good fit

AI can handle this.

Average across 1 submission.

avg / 100

The honest read

This is a well-scoped data analysis and synthesis task with clear inputs, defined outputs, and low error cost — exactly where AI agents perform reliably. The NPS math is mechanical, the thematic analysis is well within current LLM capability for 120 short texts, and the deliverable format is specific enough to evaluate. The main caveat is that domain-specific nuance in the comments (e.g., niche equipment terminology or industry-specific complaints) may require a light human review pass before the report goes to stakeholders.

Aggregated across 1 submission.

The five dimensions

Repeatability

High

The structure is identical every time: ingest CSV, compute NPS by cohort, run thematic analysis on comments, format into summary and deep-dive. This is a repeatable analytical pipeline with no structural variation.

Ambiguity Tolerance

Medium

The deliverable format (one-page dashboard + 3–4 page report) and analytical goals (promoter vs. detractor drivers, improvement themes, unexpected themes) are clearly stated. Some judgment is needed on how to define and label themes, but success criteria are concrete enough for an agent to self-evaluate.

Data & Tool Availability

High

The CSV is the only required input and is explicitly available. An agent with file access, a Python/pandas environment, and an LLM for text analysis has everything it needs to complete the task end-to-end.

Error Cost

Low

This is an internal analytical report, not a financial decision or public-facing output. Errors are easily caught in human review before the report influences any action, and nothing is irreversible.

Human Judgment Required

Low

NPS segmentation is arithmetic, and thematic clustering of 120 short texts is well within current LLM capability. A human should do a final sanity check on theme labels and framing, but the core work does not require intuition or domain expertise the agent lacks.

What an agent would need

Access to the raw CSV file with all five fields (respondent ID, NPS score, company size, lease tenure, comment)
A code execution environment (Python with pandas, numpy) to compute NPS breakdowns and cohort statistics
An LLM with sufficient context window to process and cluster all 120 open-ended comments in one pass
A document generation tool or template to produce a formatted one-page dashboard and 3–4 page report
Clear definitions of company size cohort boundaries (e.g., SMB vs. mid-market vs. enterprise) if not already encoded in the CSV

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