---
id: "action-setup-poc"
type: "action-item"
source_timestamps: ["§ The Road Ahead"]
tags: ["implementation", "validation"]
related: ["framework-ai-moderation-use-cases", "action-establish-metrics"]
action: "Set up a proof of concept comparing AI moderation to traditional methods using a small sample."
outcome: "Empirical validation of AI moderation efficacy and establishment of internal benchmarks before full-scale deployment."
sources: ["commercial"]
sourceVaultSlug: "hbr-seg-commercial"
originDay: 5
articleStem: "hbr-new-30-ai-scale-customer-research"
sourceUrl: "https://hbr.org/2026/04/how-ai-helps-scale-qualitative-customer-research"
sourceTitle: "How AI Helps Scale Qualitative Customer Research"
---
# Set Up an AI Moderation Proof of Concept

**Action.** Before fully committing to AI moderation, set up a **proof of concept (POC)**: run a small-sample test comparing the **results, speed, and cost** of an AI moderator against traditional human-led methods to establish internal benchmarks and validate the technology for your specific organizational needs.

**Expected outcome.** Empirical validation of AI-moderation efficacy and internal benchmarks before full-scale deployment.

Pair the POC with the use-case diagnostic in [[framework-ai-moderation-use-cases]] (pick the situation you're testing) and follow it immediately with [[action-establish-metrics]] so the POC produces *scientifically interpretable* results rather than a one-off demo. Enrichment reinforces this: fast large-scale AI qual can **amplify methodological errors** (Pearson, 2024) if the instrument is poorly designed — the POC is the guardrail against multiplying a biased instrument across thousands of sessions.