Executive Summary

Manual product data enrichment scales poorly and introduces operational risk.

This guide models the real cost of manual workflows and outlines the ROI case for structured AI enrichment.

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1. The True Cost of Manual Enrichment

1.1 Cost Per SKU

Research time

Data entry time

Validation time

Correction cycles

1.2 Hidden Costs

Spreadsheet fragmentation

Internal email workflows

Approval bottlenecks

Data inconsistency

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2. Agency & Outsourcing Costs

2.1 Per SKU Pricing Models

Per description cost

Per attribute enrichment cost

Bulk normalization fees

2.2 Quality & Governance Risk

Lack of traceability

No structured modeling

No audit trail

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3. Risk Cost of Poor Data

3.1 Marketplace Rejections

3.2 Compliance Violations

3.3 Conversion Loss

3.4 Customer Trust Impact

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4. ROI Framework for Structured Enrichment

4.1 Baseline Inputs

SKU count

Average attributes per SKU

Manual time per attribute

Labor cost per hour

4.2 Calculating Manual Cost

Formula examples.

4.3 Modeling AI-Enrichment Cost

Hybrid SKU + attribute model comparison.

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5. Payback Period Modeling

Time saved

Reduced error correction

Reduced outsourcing

Increased attribute completeness impact

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6. Executive Justification Template

Questions to answer:

What is current enrichment cost?

What is current error rate?

What is average SKU growth per year?

What is catalog complexity trajectory?

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Conclusion

Manual enrichment does not scale with complexity.

Structured AI enrichment shifts cost from labor to infrastructure.

Infrastructure scales.