# Segment AI gains by experience before standardizing the workflow

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## Segment AI gains by experience before standardizing the workflow

ID: MHC-D-RESEARCH-9188 · Version: 0.2.0 · Kind: checklist
Source: https://vedokrok.com/knowledge/segment-ai-gains-by-experience-before-standardizing-the-workflow

An average gain can hide opposite effects across the experience curve.

### Use when

- A team reports one average AI productivity gain and is considering the same workflow for novices and experts.

### Avoid when

- Do not label people permanently as low- or high-skill from one metric. The cited pattern comes from one customer-support setting and may reverse for other tasks, tools or forms of expertise.

### Explanation

In a 5,172-agent workplace study, AI assistance produced substantial gains for newer and lower-skill workers but little productivity benefit for the most experienced workers, with a small quality decline among the highest-skill group. When roles differ in experience or baseline performance, report AI effects by meaningful skill band. A workflow that is excellent scaffolding for a novice may be friction or standardization pressure for an expert.

### Checklist

- Define experience or baseline-performance groups before reading the result.
- Measure speed and quality separately for each group.
- Inspect whether review burden also changes by group.
- Look for a quality loss among strong performers even when the overall average improves.
- Allow different AI defaults, permissions or review rules when the evidence supports them.

### Example

A support copilot may help a new consultant draft a technically complete first response while making a senior consultant slower if they must remove generic wording and restore context-specific judgment.

### Check

The rollout decision can show who improved, who did not, and whether any group traded quality for speed.

### Limits

- Do not label people permanently as low- or high-skill from one metric. The cited pattern comes from one customer-support setting and may reverse for other tasks, tools or forms of expertise.

### Evidence and sources

- supports: In a staggered deployment to 5,172 customer-support agents, AI assistance increased issues resolved per hour by 15% on average, with much larger gains among less experienced and lower-skill workers. — RS-5512E42A86444868ED6F. The study concerns one firm, occupation and AI assistant; the magnitude and distribution of gains may differ in other work. (Abstract and Introduction)
- supports: The customer-support study reported that less experienced and lower-skill workers improved across productivity measures, while the most experienced and highest-skill workers saw little productivity gain and a small decline in conversation quality. — RS-5512E42A86444868ED6F. Worker skill was measured within the studied support environment; the pattern should not be generalized to every expert or every AI system. (Introduction, heterogeneity results and conclusion)
- RS-5512E42A86444868ED6F: Generative AI at Work — https://doi.org/10.1093/qje/qjae044

No review details supplied.
Published: 2026-10-01T14:27:00Z
Related (useful_with): https://vedokrok.com/knowledge/benchmark-human-plus-ai-against-the-stronger-solo-mode