How do companies actually measure generative AI productivity gains without just relying on subjective employee feedback?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do companies actually measure generative AI productivity gains without just relying on subjective employee feedback?
3 real searches · 1 real-search pass
empirical studies generative AI productivity measurement randomized controlled trial customer support coding developers productivity 2023 2024Google ↗
OBSERVEDReason
NBER generative AI productivity field experiment customer support workers productivity 14 percentGoogle ↗
OBSERVEDConsequence
METR randomized controlled trial AI coding tools experienced developers 2025 productivity studyGoogle ↗
OBSERVEDReason
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (3)
Classes come from the query-classifier (a local Laya model). 3 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
empirical studies generative AI productivity measurement randomized controlled trial customer support coding developers productivity 2023 2024 Google ↗
NBER generative AI productivity field experiment customer support workers productivity 14 percent Google ↗
METR randomized controlled trial AI coding tools experienced developers 2025 productivity study Google ↗
- Generative AI at Work · nber.org
- Measuring the Productivity Impact of Generative AI · nber.org
- The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers · microsoft.com
- The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers · doi.org
- Shifting Work Patterns with Generative AI · nber.org
- Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity · metr.org
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
empirical studies generative AI productivity measurement randomized controlled trial customer support coding developers productivity 2023 2024 OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
NBER generative AI productivity field experiment customer support workers productivity 14 percent OBSERVED | Google ↗ AI Mode ↗ | — | Consequence | — | — | 1x of 1 | |
METR randomized controlled trial AI coding tools experienced developers 2025 productivity study OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |