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Brynjolfsson, Li, Raymond 2023 (NBER) — generative AI lifts customer-support productivity ~14% with largest gains for novices
In a staggered rollout of a generative-AI-based conversational assistant at a large customer-support contact center, average productivity (issues resolved per hour) rose by approximately 14% post-adoption, with the largest gains concentrated among less-experienced and lower-skilled workers — partly because the AI assistant diffused the conversational patterns of high-performers to lower-performers in real time.
Issues resolved per hour (customer-support agent productivity)Approximately 14% average productivity gain post-adoption; largest gains for less-experienced / lower-skilled workers, much smaller gains for top performers.
- Sample
- Staggered rollout at a large customer-support contact center; exact agent N and call N not extracted to verification.
- Methodology
- Quasi-experimental staggered-adoption analysis with pre/post and treatment/control comparisons; productivity measured via objective issues-resolved-per-hour telemetry.
What this means
- The cleanest mid-2020s field-experiment result on generative AI productivity gains in real workflow conditions — directly cited in nearly every adoption-vs-productivity discussion.
- Heterogeneous effects (novices benefit more than experts) is the load-bearing finding: it predicts where AI substitution operates first and where senior judgment remains differentiating.
- Provides a transaction-cost-economics-compatible reading: AI lowered search + drafting costs for routine customer interactions (low asset specificity), with steepest gains where prior human variance was largest.
Source
National Bureau of Economic Research (NBER) working paper w31161 · Erik Brynjolfsson et al. · 2023 · peer-reviewed
Context
- What came before
- Pre-2023 generative-AI productivity claims were largely vendor-anecdotal or based on small controlled-task experiments. The Brynjolfsson NBER paper was the first large-scale field-quasi-experiment on real workflow productivity.
- What comes next
- Verify exact N (agents + calls), exact methodology of issue-resolution measurement, and exact heterogeneity effects by tenure decile. Connect to the 2025 66-firm field experiment (much narrower individual-level effects) — together they suggest the customer-support setting was an unusually favorable case rather than a generalizable template.
- Where this lands
- Encyclopedia Part I §1.3 (methodology — what we actually have evidence for), Part II (workforce — novice/expert heterogeneity), Part III (CX — direct domain application).