Category: Office & administrative support · SOC 43-4051.00 · Industry: Retail & customer service
High exposure
3 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
9 sources
7 core tasks shown · 6 secondary hidden
Customer Service Representatives Very High exposure: 6 of 7 core tasks show high task automation potential : exposure 69/100 (high exposure).
2 models with published evidence against this job. Strongest: GPT-4o (92/100 capability).
Score breakdown
Every component is stored with the score row and can be recomputed from the evidence table.
Methodology 2026-09-a · score v1.0 · input hash da9853cc893f7737
Strength of current models on the capabilities this job's tasks require.
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Evidence that AI is actually being used for this work.
Observed changes in postings, employment or earnings.
Quantity and source quality of the evidence behind the score.
9 evidence item(s) contribute. Calculated 19 hours ago. 3 Oct 2026 UTC.
Interact with customers to provide basic or scripted information in response to routine inquiries about products and services. May handle and resolve general complaints. Excludes individuals whose duties are primarily installation, sales, repair, and technical support.
Employment
≈2,800,000
approx. BLS OES
Median wage
≈$39,700
approx. BLS OES
Tasks tracked
13
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Tasks
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 4.7/5
core task · importance 4.5/5
core task · importance 4.4/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.1/5
core task · importance 4.1/5
Killers
Ranked by strongest demonstrated capability. Task and evidence counts come from the attributed evidence trail below.
OpenAI · 1 evidence item
Meta · 1 evidence item
Evidence
9 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
In a randomised controlled trial with about 5,000 customer support agents, access to a generative AI assistant increased issues resolved per hour by about 14% on average, with gains of roughly 34% for the least experienced workers and negligible effects for the most experienced.
The strongest causal evidence available on AI in service work. Note carefully what it does and does not show: it measures output per agent, not headcount. Because it compresses the experience gap, it can reduce demand for senior specialists while raising productivity of new hires.
Source: Brynjolfsson, Li & Raymond — Generative AI at Work (Quarterly Journal of Economics) · Updated 1 Feb 2025
GPT-4o added a single model handling text, audio and image, enabling sub-second conversational voice — the capability precondition for substituting routine telephone work.
Capability event, not an adoption or labour-market event. It raises the capability component for voice-heavy tasks only.
Source: OpenAI · Updated 13 May 2024
Llama 3.1 405B and subsequent open-weight releases let organisations run near-frontier models on their own infrastructure, changing the cost and compliance barriers to automation.
A cost-and-adoption enabler rather than a capability jump. Included because cost is one of the main reasons high capability does not translate into adoption.
Source: Meta AI · Updated 23 Jul 2024
Using O*NET task descriptions scored by GPT-4, the authors estimate that about 80% of the US workforce could have at least 10% of their tasks affected by large language models, and about 19% could see at least 50% of their tasks affected.
This is a *capability and exposure* estimate produced by having a model rate task descriptions — not a measurement of employment change. It is the most-cited exposure figure in the field and is included here as an exposure input, weighted accordingly.
Source: Eloundou, Manning, Mishkin & Rock — GPTs are GPTs (arXiv:2303.10130) · Updated 24 Aug 2023
Estimating that only a small share of tasks are currently cost-effective to automate, the paper projects total-factor-productivity gains of no more than about 0.66% over ten years, with limited aggregate labour-demand effects.
Included as a counterweight. Exposure — even high task exposure — does not imply large aggregate job loss, because adoption depends on cost, reliability, regulation and complementary investment. This evidence lowers the labour-market component rather than capability.
Source: Acemoglu — The Simple Macroeconomics of AI (NBER Working Paper 32487) · Updated 13 May 2024
The AI Index reports continued increases in the share of organisations using generative AI in at least one business function, alongside rapid benchmark gains on reasoning and coding tasks.
Survey-based adoption evidence across firms. Useful for the adoption component and for interpreting why capability and adoption diverge by occupation.
Source: Stanford HAI — Artificial Intelligence Index Report 2025 · Updated 1 Apr 2025
The authors argue that AI can enable workers with less formal training to perform expert tasks, potentially rebuilding middle-class occupational demand rather than only displacing it.
A credible counter-direction hypothesis. Included so that the index does not systematically overstate substitution, and because the strongest experimental evidence (support agents, consultants) shows exactly this experience-compression effect.
Source: Autor, Levy & Solan — Applying AI to Rebuild Middle-Class Jobs (NBER 32140) · Updated 1 Feb 2024
Indeed Hiring Lab scores the skills appearing in job postings against AI capability and reports that the share of skills generative AI could perform has continued to increase, with larger effects for software development and customer-facing roles than for hands-on occupations.
Job postings measure what employers ask for, which is a leading indicator but not employment. Exact series values are not reproduced here; read them from the source. Used to inform the job-posting-change signal type.
Source: Indeed Hiring Lab — AI at Work series · Updated 14 Aug 2025
A meta-analysis of early controlled studies reported an average productivity increase of about 66% across supported tasks, with wide variance by task type.
Useful directionally but the average is dominated by small short-duration studies on well-specified tasks. Weighted low. The variance, not the mean, is the useful finding.
Source: Nielsen Norman Group — AI Improves Employee Productivity by 66% (meta-analysis) · Updated 13 Jul 2023
Historical trend
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Reconstruction, not measurement. 5 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Indeed Hiring Lab scores the skills appearing in job postings against AI capability and reports that the share of skills generative AI could perform has continued to increase, with larger effects for software development and customer-facing roles than for hands-on occupations.
The AI Index reports continued increases in the share of organisations using generative AI in at least one business function, alongside rapid benchmark gains on reasoning and coding tasks.
In a randomised controlled trial with about 5,000 customer support agents, access to a generative AI assistant increased issues resolved per hour by about 14% on average, with gains of roughly 34% for the least experienced workers and negligible effects for the most experienced.
Llama 3.1 405B and subsequent open-weight releases let organisations run near-frontier models on their own infrastructure, changing the cost and compliance barriers to automation.
Estimating that only a small share of tasks are currently cost-effective to automate, the paper projects total-factor-productivity gains of no more than about 0.66% over ten years, with limited aggregate labour-demand effects.
GPT-4o added a single model handling text, audio and image, enabling sub-second conversational voice — the capability precondition for substituting routine telephone work.
The authors argue that AI can enable workers with less formal training to perform expert tasks, potentially rebuilding middle-class occupational demand rather than only displacing it.
Using O*NET task descriptions scored by GPT-4, the authors estimate that about 80% of the US workforce could have at least 10% of their tasks affected by large language models, and about 19% could see at least 50% of their tasks affected.
SK Telecom invests $100 million in Anthropic and partners to build a telco-tuned version of Claude for customer service, marketing, and sales.
A meta-analysis of early controlled studies reported an average productivity increase of about 66% across supported tasks, with wide variance by task type.
Zoom will integrate Claude into its Contact Center portfolio, and Zoom Ventures invests in Anthropic.
Related
Occupations sharing major SOC group (43) with Customer Service Representatives.
faq
Common questions regarding Customer Service Representatives and AI exposure.
Current AI models show an exposure score of 69/100 for Customer Service Representatives. This measures technical capability to assist or automate specific tasks rather than predicted immediate job loss. High exposure indicates strong overlap between current LLM/multimodal capabilities and routine tasks in this occupation.
The most exposed tasks for Customer Service Representatives include "Keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well…" (85% exposure) and "Complete contract forms, prepare change of address records, or issue service discontinuance orders, using computers." (85% exposure).
Tasks with the lowest AI exposure for Customer Service Representatives include "Contact customers to respond to inquiries or to notify them of claim investigation results or any planned adjustments." (15% exposure) and "Determine charges for services requested, collect deposits or payments, or arrange for billing." (75% exposure). These responsibilities typically require physical dexterity, licensed human accountability, or complex interpersonal navigation.
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Notice: All ratings shown are AI-estimated technical exposure scores measuring technical capabilities across O*NET task statements, not predicted job loss. Contains information from O*NET 31.0 by the U.S. Department of Labor, ETA, used under CC BY 4.0.