Category: Office & administrative support · SOC 41-9041.00 · Industry: Retail & customer service
High exposure
3 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
2 sources
8 core tasks shown · 4 secondary hidden
Telemarketers Very High exposure: 6 of 8 core tasks show high task automation potential : exposure 67/100 (high exposure).
1 model 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 6df5e4d4827b2db0
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.
2 evidence item(s) contribute. Calculated 19 hours ago. 3 Oct 2026 UTC.
Solicit donations or orders for goods or services over the telephone.
Employment
≈90,000
approx. BLS OES
Median wage
≈$39,000
approx. BLS OES
Tasks tracked
12
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 4.8/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.3/5
Killers
Ranked by strongest demonstrated capability. Task and evidence counts come from the attributed evidence trail below.
OpenAI · 1 evidence item
Evidence
2 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
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
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.
GPT-4o added a single model handling text, audio and image, enabling sub-second conversational voice — the capability precondition for substituting routine telephone work.
Zoom will integrate Claude into its Contact Center portfolio, and Zoom Ventures invests in Anthropic.
Related
Occupations sharing major SOC group (41) with Telemarketers.
faq
Common questions regarding Telemarketers and AI exposure.
Current AI models show an exposure score of 67/100 for Telemarketers. 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 Telemarketers include "Maintain records of contacts, accounts, and orders." (85% exposure) and "Record names, addresses, purchases, and reactions of prospects contacted." (85% exposure).
Tasks with the lowest AI exposure for Telemarketers include "Contact businesses or private individuals by telephone to solicit sales for goods or services, or to request donations…" (30% exposure) and "Obtain customer information such as name, address, and payment method, and enter orders into computers." (35% exposure). These responsibilities typically require physical dexterity, licensed human accountability, or complex interpersonal navigation.
Embed this interactive AI exposure card into your article, research, or dashboard.
We monitor frontier AI model releases and benchmark updates. Enter your email for alerts when this occupation's exposure changes. No spam, ever.
By submitting, you consent to receive career disruption updates. We never share your data. Read our Privacy Policy.
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.