LO
Telemarketer / telesales agent — how we know
The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Which technologies matter here#
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
How it got here#
The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
Starts at 44 because much of the work around the call was automated before language models: predictive dialers that pass on only answered calls, recorded-message campaigns, keypad payment capture, automated phone surveys and online forms that took over simple order-taking. It rises through 2023 as text models draft scripts and call notes, and more steeply into 2024 as speech recognition and synthesis became fast enough to hold a scripted call, which reached warm appointment booking, order entry and fixed surveys. From 2025 it flattens towards 68: the cold pitch and objection handling, the core of the job, stay with people mainly because of rules on unsolicited and artificial-voice calls and listeners hanging up on machines, so capability kept moving while adoption on those tasks did not.
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
Method and sources#
- Assessment date
- 2026-10-25
- Basis of the task judgements
- 0 evidence-backed · 7 platform inference · 0 not enough evidence
- Verified events
- 0