LO
Sales / account manager — 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
● 3 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
Lead scoring, quote templates and follow-up reminders were in CRMs long before 2022, so the baseline is not low — sales was never un-automated, it was automated only down to the paperwork and never into the conversation. The two real steps are late 2023, when documents and tool calling let a model read quotes out of email attachments, and 2024, when the price fell far enough to run that on every enquiry rather than the large ones. It flattens at the end because what remains is gated by who may authorise a concession, and no model release moves that.
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.
Written about this#
These pieces argue from the same records this page holds, and each of their sections names what it rests on.
Method and sources#
- Assessment date
- 2026-09-11
- Basis of the task judgements
- 3 evidence-backed · 3 platform inference · 0 not enough evidence
- Verified events
- 3