LO
Partnerships / channel 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
● 1 verified event for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
A low start — before 2022 the research, the chasing and the reconciliation were all manual, and there was no cheap way to shorten any of them. The steep middle is three separate things arriving at once and all pointing the same way: desk research on a candidate partner collapsing from weeks to an afternoon, connector code becoming near-free, and attribution across two systems becoming a query. It flattens because what is left is the part where someone has to commit the company and be held to it. Note the shape this produces in practice: the cheap half is the half that makes partnerships exist, so the visible result is more signed partners rather than fewer managers — the pressure shows up as portfolio size, which no automation index measures.
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
- 2026-09-11
- Basis of the task judgements
- 1 evidence-backed · 5 platform inference · 0 not enough evidence
- Verified events
- 1