It's an investment problem for those who hold the money in the organization.
Say, we have an organisation consisting of exec team and engineering team.
Engineering team has a bright idea that a certain undertaking will reduce time to make one widget. They bring this case to exec team. "It will reduce the cost of a widget from X1 to X2, the project will take T time and cost C."
If exec team sees this investment interesting and possible, they make that investment. The worth of the project is simply its cost C. Exec team willingly paid, and if they got what they wanted for it, they should be happy with "productivity". (Similarly to how you don't bemoan "productivity" of the baker you buy your bread from.)
Yes. Again, properly estimating X1, X2, T and C is so difficult in most cases that this strategy is not really applicable (notable example: technical debt).
Calibrated estimates with huge uncertainty ranges are still useless, even if calibrated.
"One month of refactoring this component can save us between 500k and 50M in the next five years with 90% probability" - this is not very useful for the decision maker, yet it is quite difficult for a domain expert to narrow the range.
It is possible, and in some cases practical, to spend more resources to obtain more information which narrows the range. That's what Applied Information Economics methodology preaches.
Say, we have an organisation consisting of exec team and engineering team.
Engineering team has a bright idea that a certain undertaking will reduce time to make one widget. They bring this case to exec team. "It will reduce the cost of a widget from X1 to X2, the project will take T time and cost C."
If exec team sees this investment interesting and possible, they make that investment. The worth of the project is simply its cost C. Exec team willingly paid, and if they got what they wanted for it, they should be happy with "productivity". (Similarly to how you don't bemoan "productivity" of the baker you buy your bread from.)