Groups resist to change - the bigger the group, the most resistance there is.
As a leader, pushing for rapid change cannot really be nuanced lest the push dissipates into the organization's entropy.
As a leader, pushing for rapid change cannot really be nuanced lest the push dissipates into the organization's entropy.
It's irrational to push for tokenmaxxing (literally "please increase our AI spending") and not expect that this is the result you are going to get. You won't get productivity increase, since that is not what you are pushing for - you will get token usage maximization (engineers running inane agentic tasks against your code base to increase usage, using company paid AI for their side projects, etc, etc).
Perhaps that is what they were trying to do, but the reality is that all they will have got is a large token bill. The decision makers may have hoped that tokens would be used in most productive fashion possible so they could evaluate if the cost was worth it, but what they will have actually got is what they asked for and measured, high token usage (applied to whatever people needed to do to get their usage stats up, regardless of productivity).
The other business-as-usual factor is that there will be false reporting up the chain, so if the company understands the CEO want to see high AI usage and productivity gains, then s/he will see high AI usage (a large token bill) and will be fed success reports of corresponding productivity gains.
In a typical corporate environment, if all your peers are reporting success, achieving what the CEO wants, do you want to be the only one reporting failure? So - everyone reports high AI usage (easy for the employees to make happen), and most everyone also reports productivity gains if they understand this is the expectation.
If you want to report productivity gains or cost savings from some initiative (increased AI usage or whatever) and need some stats to point to, then you just point to whatever is working, for whatever reason, and attribute the success to the new initiative.
In a company I used to work for, one manager, when pushed to increase machine learning usage (a few years back, before ML became AI), just renamed his product from foo to foo-ML (with ZERO ML usage), and reported how well it is working. He has since been promoted twice.
Do these measurements have sufficient information? As much as any, I'd guess. It sounds like you already know that it's pretty hard in general to measure the productive output of software development organizations.