If the corporate directive was to share "if AI has helped and how" I would agree. But my company started that way and when I tested the new sql query analysis tool and reported (nicely and politely with positive feedback too) that it was making up whole tables to join to (assuming we had a simple "users" table with email/id columns which we did not have due to being a large company with purposefully segmented databases. The users data was only ever presented via api calls, never direct dB access).

My report was entirely unacknowledged along with other reports that had negative findings. The team in charge published a self-report about the success rate and claimed over 90% perfect results.

About a year later, upper management changed to this style of hard requiring LLM usage. To the point of associating LLM api calls from your intellij instance with the git branch you were on and requiring 50% llm usage on a per-pr basis otherwise you would be pip-ed.

This is abusive behavior aimed at generating a positive response the c suite can give to the board.

I know you don't want to hear this, but I also know you know this is true: you would genuinely need to look at the full dataset that team collected to draw any meaningful conclusion here. Your single example means pretty much nothing in terms of whether the tool makes sense at large scale. Not a single tool or technology exists in this entire field that never fails or has issues. You could just as well argue that because you read something wrong on Google or Stack Overflow that those tools should be banned or discouraged, yet that is clearly false.

That said, I don't agree with or advocate the specific rollout methodology your company is using and agree that it feels more abusive and adversarial than helpful. That approach will certainly risk backfiring, even if they aren't wrong about the large-scale usefulness of the tools.

What you're experiencing is perhaps more poor change management than it is a fundamentally bad call about a toolset or technology. They are almost certainly right at scale more than they are wrong; what they're struggling with is how to rapidly re-skill their employee population when it contains many people resistant to change at this scale and pace.