> And to realize meaningful benefits, it's necessary to move past the pilot testing stage, something few companies have managed.
Huh, why not?
> Among the organizations surveyed, the best generative AI initiatives involved governance (standardizing models to ensure alignment, limiting access to generative AI tools based on role, and so on) and cost reduction, both general and administrative (Q&A testing, debugging, code suggestion, and HR help documentation).
Robust implementations work.
Maybe the sub-text here is that most early adopters didn't implement robust implementations, and failed out because of it.
Agreed. Sometimes the early bird gets the worm, but it's always the
second mouse that gets the cheese.
Hype cycles seem to be getting shorter and more intense, and the
honeymoon is an orgy of capital. The morning after, when the hangover
clears and the drinks bill needs paying there's a thousand firms out
there needing to "do something with AI whatever the cost". That breeds
unnecessary and unethical technology, and it means forcing it on
people through pseudo-markets. It's not just that AI is questionable
as a technology, but that toxic money and motives around it make
things worse.
Huh, why not?
> Among the organizations surveyed, the best generative AI initiatives involved governance (standardizing models to ensure alignment, limiting access to generative AI tools based on role, and so on) and cost reduction, both general and administrative (Q&A testing, debugging, code suggestion, and HR help documentation).
Robust implementations work.
Maybe the sub-text here is that most early adopters didn't implement robust implementations, and failed out because of it.