In 1987, Nobel Prize–winning economist Robert Solow wrote, “You can see the computer age everywhere but in the productivity statistics.”
One reasonable supposition about that would be "we are measuring the wrong thing". Or maybe to put it differently: "we don't really understand how to properly measure this thing called 'productivity'".
The same could be said about AI today.
Yes, and we may still not know how to measure the thing we're trying to measure.
Some 95% of enterprise generative-AI pilots have produced no measurable effect on the bottom line.
A couple of things: "pilots" and "measurable".
A better idea: Blame AI overload
The time and trouble of producing a business report, proposal, plan, slide deck, or budget used to limit how large, how complex, and how frequent such documents were. Now, thanks to AI, people can churn out incredibly complex business communications, ideas, and proposals in a few minutes.
I think there's something to this. That is, with AI the work has changed qualitatively not just quantitatively. There may also be a bit of something analogous to the Heisenberg Uncertainty Principle at play, where we simply can't have perfect knowledge of all parts of the system. When we attempt to measure something, something else changes.
Suppose AI enables you to write three times as many emails as before (say, 30 a day instead of 10). Your email-writing productivity has tripled, making you more valuable to the company. The problem is that every additional email you send lands in someone else’s inbox. Your colleagues, who once got 10 emails from you daily, now get 30. Multiply that across an organization: if 10 people triple their email output, the team gets 300 emails a day instead of 100. If 100 people do the same, that figure blows up to 3,000, three times the reading burden. The AI that makes email writing easy makes email reading hard for everyone else.
I'd guess that something like this is at least part of the issue with why companies are not observing the productivity boost they are anticipating.
(This comment was 100% written by a human, with no AI assistance or support other than the spell-checker built into Firefox)
There'e always the possibility that business operators having enough natural acumen to rake in the bucks for their org without any AI at all are going to always have an unfair advantage going forward.
Whatever degree AI is adopted by their company or not.
In 1987, Nobel Prize–winning economist Robert Solow wrote, “You can see the computer age everywhere but in the productivity statistics.”
One reasonable supposition about that would be "we are measuring the wrong thing". Or maybe to put it differently: "we don't really understand how to properly measure this thing called 'productivity'".
The same could be said about AI today.
Yes, and we may still not know how to measure the thing we're trying to measure.
Some 95% of enterprise generative-AI pilots have produced no measurable effect on the bottom line.
A couple of things: "pilots" and "measurable".
A better idea: Blame AI overload The time and trouble of producing a business report, proposal, plan, slide deck, or budget used to limit how large, how complex, and how frequent such documents were. Now, thanks to AI, people can churn out incredibly complex business communications, ideas, and proposals in a few minutes.
I think there's something to this. That is, with AI the work has changed qualitatively not just quantitatively. There may also be a bit of something analogous to the Heisenberg Uncertainty Principle at play, where we simply can't have perfect knowledge of all parts of the system. When we attempt to measure something, something else changes.
Suppose AI enables you to write three times as many emails as before (say, 30 a day instead of 10). Your email-writing productivity has tripled, making you more valuable to the company. The problem is that every additional email you send lands in someone else’s inbox. Your colleagues, who once got 10 emails from you daily, now get 30. Multiply that across an organization: if 10 people triple their email output, the team gets 300 emails a day instead of 100. If 100 people do the same, that figure blows up to 3,000, three times the reading burden. The AI that makes email writing easy makes email reading hard for everyone else.
I'd guess that something like this is at least part of the issue with why companies are not observing the productivity boost they are anticipating.
(This comment was 100% written by a human, with no AI assistance or support other than the spell-checker built into Firefox)
There'e always the possibility that business operators having enough natural acumen to rake in the bucks for their org without any AI at all are going to always have an unfair advantage going forward.
Whatever degree AI is adopted by their company or not.