Imagine a perfect world? I have an AI agent that knows my business well. It’s getting real time input from sales, from customers, it makes amazing decisions. We get a large order? We need to ramp up production of our widgets. We have an order pipeline of xx widgets and we know over time that yy% will close. Let’s place a larger order with a supplier overseas.
The next day, we have an election and tariffs are announced on imported parts. We react immediately, cancel the order, start the process to expand a local factory. We place ads to hire workers and order equipment. Things are looking good for our business and our factory will be up and running in a few months.
The next week we find out the tariffs weren’t really being enforced, so they’re paused. Our AI agent re-places our large order for imported parts and tried to cancel the factory expansion. Of course, it calculates the costs of both sides before deciding, and perhaps consults with me on other uses of our local factory.
How many times can we do this? Or rather, how many times would we let an AI agent keep adjusting our business?
To be fair, humans might do the same thing and over-react, but mostly we become hesitant with unexpected news. That slowness can be an asset. We often need time to think and come to a decision. Lots of our decisions aren’t always based on hard facts, and a lot of business isn’t necessarily fact driven either. We often put our thumb on the scales when making decisions because there isn’t a clear path based on just data.
Things can get worse when we collaborate. I used to run real-time reports for an importing company, and we found that executives would print a report, get busy, and after minutes (or hours), discuss the report with someone in a department. However, their numbers rarely matched because the reports were printed at different times. At first they lost trust in the system because the same report on the same day had different numbers. Even when we added a “print” or an “as of” time, the reports were too annoying to users to be helpful because the numbers didn’t match.
Real time isn’t what most of us want. Except in the Olympics. There we want the photo finish right away.
But not in all sports. A review is good. In the NFL, I’ve come to like instant reply. It’s gotten better/faster and often gives us the right answer. Not always, but often. It’s better, arguably, then just real-time humans.
Real-time decisions and reactions can be good in some cases. Adjusting machinery, vehicles, electricity, etc. where we need too-quick-for-humans decisions based on data is a good place for real time data. Lots of business decisions we make aren’t the places where we really need real-time insights. Our human brains just don’t work that fast.
Steve Jones
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In the world of accounting you can bet the accountants and auditors expect tie-out and they are not happy when the do not tie out. This can be a real pain with something like AR (Accounts Receivable) since its a very volatile figure depending on the time of the month. An Aging AR Summary/Detail is a very common report that when run you specify an AS OF date. The problem is that if you run it today with an as of date of say 2025/12/04 and then run it again tomorrow with the same AS of Date you will get different numbers because the amounts are being updated throughout the day. I came up with a solution that captured this data at hour intervals and then forced the report users to always pull from the same day/hour to guaranteed that it mattered not what date they ran the thing only that the AS Of Date was the same. Since doing that 8 years ago we’ve not had single issue with the report from, anyone. The most difficult part was explaining to my superiors WHY we had to do this. They just couldn’t grasp why using the same criteria produced differing values just because of when they ran the report.
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Regarding AI…. I cam across an interesting but about Googles AI. It has since been fixed so you can not reproduce it but at one point Googles AI was returning an answer of “YES” to the question of can you melt an egg. Not some chocolate candy egg but a chicken egg. The piece went into the details of how this happened but what was most important in my mind is it proved how these things are still far from being viable tools that don’t need constant human monitoring. In this case it wasn’t simply that the AI did not think the way humans do but that it was humans who caused this with their needs to optimize profits. We can short circuit our own efforts simply by being greedy.
As I’ve said before, these should be tools used by humans like a PC and not replacements for humans.
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