I can't tell you how many meetings I have been in where I have raised my hand and said "It's a cool idea to try to connect X to Y, but we'd need to do the infrastructure work before we'd see any results." It's just part of the gig — Operations often plays the role of bursting bubbles or throwing brakes on runaway ideas. Often I even agreed the idea was novel or interesting — but the ROI calculation, measuring the level of effort vs. the potential discovery, just doesn't work.
But what if AI allows us to change the calculation?
** I Need Coffee **
I've been testing a number of processes for months now. Trying to build skills, attempting agent builds, learning, listening to podcasts. The world is changing, and I am determined to be a part of that change.
It's late — and echoing through my mind is a conversation I was having recently about a problem most enterprises deal with: measuring fields in a CRM which rely on hundreds of sellers to fill them out correctly. The data always comes back mostly unpopulated, often misspelled, hastily completed. The signal should be there, but the structure keeps the insight just out of reach.
What if I just query all the account fields for the keywords I need? Or might need? And while I'm at it, query all the opportunities. And all the support cases. And all the seller notes…
I've said "No" to this request a hundred times. It's a week of time for a data engineer, with no promise of insights, let alone real signal. The SQL joins, the data mismatches — it's prohibitively challenging. I can't burn the team's time on this. But what about the AI? What if I just… try… myself?
** Last Cup, Then Bed **
"That's wrong, adjust, rerun."
"Kill that source, we aren't getting anywhere there."
"Try this source — I know it's big, but just use the one field, blend by account number."
"That's interesting. Drill in there."
"Yeah. We are on to something."
Ok… so… what AM I looking at?
It wasn't a finished product. It was a cleared path. Nothing about the problem got easier — but the cost of exploring solutions went to zero. AI had done the dozens of hours of menial data scrubbing, re-pointing queries, and just drudgery to get me to the point where I had something ready for a human to work on.
The real work hadn't even started — but as an operator, historically the judge of whether or not the juice will be worth the squeeze, AI let me change my internal calculation from "That probably won't work, and it's not worth the effort" to "That could work, so let's give it a try."
The constraint was never the idea. It was the cost of testing it.