If AI could solve one problem for you tomorrow, what would it be?
Nobody hesitated. Every one of them had an answer ready. Not one of them wanted AI to do the actual job. Nobody wanted it closing their deals or talking to their clients. They wanted it to clear everything standing between them and that work.
The wishes were specific. They were real. And they have one thing in common, which I'll get to at the end.
The president who wants his data in one place
The president of one of Idaho's largest development and homebuilding companies is not an AI skeptic. He builds his own tools. He drops monthly reports into Claude, runs what-if analysis on incentive changes, models profitability six months out. He's ahead of almost everyone I've talked to.
And he's stuck. Not on the technology. On his own data.
"There's not a single source of truth within our organization. The county may have something, the development team has something, purchasing is working in a spreadsheet. The more separate the data is, the more inefficient it is to use the tools."— President, Idaho development company
His wish is simple to say and hard to build. He wants his company's data in one place, so the AI can finally use all of it. Right now everyone on his team runs their own version, their own assumptions, their own files. The intelligence is there. The foundation under it isn't.
The broker who wants a team he doesn't have to hire
A brokerage owner I spoke with runs a busy shop and admits he knows just enough about AI to be dangerous. But he described what he wants with total clarity. He's seen it online and it stuck with him.
"What would it look like if I could pull up my computer and all of my AI employees have already said, here's what you need to look at. And by the way, I have this marketing campaign ready to go. If you approve it, I can launch it. I think that could be a game changer."— Owner, Idaho brokerage
He wants a team he doesn't have to hire. One assistant handling the inbox he's drowning in. One marketer drafting the campaign. Each one teed up and waiting for his yes. He doesn't want them making the call. He wants them doing the prep so he can make the call faster.
He named the daily version of it too. His inbox. Scanning a flooded screen trying to sort what's urgent from what's noise. He called keeping it clean a "neverending nightmare." That's the busy work. The AI employees are the wish.
The lender who wants it gone, but done right
A commercial real estate executive was the most precise of anyone. He knows exactly which parts of his job he'd hand over tomorrow.
"It's just a lot of keystrokes, taking weird data sets and condensing them into our template. And there's a lot of leases and legal documents to review. Those are the two things I think AI will advance quickly."— EVP, commercial real estate lender
The model and the lease review. The keystroke-heavy, document-heavy work that fills his day and uses almost none of what makes him good at his job. He wants it gone.
But he was the first to name the catch. He's closing a fourteen million dollar loan, and AI missed something elementary in a lease. He caught it. Barely. He had to cut the loan by a million dollars.
"Shame on me for not cross-checking it. We all know it's not perfect."— EVP, commercial real estate lender
So his wish comes with a condition. He doesn't just want AI to do the grunt work. He wants it done accurately, on sensitive data, in a business that can't afford a miss. That's not a toy. That's a real build.
Same instinct, every time
The president, the broker, the lender. Three different roles, three different problems, one identical answer. None of them wanted AI to do the actual job. They wanted it to clear the work around it.
So why doesn't anyone have it yet?
Not because it's impossible. Unified data, agents that prep and draft, secure document review, all of it exists today. The wish isn't the hard part. The build is.
Pulling scattered data into one place without breaking what works. Fitting AI into a real workflow instead of a demo. Doing it on sensitive files, in a business that can't afford a mistake. That is where most companies stall. Not at the idea. At the execution.