Takeaways

• U.S. job growth has slowed sharply—just 22,000 new jobs in August, unemployment at a four-year high (4.3%).

• Kansas City sits higher at 4.7%, up a full point since spring.

• Tariffs, high rates, and AI are all squeezing hiring decisions.

• AI isn’t erasing jobs—it’s reshaping them, changing what roles look like and what they’re worth.

• The leaders gaining ground are the ones pausing, experimenting, and adjusting pay before making top-down bets.

 

The National Backdrop: A Soft Labor Market

The August jobs report confirmed what many already suspected: the U.S. labor market has lost momentum. Only 22,000 jobs were added, well short of expectations, while unemployment climbed to 4.3%—its highest since 2021. Kansas City’s picture is even tougher. Local unemployment stands at 4.7%, a full point higher than in the spring.

 

When we sit with executives across industries, we hear the same tension. Some argue tariffs and high borrowing costs are the bigger culprits—“rates are what’s freezing our growth, not AI.” Others see AI as the real disruptor, explaining how they’ve slowed hiring simply because fewer people are needed to process the same amount of work. Both views hold truth, and both can exist side by side. The bigger point: the stall-out isn’t caused by one variable. It’s an overlapping set of pressures.

 

What AI Is Really Doing to Work

The mistake we see too often is treating AI as a hammer that smashes entire jobs. In practice, AI is less a hammer and more a scalpel—it slices work into parts.

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• Customer Service: A Kansas City logistics firm cut repetitive ticket work by a third after layering AI into its workflows. Those displaced roles weren’t simply eliminated—remaining reps were redeployed into retention and upsell. Contrast that with a local manufacturer that piloted AI in back-office admin but pulled back when quality slipped; the cost of errors outweighed the savings.

• Finance & Operations: In several finance shops, AI now drafts reports, summarizes meetings, and handles routine audits. Yet some CFOs find AI’s draft outputs more distracting than helpful—“we spend as much time fixing it as we would writing it ourselves.” Both experiences are real. The outcome depends on how leaders frame the human–machine division of labor.

• Goldman Sachs estimates 6–7% of U.S. jobs could be displaced at peak adoption, yet history shows most displacement proves temporary. Technology rarely eliminates work; it shifts it.

 

Kansas City Risk Map​

 

 

 

 

 

 

 

 

 

 

 

 

The Hiring Playbook for 2025

Running searches across Kansas City, we’ve found that leaders who navigate this moment best follow a sequence that feels less like guesswork and more like the scientific method:

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• Empower Teams First – Let ops, finance, and sales leaders test AI directly. As one executive told us: “If it saves time, great. If it doesn’t, at least we know before hiring.”

• Document Task Shifts – Some tasks vanish, others grow in importance. Without evidence, hiring becomes blind.

• Adjust Job Design – Yesterday’s title doesn’t map neatly to today’s responsibilities. One COO put it plainly: “The role name is the same. The job isn’t.”

• Recalibrate Compensation – Evidence cuts both ways. Some tasks should be valued less if AI takes them on. Others—judgment, oversight, trust—demand more.

 

The point isn’t whether AI is good or bad. The point is to see what it actually does before hiring against an outdated blueprint.

 

What’s Next for KC Leaders

Yes, the Fed may eventually cut rates. Yes, tariffs may ease. But those variables are outside any leader’s control. AI adoption is not.

 

Leaders who treat AI as an experiment—running small pilots, observing results, then scaling or stopping—are the ones best positioned to avoid over-hiring for work that no longer exists. The trap isn’t failing to adopt AI. The trap is hiring as if AI doesn’t exist.

 

Key Takeaway

AI isn’t a job killer—it’s a job re-designer. Across Kansas City, the executives who are pulling ahead aren’t the ones rushing to hire, but the ones pausing, testing, and then building compensation plans that match the work as it really is—not as it used to be.