AI is eliminating routine work—but the real shift is who takes responsibility when automation ends.
By Carter Barczak
Appeared in Cityview Magazine, Vol. 42, Issue 2 (March/April 2026)
Most conversations about artificial intelligence and job loss start the same way. They focus on what’s disappearing. Analysts, coordinators, customer support roles, entry-level technical jobs — work that lives almost entirely on a screen and follows a repeatable pattern. And that concern isn’t misplaced. I’ve watched it happen in real time. I’ve seen roles quietly phased out after workflows were automated, responsibilities folded into systems, and teams told that “the process is just more efficient now.” That reality is uncomfortable. But it’s also incomplete.
What gets far less attention is what’s happening at the same time. While AI is cutting roles on the surface, it’s creating an entirely new set of opportunities underneath — and not just at the company level, but at the individual level. The biggest shift isn’t simply that jobs are going away. It’s that people now have the ability to automate parts of their own job and use that leverage to move into work that actually matters.
That’s the real inflection point.
The winners in this next phase won’t be the people who avoid AI or complain about it. They’ll be the ones who use it to eliminate low-value work from their day, reposition themselves closer to decision-making, and become harder to replace in the process.
By the time most people realize their job is being automated, it’s usually too late. But what gets lost in the noise is that the same tools eliminating roles are also quietly turning individual workers into force multipliers. I felt this shift personally while learning tools like Power Automate. It became obvious how much routine work exists simply because “that’s how it’s always been done.” Once automated, those tasks don’t disappear — they just stop demanding constant human attention.
For the first time, a single person can automate pieces of their own job that once justified entire positions. Tasks that used to require junior analysts, coordinators, or support staff can now run quietly in the background. That doesn’t just change headcount decisions. It changes who becomes valuable inside an organization.
If part of your job is repetitive, manual, or rule-based, AI gives you a choice. You can wait for that work to be automated out from under you, or you can automate it yourself and reinvest that time into higher-value responsibilities. That shift — from execution to ownership — is where new roles are actually being created.
This distinction matters because AI is exceptionally good at removing the middle. Not the top and not the bottom, but the layers built around predictable execution. Reporting roles, static analysis, manual coordination, and process-heavy work are the easiest targets because they produce standardized outputs without real ownership. When software can do that faster and cheaper, organizations respond the same way they always have: they compress.
What AI still struggles with is accountability. It doesn’t own outcomes. It doesn’t absorb risk. It doesn’t make judgment calls when assumptions break. And as automation increases, that’s where human value concentrates.
That shift becomes easier to understand when you look outside the screen.
I see it play out every day in my own work around large-scale data-center developments — the physical backbone of the AI economy. These facilities are anything but abstract. A single data center campus can stretch across more than 100 acres, draw more electricity than a city the size of Knoxville, and represent billions of dollars in combined projects and infrastructure.
Automation doesn’t eliminate work — It redistributes it.
At peak construction, data center sites can involve thousands of workers on the ground at once — electricians, mechanical trades, concrete crews, steel workers, commissioning teams, safety managers, and logistics coordinators all operating in parallel under tight schedules and unforgiving constraints.
Once the buildings are live, the human involvement doesn’t disappear. Facilities engineers, operations teams, maintenance specialists, and network staff remain embedded for the life of the asset. These environments are highly automated, but they are not autonomous. Software can help forecast demand, optimize layouts, and track equipment. It cannot decide how much risk is acceptable. It cannot navigate supply-chain delays, permitting challenges, weather disruptions, or equipment failures without human intervention. And when something goes wrong, it can’t own the consequences. That’s the part of the automation conversation most people miss.
Automation doesn’t eliminate work — it redistributes it. It reduces the amount of pure execution required and increases the value of coordination, judgment, and ownership.
This pattern shows up anywhere work meets the physical world — energy, infrastructure, healthcare, manufacturing, logistics. The closer the work is to irreversible outcomes, the harder it is to automate completely.
That’s why much of the career advice around AI falls short. Learning tools is useful, but it’s not enough. The real advantage comes from understanding where automation makes sense — and where human judgment still matters.
This is where individuals have more leverage than they realize. AI gives you the ability to remove the lowest-value parts of your own job before someone else does it for you. Not by waiting for permission or making a big announcement, but by quietly automating the repetitive work that doesn’t require judgment and using that time to focus on work that does.
Those shifts aren’t flashy, but they compound. Over time, the people who make them become harder to replace because their value isn’t tied to how much work they produce. It’s tied to context, judgment, and ownership.
AI will eliminate jobs. That part of the story is real. But it will also expose something deeper: many roles existed primarily to execute work that no one truly owned.
Because AI doesn’t replace people who do work. It replaces people who never owned it.
Comments are closed.