It is hard to believe how quickly artificial intelligence has moved from curiosity to urgency. Only a few years ago, AI felt like something you read about and admired from a distance. Today, it is showing up inside the software we use every day, the documents we process, the schedules we fight with, and the engineering work that often defines whether a project succeeds or fails. The challenge is that the AI conversation often gets framed in extremes. Either it is a miracle that fixes everything, or it is a threat that wipes out jobs. I do not see it that way. I see something more practical, and more disruptive.
The next five to ten years will not be a clean break. It will be a steady reallocation of human effort. Less time doing repetitive work. More time verifying outputs, managing exceptions, and owning outcomes.
Many operational office roles will shrink because AI reduces the number of times a human needs to touch a process. Quality control will remain human involved longer than most people assume, because quality becomes the safeguard for automated decisions. Engineering teams will be able to output multiples of what they can today, but the expectations for speed and accuracy will rise right alongside it. In non-complex manufacturing, meaning high volume and low variation, automation will replace labor sooner because the economics are compelling. In complex manufacturing, where there is high variability, jobs will remain for the foreseeable future due to the cost and complexity of automation, though technological solutions will still replace pieces of human labor over time.
In most industrial companies, the office is packed with what I call touches. A touch is any moment where a human has to move information from one place to another, interpret it, retype it, reconcile it, or clean it up.
Quoting is full of touches. Order entry is full of touches. Scheduling is full of touches. Accounting and reporting are full of touches. Customer service is full of touches, especially when people have to hunt through multiple systems just to answer a simple question.
Not because it is magical, but because much of this work is document driven and repetitive. A person reads an email, opens an attachment, finds information, compares it to historical data, enters it into the ERP, sends it to the next step, then fixes what breaks along the way.
Now imagine a workflow where the system drafts the work and the human verifies the work.
That is the future of many office roles.
Today, an RFQ arrives. Someone opens the email, reads the documents, pulls up history, checks drawings, asks questions, builds assumptions, and types it into the ERP. Then the quote becomes an order. Then the order gets corrected. Then purchasing starts chasing parts. Then scheduling tries to make sense of it.
In the near future, the system will read the RFQ and the attachments, identify similar historical jobs, draft a quote, propose a lead time based on actual constraints, and populate most of the order entry fields. A human will still review, especially on complex work, but the human will spend far less time on clerical steps and far more time on what matters.
Here is what that sounds like in plain language:
“Do you still do order entry all day?”
“No. The system drafts it. I handle exceptions and keep customers from getting surprised.”
And here is the reality most leaders will have to face. When you remove touches, you usually remove headcount, or you consolidate work into fewer roles with broader scope. That can be uncomfortable, but it is also an opportunity to elevate roles into higher value work, especially customer support, exception handling, and process ownership.
As AI starts drafting quotes, proposing schedules, creating transactions, and supporting engineering output, quality becomes more than inspecting parts. It becomes inspecting decisions. This is where I believe humans remain involved longer than many people assume.
Quality, in an AI-enabled environment, expands into questions like these:
Are we using the correct revision and customer requirements?
Is our master data clean enough to trust the workflow?
Did the system make a reasonable assumption, or did it fill in a blank incorrectly?
Can we explain why the system did what it did?
Are we confident enough to let automation run, or do we require human sign off?
You will still have product inspection, but you will also have process inspection. People will sample outputs, review exceptions, verify compliance, and maintain an audit trail that can stand up to a customer challenge, a warranty issue, or a formal audit.
When something goes wrong, and something will go wrong, the question will shift:
“The system did exactly what it was told to do.”
“Then what failed?”
“Our data, our process, and our assumptions.”
In a faster organization, the cost of being wrong rises. That is why quality becomes more central, not less.
Author’s Bio:
Travis Young is CEO of Velocity Flow Technologies and has 25 years of experience in product marketing within industrial manufacturing. He focuses on building industrial brands that earn trust, shorten sales cycles, and support long-term growth.
