Low-Hanging Fruit, Long Poles, and the Four-Letter Word: DATA
- Chris Terrell
- Jan 16
- 3 min read
If you’ve been around operations long enough, you’ve seen it happen.
An executive goes to a trade show, has a great dinner, sees something shiny at CES (or whatever the equivalent is for your industry), and comes back convinced they just witnessed The Answer. The next thing you know, there’s a “strategic initiative” to “implement the technology.”
And everybody else quietly thinks, “Cool… but we still can’t even find the right spreadsheet.”
In this episode, Toby and I sat down with David Kilzer (engineer at the core, MBA on the side) and we ended up talking about automation the way it actually shows up in real companies: messy, emotional, data-dependent, and usually backwards.
David’s big point was simple: before you automate anything, you have to start with the business objective.
Not “we want robots. ”Not “we want AI. ”Not “we want to modernize.”
The objective.
Because if you don’t start there, you end up automating chaos. You just make the wrong thing happen faster.
The boring truth nobody likes
Here’s the part I loved: David didn’t romanticize automation.
He described the uncomfortably long work of building strategy first. Pulling knots. Finding the real constraints. Making the long-term vision clear enough that you can actually make decisions against it.
Then—only then—you go hunting for “low-hanging fruit.”
And the way you find it isn’t by asking leadership what’s inspiring.
You ask the people doing the work every day: “What do you hate when you come in?”
That question is magic.
Because it does two things at once:
It finds waste you can actually remove.
It tells the organization, “We’re listening.”
David shared a story from a winding manufacturing plant where the breakthrough wasn’t a robot or a new machine. It was scheduling.
Operators had been saying for ages, “If you ran product A then C then B, we’d minimize wire changes and setup time.” Nobody listened. David’s team listened, changed the release schedule, and saw a 15–20% productivity impact in the first few months.
No machine ran faster.No automation fantasy.Just less avoidable friction.
That’s process debt in its purest form: value trapped inside obvious fixes that nobody prioritized.
The four-letter word that breaks automation
Then David dropped the line that made Toby laugh (and made me nod like a man who has suffered):
D-A-T-A.
Corporate data is universally terrible, and it’s the foundation for any meaningful automation.
Everyone wants the future, but nobody wants the work that makes the future possible.
We talked about product masters—the “easy” stuff companies assume they have nailed: dimensions, weights, packaging, how products stack, how they ship, how they fit on pallets. David described a project where they went through 17,000 items for a large utility company and captured the data at every packaging level (part → box → case → pallet).
That’s not glamorous work. It’s not “innovative.” It won’t get a standing ovation at a conference.
But it’s enabling.
And enabling is the point.
AI and robots: the real blocker isn’t tech
We also got into humanoid robotics and AI—how fast things are moving, how capabilities are compounding when technologies mature at the same time (think: TCP/IP + packet switching becoming the internet; smartphone + GPS becoming… everything).
But the most practical moment might’ve been when David described how AI enters companies emotionally:
Shop floor fear: “Is this going to replace me?”
C-suite fear: “Am I about to get run over by someone who understands this better than I do?”
So the job isn’t “sell AI.” The job is to move people out of fight-or-flight and into grounded, useful examples—then use automation to implement their ideas, not impose yours.
Because when people want it to work, they help you fix it. When they don’t, they sit back with crossed arms and wait for it to fail.
And that’s the hidden cost.
Process Debt Truth: You don’t lose to bad technology. You lose to skipping the boring foundations that make good technology usable.
Comments