At The Overlap

It Looked Like Progress

A few months ago, the drugstore down the street reopened after renovations. New shelves. Brighter lights. And a short row of self-checkout machines.

For the first time, one of them was technically wheelchair-accessible with a lower screen and reachable scanner. Since I use a wheelchair to get around, I was genuinely pleased to see that. Progress, I thought.

Until I actually used it. There wasn’t much surface space to place items before scanning. Very little space after scanning. And turning my wheelchair slightly to reach bags while balancing my to-be purchases wasn’t as practical as it looked.

Then the chocolate I picked up had alcohol in it. That required ID verification. One item didn’t scan properly. That required assistance. A discount didn’t register. That required someone to override it.

So there I was, at the “accessible” self-checkout. Maneuvering items. Every second item sliding onto the floor after scanning. Waiting for interventions. Trying not to drop anything from my lap.

It wasn’t unusable. But it definitely wasn’t frictionless either.

I left thinking: That wasn’t less complexity. It was complexity rearranged. And some of it had shifted onto me.

It Wasn’t Less Work. It Was Reassigned.

The machine worked. It likely reduces staffing pressure and shortens queues.

And accessibility matters. Even imperfect accessibility.

The friction didn’t disappear. It moved.

And once you start looking for that pattern, you see it elsewhere too.

---

In Short

• Most improvements don’t eliminate complexity.
• They relocate it. They shift effort. They redistribute uncertainty. They change who carries the friction.
• The visible outcome improves. The underlying trade-offs move.
• And unless we ask where they moved to, we misunderstand what “better” really means.

---

When the Lab “Improves”

In my current line of work, consulting in regulated laboratory environments, I see the same pattern in a different form. This is one specific example, but the concept is ubiquitous. Bear with me.

Imagine a lab testing finished products — say protein bars — before they are released for sale.

At first the lab uses the traditional process. A senior technician reviews every result before approving product release, ensuring your protein bars are not only safe to eat but of high, consistent quality.

Then the lab starts automating parts of their processes, including automating this specific result review process. Now results that fall within predefined limits can be automatically approved by the system, no longer requiring the human “okay”. Results outside those limits, such as a higher moisture content, are flagged and require a human to review.

The advantages are obvious. Faster release. Fewer bottlenecks. Less time spent reviewing routine data. That person is freed up to do other tasks.

And in fast, high-volume environments like food production, that matters. But here’s what actually changes. Before automation, the risk lived in human judgment:

• Did someone miss a subtle pattern?
• Did fatigue affect the review?
• Did two people interpret borderline results differently?

After automation, the risk moves into rule design:

• Were the limits defined correctly?
• Were unusual combinations of results considered?
• Was the validation thorough enough to trust the configuration?

You remove variability from day-to-day decision-making. But you increase dependence on how well the system was set up in the first place.

Whether that shift feels safer depends entirely on where you believe risk is easier to manage — in human judgment or in system configuration.

The uncertainty hasn’t disappeared. It has simply moved upstream. Into configuration, validation, and governance.

Again — nothing vanished. It moved.

When We Simplify the Label

Let’s look at another example. In my last post I mentioned the clean label trend, where food products are often reformulated to meet customer desires for clean labels. Shorter ingredient lists. Fewer additives.

Does all this look nice and “clean” on a label? Not really. One option is to replace sweetness and standardized vitamin premixes with vegetable powders and other whole-food ingredients.

On the label, it looks cleaner. Shorter. More recognizable. Simpler.

But sourcing those powders from different suppliers introduces variability that can’t be ignored. (Which stems from the inherent natural variability in food ingredients — something I touched on in a recent post.)

Unlike standardized sweeteners or vitamin premixes, fruit and vegetable powders contain naturally occurring nutrients and micronutrients in varying concentrations. Their nutrient profiles can also shift with season, supplier, and processing method.

Not to mention that they can also produce noticeably different colors in the final product from batch to batch. And this is definitely something that customers notice. (Don’t tell me you wouldn’t complain if your protein smoothie was a wildly different pink or green from one month to the next.)

Fruit and vegetable powders can also have large differences in particle size and dispersion properties. That means that some powders settle more quickly and create more visible sediment at the bottom.

None of this necessarily makes the product unsafe. But visible consistency, which customers interpret as quality, is harder to maintain. And nutritional consistency becomes harder to standardize precisely.

To manage that variability, specifications become tighter. Supplier parameters require clearer definition. Incoming material checks become more detailed.

The ingredient list looks cleaner. Upstream variability management becomes more demanding.

Again — nothing vanished. It moved.

Redistribution Is Often Intentional

This is the part we rarely describe clearly. Redistribution isn’t always accidental. Sometimes we deliberately move risk.

• In the case of the clean label example, tighter sourcing controls are accepted to gain consumer trust.
• In the case of automation, dependence shifts from individual judgment to system design and validation in exchange for speed and consistency.
• In the case of self-checkouts, small user inconveniences are accepted to gain operational efficiency.

Trade-offs are not mistakes. They are structural choices.

The problem is not that complexity moves. The problem is pretending it doesn’t.

The Question Beneath “Better”

We tend to ask: Is this better?

But more often than not, that question is incomplete.

• Better for whom?
• In which dimension?
• Under what conditions?
• And where did the uncertainty go?

Once you start asking those questions, “improvement” looks different. It stops being a simple upgrade. It becomes a structural choice about where effort, risk, and responsibility will sit.

Progress isn’t the absence of trade-offs. It’s the conscious management of them.

That’s not pessimism. It’s literacy.

And in complex environments, literacy is often more valuable than optimism.

A Question For You: Where have you seen “improvement” quietly move the burden elsewhere?

Author’s Note

The reflections shared here are my own and do not represent the views of my employer or any clients. All examples are generalized to protect confidentiality.