The Assembly Line Moment: Why Engineering Leaders Must Evolve from Execution to Orchestration

In 1950, factory managers watched robots lift heavy things. By 1980, they were orchestrating entire assembly lines. Today, robotics aren't a competitive advantage—they're table stakes for survival.

Here's what's interesting: software is following the exact same pattern. Just faster.

Danny Presten spent decades in manufacturing and robotics before joining the software world. On the Allstacks Stack Sessions podcast, he laid out something I've been thinking about: "In the fifties, robots lifted heavy stuff. In the eighties, humans managed the robot assembly lines. Now? Robotics are a necessity, not a luxury. Software is following the exact same trajectory—we're just compressing decades into years.

The Identity Crisis No One's Talking About

Think about where we were eighteen months ago. Bragging about using AI to write code was a flex—it signaled you were forward-thinking, adaptive, willing to experiment. Today? If you're not using AI for tactical execution, you're already behind. It's baseline.

And just like robotics in manufacturing, we're approaching the moment where AI-augmented development isn't a competitive edge. It's the cost of entry.

Here's the pattern I'm seeing: the technical skills that got engineering leaders promoted—deep coding ability, architectural expertise, being the person who could solve the hardest problems—those skills are being commoditized. Not eliminated, but commoditized.

The question shifts from "can you execute?" to something different: can you see the whole system? Can you answer the business questions about where engineering investment is actually going and what it's producing?"

The Three Questions That Actually Matter

Danny's insight cuts through all the AI hype and gets to what actually matters at the executive level. There are three fundamental questions that have driven business decisions for centuries:

1. Are we putting our money on the right bets?
2. When will we get ROI?
3. How do we accelerate that ROI?

CEOs asked these questions in 1925. They're asking them today. They'll ask them in 2075.

Here's what I've noticed: technical prowess doesn't answer these questions. Your ability to ship clean code doesn't answer them. Your deep understanding of system architecture—as valuable as it is—doesn't answer them.

What does? Having visibility into where your engineering investment is actually going, what it's producing, and how efficiently it's flowing through your system. Being able to demonstrate that you're maximizing the investment and that teams are aligned with strategic goals.

If you can answer those two questions with evidence, most of the other questions become less critical.

What Software Can Learn from Manufacturing Floor Visibility

Here's why this moment is different: for the first time, software delivery can have the same kind of visibility that manufacturing has had for decades.

Danny could walk a factory floor and immediately see bottlenecks. He could watch the flow of work, identify waste, understand cost-per-unit, and make strategic decisions about where to invest in automation or process improvement.

Software leaders? We had timesheets. Maybe some story points if we were lucky. A lot of self-reported data. You know how many times all of us have filled out a timesheet and just stuffed data in? And then when there's scrutiny, the easiest way to "fix" velocity is to have everybody broadly increase their estimates.

All the sprint reporting looks great—delivered X story points, cycle time is a few days, change failure rate is acceptable. But what's hidden is all the actual work happening. The pet projects. The experiments. The context switching. Too much work in progress.

What's different now: flow metrics, investment visibility, cost-per-feature, work distribution patterns. The ability to look at actual work being done, not just what's being self-reported. To see where engineering capacity is really going, what's creating bottlenecks, where strategic investment will actually move the needle.

Where This Gets Practical

Let's be honest about where AI is right now: probably 25% genuinely valuable, 75% suspect. But remember the manufacturing timeline—robots in the 1950s were clunky, expensive, and limited. By the 1980s, they were running entire production lines.

Software is on the same compressed timeline.

So the question becomes: how do you make decisions about where to deploy AI, where to invest human creativity, and how to answer those three fundamental business questions?

You need visibility. Not just into what your systems are doing, but into how the work actually flows. Where the bottlenecks are. What's creating waste. Whether teams are working on the right things and if they're aligned with strategy.

Without that visibility, you're making decisions based on gut feel and self-reported data. With it, you can demonstrate that you're maximizing business value and that teams are aligned. And if you can provide evidence for those two things, most of the other questions become less critical.

Where to Start 

The shift isn't abstract. Here's what it looks like:

Instead of diving into the codebase to solve the hardest technical problems, you're looking at flow metrics and identifying that 40% of your senior engineering capacity is stuck in a review bottleneck.

Instead of writing the critical pieces of code that "only you" can write, you're analyzing work distribution patterns and spotting that your team is thrashing between too many concurrent initiatives.

Instead of focusing purely on technical elegance, you're using cost-per-feature data to make investment decisions about where engineering time actually matters most.

The value shifts from "I can build anything" to "I can tell you exactly where we should build, why it matters, when we'll see return, and how to accelerate delivery."

It's the same pattern from manufacturing: your value isn't in operating the robots. It's in understanding the assembly line.

Your This-Week Action Plan

If this pattern resonates, here's what might help:

Ask the three questions about your current projects. Take your top five engineering initiatives right now. For each one, try to answer:

• Is this the right bet given our business priorities?
• When will we see measurable ROI?
• What's the constraint preventing us from accelerating that ROI?

If you can't answer these clearly, that's the gap.

Look at where your tactical time is going. Find one thing you're doing that's "in the weeds" execution work. See if AI can handle it. Use that time for strategic thinking instead.

Request flow metrics visibility. If you can't see cycle time, WIP, flow distribution, and investment allocation across your engineering org, you're making decisions without the data. It's like trying to manage a factory floor without being able to walk it.

The Future Belongs to Strategic Orchestrators

AI is going to keep getting better at execution. Much better. Much faster than most engineering leaders expect.

But AI can't make strategic investment decisions. It can't answer whether you're betting on the right initiatives. It can't tell you if your flow bottlenecks are technical or organizational. It can't orchestrate the complex human and technical systems that deliver business value.

Here's what I think happens: the engineering leaders who thrive in the next five years will be the ones who can see the whole system, make investment decisions backed by data, and answer those three fundamental business questions.

In manufacturing, the managers who refused to evolve from operators to orchestrators became obsolete. The ones who embraced the shift became executives. Software is following the same pattern.

The question is: do you have the visibility you need to make that shift?

Allstacks provides the flow metrics and investment visibility that help engineering leaders answer the three questions that matter: Are we maximizing our investments? Are teams aligned with strategic goals? How do we accelerate ROI?