The Expanding Enterprise Stack, Part 3: The Skills That Will Matter
What Changes Inside Companies When AI Does the Work
Part 1 argued the enterprise stack is expanding, not collapsing. Part 2 forecast how software and digital workforces will coexist. This post is about what changes inside the companies that adopt them.
I saw a post on X this morning. A developer ran 12 parallel AI agents on a codebase refactor. The work took 1 hour, 1 minute, and 40 seconds. It cost $7. He estimated the equivalent human effort at 6 months.
The top reply was the real story: “The 6 months wasn’t ‘saved.’ It was compressed into your architecture decisions. You’re still doing 6 months of thinking. Just not 6 months of typing. The bottleneck moved from execution to judgment.”
Read that again. The bottleneck moved from execution to judgment.
This is not a productivity story. It is a skills story. And if you run or govern an enterprise, it is the most important shift you need to understand right now.
The Inversion
For decades, enterprises have valued execution speed. Ship faster. Type faster. Process faster. Headcount was a proxy for capacity. More people meant more output.
Digital workforces invert this. When execution becomes near-free, the constraint moves upstream. The scarce resource is no longer the hands that do the work. It is the mind that directs it.
The developer said it himself in a follow-up: “The skill bar here is ultra high. My brain power is almost exhausted because all simple tasks are done by AI, and the most complex job is still on me.”
Another reply nailed the implication: “The skill required shifts toward architecture, planning, and systems thinking. I think most people are underestimating the cultural impact of this shift.”
In Part 1, I argued that digital workforces don’t replace software. They consume it. The same logic applies to people. Digital workforces don’t replace your best people. They expose who your best people actually are.
What Changes for Enterprises
In Part 2, I laid out how software companies and digital workforce providers will coexist and where each should invest. Now the question turns inward. If your company adopts digital workforces at scale, what has to change about your people, your culture, and your governance?
Three things.
1. The value of judgment goes vertical.
When a team of AI agents can execute a 6-month refactor in an hour, the quality of the instructions matters more than the speed of the typist. This means your highest-value employees are the ones who can define problems precisely, anticipate edge cases, and make architectural decisions under uncertainty.
This is not new in theory. Every CEO says they value strategic thinking. But in practice, most enterprises still reward volume. Number of tickets closed. Lines of code shipped. Calls made. Reports filed.
Digital workforces make volume metrics meaningless. If an agent can close 500 tickets a day, the person who figured out which 500 tickets actually matter is your MVP.
Action for CEOs: Audit your performance metrics. If more than half of them measure output volume, you are optimizing for a world that is ending. Replace volume metrics with judgment metrics: decision quality, problem framing accuracy, architectural soundness.
Action for boards: Ask management what percentage of the workforce is evaluated on execution speed versus decision quality. If leadership cannot answer this question clearly, that is a red flag.
2. Organizational memory becomes a competitive moat.
In Part 1, I described how digital workforces sit on top of existing software and act as a new layer in the stack. One thing I did not emphasize enough: the layer only works if it has context.
An AI agent running 12 parallel threads on a codebase refactor works because someone made the architecture decisions upfront. Someone encoded the context. Someone defined the constraints. In an enterprise setting, that “someone” is usually a combination of institutional knowledge, process documentation, and tribal wisdom that lives in people’s heads.
The companies that win will be the ones that externalize this knowledge into systems their digital workforces can consume. Not just SOPs and wikis. Living, structured memory that evolves as the business evolves.
This is why the managed services model matters more than the software model for digital workforces. Software gives you a tool. A managed service gives you a team that builds and maintains the organizational memory layer over time. (Yes, this is what we do at Humanity Labs. I am biased. I also believe it is true.)
Action for CEOs: Identify the top 10 decisions your teams make repeatedly. Ask whether those decisions are documented well enough for a new hire to make them on day one. If not, they are not documented well enough for a digital workforce either. Start there.
Action for boards: Add “organizational knowledge capture” to your technology governance framework. Treat it like you treat data governance. It is that important.
3. The skill floor rises and the skill ceiling disappears.
Here is the part most people get wrong. They assume AI makes work easier. It does not. It makes simple work disappear and makes the remaining work harder.
That developer’s exhaustion is not an anomaly. It is the new normal. When AI handles every routine task, every task left on your desk is a hard one. There is no easy win to warm up with. No quick ticket to build momentum. You go straight to the complex problems all day, every day.
This has massive implications for talent strategy. Junior roles that were designed as training grounds (data entry, basic analysis, first-pass reviews) are the first to be automated. But those roles existed for a reason. They were how people learned the business. They were the on-ramp.
If you automate the on-ramp without building a new one, you end up with a bimodal workforce: senior people who are exhausted and junior people who never develop. Neither outcome is sustainable.
Action for CEOs: Redesign your entry-level roles now. Not after digital workforces are fully deployed. The new junior role is not “do the simple version of what seniors do.” It is “learn to direct and evaluate AI output.” This is a fundamentally different skill set and it requires a fundamentally different training program.
Action for boards: Ask management for their AI-era talent development plan. If the answer is “we’ll figure it out as we go,” push back. The companies that solve the junior talent pipeline first will have a structural advantage for the next decade.
The Values Shift
Skills can be trained. Values are harder. And the values that served enterprises well in the execution era will not serve them in the judgment era.
From “move fast” to “think clearly.” Speed of execution was a virtue when execution was the bottleneck. When execution is instant, speed of thought without clarity of thought is just fast failure. The new virtue is precision of intent.
From “do more” to “direct better.” Volume is no longer a signal of contribution. The person who runs 50 AI agents poorly creates more mess than the person who runs 5 well. Quality of direction matters more than quantity of output.
From “know how” to “know why.” Procedural knowledge (how to do X) is exactly what AI agents excel at. Contextual knowledge (why we do X this way, what happens if we don’t, what changed last quarter that matters) is what they lack. The most valuable employees will be the ones who understand the why deeply enough to encode it for machines.
The Punchline
The enterprise stack is expanding. Software is not dying. Digital workforces are not replacing your people. But they are changing which people matter and why.
The bottleneck has moved from execution to judgment. The companies that recognize this and restructure their skills, metrics, and values accordingly will compound the advantage. The ones that keep measuring keystrokes will wonder why their AI investments are not paying off.
If you are a CEO: start with your performance metrics, your knowledge systems, and your junior talent pipeline. Those three things will determine whether digital workforces multiply your capacity or multiply your problems.
If you are a board member: ask the hard questions now. Not “are we using AI?” but “are we building the organizational muscle to direct AI well?” The answer will tell you more about the company’s future than any revenue forecast.
The future of enterprise is not fewer humans. It is humans doing harder, more valuable work. The question is whether your organization is ready for that.
This is Part 3 of the Expanding Enterprise Stack series. Part 1 covers why the stack is growing. Part 2 covers how software and digital workforces coexist. Part 4 will cover the investment implications.