When Automation Makes Management Disappear

A few months back, I was facing a delivery squeeze with a side project—simple on the surface, but loaded with fiddly, recurring tasks. My first reflex was familiar. Just bring on a junior developer, delegate the grunt work, and keep things moving. I hesitated though. Maybe it was the logistics, maybe a creeping sense that I was repeating old patterns instead of solving the real bottleneck. I kept asking myself, what’s actually getting in my way here?

Instead, I took a different swing—leading through AI ambiguity by wiring up a chain of autonomous agents. No onboarding. No status updates. No project syncs or constant supervision. The agents just ran, chewing through the checklist and producing outputs while I watched, for the first time, what it felt like when the management layer began to evaporate.

Here’s what automation couldn’t touch. The work that remained wasn’t code or communication. It was deciding exactly what to build, what “done” should feel like, and how to steer the project when things veered off-plan. It was like the support rails were gone, but I still had to draw the map.

This is where AI era leadership shifts the ground for engineers and builders. The management layer—tracking tasks, updating status, herding routines—is rapidly becoming automatable. But leadership, the kind that sets direction, defines outcomes, and adapts when certainty dries up, still sticks around. For anyone building in the AI era, that’s the new fault line. Automation handles the explicit, but the hard human work calls for clarity in goals and judgment under pressure.

That’s the punch I took from a Seth Godin riff on Tim Ferriss’ podcast, one that has echoed through my workflow ever since. As machine learning drops the cost for prediction, the upside shifts to those who control data and make judgment calls about outcomes. Which means explicit work gets automated while human judgment becomes more valuable. Hearing it said out loud changed what I optimize for. Anything you can write down, the system will chew through. Leadership is what shows up when the map runs out, and you’re the one left to chart the next move. That’s the piece worth protecting as automation closes in everywhere else.

What Automation Leaves Behind

Right now, it’s tough not to notice: the more you automate, the faster the rote work vanishes, but the actual direction of a project gets blurrier. I’ve watched my calendar empty out. No more endless syncs or follow-ups, while somehow the weight of what’s left feels a lot heavier.

Here’s the shift you might be feeling. If you’re used to recognition and value coming from managing the flow, AI exposes which parts of that truly matter. If automation can handle your oversight, it’s not a threat. It’s a mirror showing what’s left when the visible busyness is gone.

Try stripping it all out—no dashboards, no check-ins, no reminders. See what remains. I did this with my own team for a sprint and the quiet was unsettling. Without dashboards to point to, my real value wasn’t tracking. It was handling the messy bits nobody could automate: clarifying priorities, fighting ambiguity, and shaping what “success” actually meant.

Even now, every week, no matter how tightly we’ve automated tracking, something ambiguous crawls in. The reports always look good. The decisions? Never automatic.

Automation wins at anything you can explain in detail, step by step, but leading through AI ambiguity is how humans set outcomes and guardrails. That means it crushes routine, explicit work. Updating statuses, triggering alerts, nudging a task along. But leadership? That’s about building outcomes and setting guardrails when things aren’t clear. My job isn’t writing tasks down anymore—it’s defining what good looks like before we automate a single step. The hard part is always the part that needs a human.

Funny how that earlier feeling—watching my screen go quiet after agents took over—still shows up every week. You get used to the silence, but the ambiguity always finds its way back.

Leading Through AI Ambiguity When Execution Is Automated

At some point, engineering leadership with AI meant I stopped drafting checklists and started writing outcome briefs. It’s a simple document, but it pulls all intent out from the noise. Objectives, a high-level view of what “good” looks like, what to avoid, and where to clarify later. Instead of endless meetings or clarifying memos, it’s just there for the agents. I write the brief before I even think about breaking things into tasks now, and it’s changed how reliably the right work gets done.

To make agents effective, define outcomes and guardrails—they don’t thrive on vibes. They need crisp inputs, clear outputs, and explicit rules on what’s allowed and what isn’t. Building effective guardrails for LLMs means leaders systematically consider context and define boundaries grounded in evidence across use cases (Guardrails for LLMs). That’s the actual work. You draw the limits, not the machine.

Designing outcomes this way is more like writing a recipe than managing a kitchen. You measure, taste, and adjust. Then do it again. I over-seasoned my first few briefs. They tasted like policy instead of guidance you actually want to use.

Here’s an example that finally clicked for me. Our AI content system. Instead of chasing after outputs and debugging endless edge cases, I started with an outcome brief—objective (“produce weekly blog drafts on new features”), acceptance criteria (“clear intro, user perspective, links to docs”), edge cases (“if a feature is experimental, flag it prominently”), and notify for manual review (“if more than 1 draft is rejected in a cycle, notify for manual review”).

The agents digested this, churned away, and handed back exactly what we needed—or flagged when we had to step in. I stopped debugging content and started refining outcomes. Each cycle, the feedback loop got tighter. Not on the tasks, but on the brief itself. That’s the playbook. Automate the routine, obsess over clarity, and put your leadership energy where the ambiguity—always—creeps in.

Still, I don’t have a formula for every situation. Sometimes the edge cases are weird, and no matter how the brief is framed, some ambiguity slips through. Maybe that’s just the forever tension when leadership gets measured against systems that seem so neat.

Turning Hesitation Into Leverage

If you’re worried about losing control, you’re not alone—so was I. But building strong guardrails, then letting agents run inside that sandbox, actually tightens your grip on outcomes. A tailored AI risk profile helps organizations spot unique generative AI risks and put real-world controls in place. I’ve found I worry less, not more, once I’ve made the call on what “good” is and set up boundaries that really stick.

There’s the visibility thing too. When the management layer falls away, it’s natural to worry about how your work gets seen. But every time this happens, the result is the same—outcomes, not oversight, are what enable you to trust agents and teams. The flavor of my updates shifted from “here’s the progress” to “here’s the impact.” That’s what sticks.

If you’re skeptical, run a tiny experiment. For one initiative, turn off the status mechanics and just measure what changes. See if the clarity improves, if trust holds. Then decide what’s worth bringing back. Sometimes, management disappeared—and nothing broke.

A Practical Shift: Automate the Explicit, Lead the Ambiguous

Here’s my playbook, straight up. First, push agents to handle all the explicit tracking. Anything routine, repetitive, or checklist-like. If you can write it out as input and output, automate it.

Second, get in the habit of writing outcome briefs: a one-pager for every new initiative that describes not just what needs to be done, but why it matters, how “done” should look (acceptance criteria), and where things could go off the rails. Framing cuts down the back-and-forth (link), which stabilizes outputs and pulls everyone toward actual results. Third, block off one hour weekly. That hour is for tackling the messy stuff: ambiguity, shifting priorities, or tradeoffs. My system is plain because complexity only ever hides in outcomes, not the status logs.

Let me ground this with my own team’s shift. Instead of assigning tasks and tracking progress myself, I started allocating real brainpower to designing decisions, setting up guardrails, and architecting what good looks like. I let agents do the status chasing and checklist updates. Everyone moved from tracking activity to steering direction—an outcome driven leadership shift that made the work feel lighter and somehow more impactful.

This is the headline. If you want to preserve and grow your value, commit. Automate anything explicit, own the artifacts and cadence that resolve the ambiguous, and don’t hesitate to lead through agent-driven uncertainty. Instead of putting a junior dev on the grind, I built an agent chain. You know what to do next.