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2025-05-15 · 5 min read

# AI Killed Servant Leadership

From zero‑interest "peacetime" growth to AI‑accelerated "wartime" precision: how Active Leadership and AI‑first operating systems unlock profitable growth.

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Gemini and Your Claw use copy and paste.

Back in **2013**, tech budgets felt limitless. VC cash flowed, middle‑manager ladders stretched ever upward, and “hire smart people, get out of their way” looked like genius. Fast‑forward to **2025**: cheap money disappears, every headcount request is interrogated, and you (yes, you) now rely on an AI tool that costs pennies an hour but ships code, copy, and forecasts quickly.

## [AI isn’t just a tool; it’s a new social contract between leaders and teams](#ai-isnt-just-a-tool-its-a-new-social-contract-between-leaders-and-teams)

The deal: humans bring judgment, context, and purpose. AI brings fast throughput at low cost. The old servant‑leadership pact doesn’t fit this contract, and clinging to it quietly starves profitability.

## [The leadership model AI demands](#the-leadership-model-ai-demands)

I joined [Anthony Enrico](https://www.linkedin.com/in/anthonyenrico/) on the [Leanscale](https://leanscale.team) podcast to unpack why AI didn’t just introduce new tools. It rewrote the leadership contract.

[Watch the full conversation on YouTube](https://www.youtube.com/watch?v=gpojpKvMD54).

## [The servant era: why it worked, until it didn’t](#the-servant-era-why-it-worked-until-it-didnt)

![](/images/writing/ai-killed-servant-leadership/01-ede52ba4.jpg)

For a 12‑year bull run, capital was cheap and patience infinite. Servant leadership thrived:

1.  **Distance from the work** was fine, your talented hires would sort it out.
2.  **Career-path politics** gave ambitious managers something to climb.
3.  **Delayed information loops** mattered less when quarters closed red but funding rounds opened green.

That model began to crack when results needed to materialize _this_ quarter, not in the next round deck. Building growth teams at [GitLab](http://gitlab.com) gave me a front-row seat to a subtle leadership drift: the further leaders float from the frontline, the fuzzier their decision-making becomes, especially when precision matters most.

## [The catalyst: AI and capital discipline](#the-catalyst-ai-and-capital-discipline)

Today, the **North Star is profitability (EBITDA or Free Cashflow)**. **Revenue‑per‑FTE** remains a warning gauge, but only cash efficiency keeps companies breathing. At the same time, we’ve unleashed AI agents with zero career aspirations. You can’t mentor an LLM the way you mentor a person. You can only _direct_ it, then prove its output ladders up to margin.

The result is a **wartime tempo**:

1.  **Speed**: ship value weekly, not quarterly.
2.  **Precision**: clarity drives profitable execution, vagueness drives inefficiency.
3.  **Accountability**: outcomes outweigh effort.

## [Active leadership defined](#active-leadership-defined)

> _Lead from the front; no job too small._

Active Leadership rejects the serve‑and‑step‑back model in favour of **earned credibility**. Inspired by [Matt McKinnis’](https://www.linkedin.com/in/macinnis/) idea of being _“micro‑interested,”_ the approach looks like this:

1.  **Micro-interest, not micromanage.** Dive deep where risk is high, then surface fast. Think of it as a trust but verify evolution, rooted in curiosity and precision rather than suspicion.
2.  **Trust = consistency / time.** Show up predictably and teams reciprocate. Deliver meaningful results sprint after sprint, and you'll earn the organization's confidence, and with it, bigger opportunities and increasingly critical initiatives.
3.  **Context by doing.** I personally trained our customGPT at [VRIFY](http://vrify.com), the nuance I gained now accelerates every go-to-market decision.

Active leaders transform “distance from the work” into “velocity at the edge.” Rita McGrath captured it best: _snow melts first at the edges_. Those closest to the work hold the freshest ground truth. When paired with clear business context, their insights enable faster, more accurate decisions, giving organizations a decisive speed advantage.

> Speed kills your competition. Lack of speed kills your business.

## [AI-first teams and the high-velocity OS](#ai-first-teams-and-the-high-velocity-os)

### [Behavioural foundation: rewriting reflexes](#behavioural-foundation-rewriting-reflexes)

Before any system change sticks, individuals must rewire their default question set. So I ask every new team member to place a sticky note, square in their line of sight, that reads:

> _How can AI help me with what I'm about to do?_

Seeing that prompt hundreds of times per week breaks legacy habits of "do‑it‑myself" execution and forms a new reflex: partner with AI first, then apply human judgment.

### [System foundation: the high-velocity OS](#system-foundation-the-high-velocity-os)

![](/images/writing/ai-killed-servant-leadership/02-bcc0b94f.png)

1.  **Quarterly OKRs (3×3 Rule)**

-   Draft cross-functionally, share, then lock.
-   Limit to **3 Objectives**, each with up to **3 quantifiable Key Results**.
-   Reserve **20% slack** for mission-critical interrupts.

2.  **Two‑Week Sprint Cadence**

-   Monday plan, mid-sprint check, Thursday review and retro.
-   Friday **20% Innovation Time**, self-directed experiments become next quarter’s growth levers.

3.  **Monthly Showcase**

-   Teams demo what shipped. Visibility breeds ownership and cross-pollinates ideas.

This operating system introduced predictable output and reduced cycle time, strengthening our ability to operate at high velocity, even while expanding headcount during a growth phase.

## [Operational precision toolkit](#operational-precision-toolkit)

![](/images/writing/ai-killed-servant-leadership/03-2cbbbbdc.jpg)

Words are force multipliers; **imprecision is waste**. Three tools keep language operational:

1.  **Goal Statement**

-   _By doing X, we will Y, as measured by Z._

2.  **Jobs To Be Done Statement**

-   _When a prospect/customer \[trigger\] ..._
-   _A prospect/customer wants to \[action\] ..._
-   _So they can \[outcome\] ..._

3.  **RAG Loop**

-   _Retrieve critical context._
-   _Augment with what’s missing._
-   _Generate a crisp action plan._
-   _Publish a sub-1-page decision brief._

Together, they push the team to think scoreboard, not storyboard.

## [Putting it all together](#putting-it-all-together)

![](/images/writing/ai-killed-servant-leadership/04-45fb99c1.jpg)

Adopt Active Leadership in **30‑60‑90 days**:

1.  **Days 0-30**

-   Shadow as many prospect/client-facing interactions as possible.
-   Launch the first formal workflow for integrating AI into daily work.
-   Circulate an OKR draft and gather feedback.

2.  **Days 31-60**

-   Launch two-week sprints.
-   Pilot Innovation Fridays.
-   Issue your first RAG brief.

3.  **Days 61-90**

-   Host the inaugural Monthly Showcase.
-   Monitor and share KPIs.
-   Refine team roles for AI leverage.

The reward: profitability climbs while morale follows, because clarity is the ultimate perk.

* * *

If you’re running a leadership off‑site, use this as the agenda: decide your 3×3 OKRs, lock the sprint cadence, and pick one RAG brief you’ll ship next week. Measure cycle time, revenue‑per‑FTE, and forecast accuracy, then iterate.

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Decision support

## Fast answers, zero fluff

The core framing, audience fit, and time commitment in under a minute.

01Why is classic servant leadership insufficient now?

I no longer think leader distance is enough. With AI making execution abundant, the constraint is directional clarity and judgment quality.

02What is Active Leadership in practice?

In practice, I run Active Leadership by setting priorities, decision rights, and escalation triggers up front, then reviewing outcomes on a tight cadence.

03Where should a team start on Monday?

On Monday, I start with a decision-rights audit on one critical workflow, set non-negotiable quality bars, and automate only repeatable steps.

04How do we use AI without lowering standards?

I use AI for draft velocity and option generation, but I keep human sign-off for customer commitments, risk calls, and irreversible decisions.

05How do we know the shift is working?

I know the shift is working when time-to-decision drops, rework declines, escalations decrease, and forecast confidence improves under pressure.
