Today is July 7, 2024. I’ve just made a decision I’ve been circling for a while: I’m no longer watching GenAI from the sidelines. I’m stepping forward.
One of the first books I read when I moved into management was The Making of a Manager by Julie Zhuo. It stuck with me—not just for its advice, but for a single line that became a kind of motto: “Your job, as a manager, is to get better outcomes from a group of people working together.” Every time things feel too smooth, too comfortable, I return to that sentence. It reminds me that improvement doesn’t come from standing still. It comes from movement, from curiosity, and from leading the next step forward. Zhuo also writes, “The best outcomes come from inspiring people to action, not telling them what to do”—and that’s exactly what I’m trying to do here.
As engineering managers, we’re not just responsible for delivery—we’re also responsible for keeping our teams relevant, efficient, and prepared. When a new technology like Large Language Models (LLMs) shows signs of becoming foundational, we can’t wait for top-down direction. We act.
Everything I’m doing right now is still theoretical. I don’t yet know what we’ll learn, or whether it will work. But I have hope—and a plan. I want to explore how LLMs can make my day and my team’s day more efficient. And if any of it works, I want to share those learnings with others in my chapter, my tribe, and across teams.
Why AI & LLMs Matter for Engineering Managers
The potential of GenAI in engineering is everywhere—boilerplate generation, documentation support, smarter incident triage, onboarding guides. But so far, most of it feels abstract. We hear success stories, but we don’t yet live them day to day.
Still, I believe it’s on us to start trying. These models are not just novelties. They may become essential tools. As managers, it’s our role to experiment, to understand what’s possible, and to support our teams in doing the same.
This isn’t a one-off exploration. This is a learning goal—one that I’m actively shaping now.
Defining My SMART Performance Development Goal
Here’s the goal I just wrote and submitted as part of my performance development plan, using the SMART framework:
Specific: I will gain practical knowledge of AI and LLM applications relevant to engineering management, focusing on identifying techniques that could realistically enhance our workflow.
Measurable: I’ve outlined what I will do—seek CTO sponsorship to explore the space, complete two certifications, summarize five pieces of content, run three knowledge-sharing sessions, and pilot at least two AI-driven improvements.
Achievable: I’m dedicating weekly time to this, using available online resources, and trying to solve real problems as experiments. No need to pause my responsibilities—just redirect part of my routine.
Relevant: This is directly tied to efficiency, learning culture, and our long-term ability to adapt.
Time-Bound: I aim to complete all of this by the end of Q4 2024.
I’m not doing this to become an AI expert. I’m doing it to become a better manager for this era.
Inviting My Team to Join the Journey
Alongside this, I’ve started encouraging my team to set similar learning goals. Nothing formal. Just an invitation to explore.
Some are looking at prompting. Others are testing how AI helps in debugging or writing documentation. I don’t have examples to point to yet—just a hunch that we’ll get better at this if we try together.
We’re building space for experimentation into our cadence. Not KPIs. Not OKRs. Just learning.
Starting the Conversation with Leadership
To get traction, I reached out to leadership. Not with a huge plan—just with a simple request that could open the door.
The Email I Sent
Subject: Budget Approval Request for ChatGPT Monthly Subscription
Hi Tomas,
Following up on our discussion during the last Tech Leaders Monthly Sync, where you challenged us to enhance our efficiency by incorporating and familiarizing ourselves with LLMs as productivity tools, I’m asking for budget approval for the ChatGPT monthly plan.
Cost details:
- Individual: $20/month
- Team: $25/month per person
Please let me know if you need further info or would like me to connect with other teams already using it.
This email helped frame the initiative in a practical, low-risk way. Now, we wait to see if others step in too.
From a Decision to a Capability
Right now, I don’t have results. I don’t have data. I have a decision, a plan, and the first steps in motion.
And that’s enough.
I believe the way we integrate AI into engineering teams won’t come from a single directive. It will come from dozens of small, intentional experiments like this—run by managers who decide to go first.
That’s where I am today. Not with answers. But with a clear intention to find them—and bring others along.