AI-Assisted Engineering: Leading the Shift with Curiosity and Clarity

Navigating the AI-Assisted Engineering Journey Together

Lately, I’ve been thinking a lot about where AI fits in our engineering world. Maybe you’ve heard something similar yourself—“Are we really using AI enough in our coding?” Maybe from your manager, a CEO, or just the buzz in tech circles. I recently came across a conversation where a leader was told by a startup friend that “50% of their code is AI-generated now.” That kind of statement hits hard, right? It’s easy to feel caught between hype and skepticism.

What really stood out to me is how this situation isn’t unique—many of us are wondering the same thing. But what I find encouraging is the idea of approaching AI not as a magic bullet or a scary unknown, but as a journey with clear steps. Instead of rushing headlong into mandates about “using AI this much,” what if we started with a phase of experimentation—where we all get to try, test, and learn without pressure?

I want us to think of this as a shared adventure, where we explore the possibilities AI offers, while being clear-eyed about its limitations. Because this shift is huge—a tectonic change in how software might get built. But it’s also one we can lead, with thoughtful planning and empathy.

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Why Experimentation is the Right Starting Point

You might ask, “Why not just jump straight to adoption? Set targets and make AI use mandatory?” The honest answer is: AI coding is evolving incredibly fast, and its impact is complex. Some engineers are all-in, trusting AI almost blindly, while others are wary, guarding quality fiercely. Both reactions are valid.

Our job as leaders isn’t to push one extreme or the other but to guide the team toward a middle path—where AI is a tool that augments human creativity and judgment, not a crutch that leads to sloppy code or unchecked pull requests.

So, the first goal isn’t “use AI for X% of your coding.” It’s about getting comfortable with the tech, understanding its quirks, and learning when it helps or hinders. By fostering a culture of experimentation, we give ourselves space to understand this new tool—and prepare for the future.

If there’s a takeaway here, it’s this: Experiment first, measure later. We can’t expect perfect results from day one, but we can expect genuine curiosity and shared learning.

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How We Can Measure Progress—Without Stress

As we experiment, how do we know if we’re moving forward? That’s where metrics come in—but not to micromanage or judge. Think of metrics as a flashlight in a dark room, helping us see where we are and where we want to go.

Early metrics might include:

• How many AI tools does each team try out?

• Whether teams hold retrospectives to discuss what’s working or not.

• Activity in channels or communities where folks share AI insights.

Later, as we adopt tools more routinely, we’ll want to track usage, how productive engineers feel with AI assistance, and the real impact on code quality and delivery timelines.

One important piece: we have to be careful not to weaponize these numbers. They’re here to support us, not to turn into pressure points or competition. Our culture should encourage honest sharing, not fear of judgment.

If you’re thinking about practical steps, even a simple biweekly survey or informal check-in can be a great start. No need for complex systems right away.

Supporting Each Other Through Change

Changing how we work isn’t just about tools and targets—it’s about people. Some engineers might be worried AI will replace them, or make their favorite parts of the job disappear. These fears are real and deserve our respect and open conversations.

We can’t ignore the emotional side of this shift. Leaders should listen, empathize, and provide space for these conversations. Regular check-ins, all-hands discussions, or even one-on-one chats can make a big difference.

Beyond emotional support, we need to remove practical barriers too. That means:

• Ensuring budgets for AI tools are approved without endless red tape.

• Addressing compliance or legal concerns swiftly so engineers don’t waste days waiting for approvals.

• Making sure teams actually have time to experiment—because without it, innovation won’t happen.

I’ve also been thinking about building a Community of Practice around AI-assisted engineering—an informal network where folks can share tips, challenges, and successes. This could be Slack channels, weekly coffee chats, lunch-and-learns, or even a newsletter like this one focused on AI coding insights.

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Moving Forward With Confidence and Curiosity

So, what’s the takeaway here? AI-assisted coding is here, and it’s changing our world. But we don’t have to fear it or blindly chase it. Instead, we can embrace it thoughtfully—leading with experimentation, clear metrics, and empathy.

Our goal? To find that sweet spot where AI handles the tedious stuff, freeing us up for creativity and craftsmanship, without sacrificing code quality.

If I had to offer some practical tips for leaders and teams embarking on this journey:

1. Start small, start now—pilot a few tools, talk about your experiences openly.

2. Measure progress, but keep it kind—metrics are guides, not grades.

3. Provide time and budget support—make it clear it’s OK to invest effort in learning AI.

4. Build communities—learning together is more powerful than going it alone.

5. Lead with empathy—listen to fears and address them honestly.

We’re at the beginning of a fascinating chapter in engineering. I’m excited to see where this journey takes us, and I hope you are too.

Here’s to exploring AI-assisted engineering together.

What’s your next spark? A new platform engineering skill? A bold pitch? A team ready to rise? Share your ideas or challenges at Tiny Big Spark. Let’s build your pyramid—together.

That’s it!

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