Speed vs. Velocity in Software Development: A Lesson from Agile (and AI)

Hey there, it’s Jake, and I want to share something I’ve noticed over years of working in software development and Agile teams: there’s a big difference between speed and velocity. At first glance, they might seem like the same thing, but understanding how they’re different can make or break your projects.

Lately, this distinction has felt more relevant than ever, especially with the rise of AI in software development. Just like in Agile, when using AI tools, it’s easy to get caught up in how fast things are moving without asking whether they’re moving in the right direction.

So let’s break it down.

What Is Speed in Software Development and AI?

Speed is all about how fast you’re moving—how many tasks or features you can crank out in a sprint, or how quickly an AI tool can generate a result. It’s that feeling of looking at a pile of closed tickets or AI-generated drafts and thinking, “Wow, we’re on fire!” But here’s the thing: speed doesn’t always mean progress.

I’ve seen teams—and even individuals using AI—churn out content, code, or features so fast they leave a trail of rework and misaligned priorities. With AI, the temptation is even greater because it can produce results at lightning speed. But are those results aligned with your goals? Do they actually solve the problem?

The downside of focusing on speed:

• Shipping features or content no one asked for.
• Accumulating technical debt (or messy AI outputs) that drags you down later.
• Burning out your team—or overwhelming yourself—by focusing on quantity over quality.

What About Velocity in Agile and AI?

Velocity, on the other hand, is a completely different mindset. It’s not just about moving fast—it’s about moving fast in the right direction. In Agile, velocity is a measure of how much value a team delivers during a sprint. With AI, velocity is about using the tool to deliver meaningful results aligned with your strategy.

Instead of asking, “How much can we get done?”, velocity asks, “How much of what we’re doing makes a difference?”

Why velocity is better:

• It keeps you focused on delivering value.
• It encourages a sustainable pace so your team (or you) doesn’t collapse under pressure.
• It helps you improve over time by looking at what’s working (and what’s not).

This mindset is especially important when incorporating AI into workflows. Just because an AI tool can generate 10 solutions in 10 seconds doesn’t mean all 10 are useful. The key is evaluating, refining, and integrating the best ones into your process.

Speed vs. Velocity: Why It Matters in Agile and AI

Here’s a quick way to think about it: Speed is how fast you’re driving. Velocity is knowing where you’re going. You can go 100 mph, but if you’re heading toward a cliff, all you’re doing is getting to disaster faster.

In software development, focusing only on speed can lead to wasted effort. The same is true with AI. You might create a ton of AI-generated drafts or prototypes, but if they don’t align with your goals or add value, you’re just spinning your wheels.

Agile Methods, AI, and Velocity

One of the things I love about Agile is how it emphasizes velocity over speed. This approach applies beautifully to using AI:

1. Prioritization: The backlog (or your AI prompts) should focus on solving the right problems first, not just generating a lot of outputs.
2. Sprints: Time-boxing helps you scope work realistically and ensures AI is used for valuable, time-sensitive tasks.
3. Feedback Loops: Agile thrives on checking in with the customer (or user) early and often to make sure you’re building or creating the right thing.
4. Retrospectives: Regularly look back at how AI and Agile are working together to refine your approach.

How to Focus on Velocity (Without Ignoring Speed)

Look, speed isn’t the enemy. There are times when you do need to move fast—fixing a critical bug, getting an MVP out the door, or responding to market changes. With AI, speed can be a lifesaver for tasks like rapid prototyping or content creation. But the key is to balance speed with velocity. Here’s how you can do it:

1. Set Clear Goals: Know what success looks like before you start moving—whether in Agile or with AI.
2. Prioritize Value: Focus on the tasks that have the biggest impact, even if they take longer to refine with AI.
3. Measure What Matters: Use metrics that track outcomes, like user satisfaction or adoption, not just output.
4. Take Care of Your Team: Burnout doesn’t lead to good work. Build a sustainable pace—whether for people or AI-enhanced workflows.
5. Celebrate Wins: Not just for going fast, but for delivering something meaningful.

A Personal Takeaway

I’ve been guilty of chasing speed before—who hasn’t? It’s tempting to show progress with a lot of “done” tasks or AI-generated drafts. But I’ve learned that speed without direction is like running on a treadmill. You’re sweating, you’re moving, but you’re not actually getting anywhere.

Velocity, on the other hand, is where the magic happens. Whether it’s Agile or AI, it’s about delivering something that matters—something your users love, your team feels proud of, and your business actually benefits from. That’s the kind of progress that makes all the effort worth it.

So next time you’re in the trenches of a sprint or exploring AI tools, ask yourself: Are we going fast, or are we going somewhere?