How Do We Know AI Is Actually Helping?

After more than twelve years in product and UX design, I've learned to be cautious with new tools—especially ones that promise speed. I've seen enough design systems, frameworks, and workflows come and go to know that efficiency alone is rarely the real unlock.

Working with Kiro as an AI agent pushed me to rethink how I measure success. Not in terms of novelty or output volume, but in terms of whether the collaboration genuinely improves how I think, decide, and execute as a designer.

Moving Beyond Activity Metrics

At a basic level, AI usage can be quantified: session time, number of prompts, number of responses. I track these, but I treat them as diagnostic signals rather than indicators of success. High engagement doesn't necessarily mean high impact—it can just as easily point to friction, ambiguity, or unnecessary back-and-forth.

What matters more is where the time goes.

I break my working sessions into phases: thinking and ideation, execution, and rework. When AI is effective, I see a shift away from prolonged ideation loops and late-stage corrections, and toward more confident execution earlier in the process. The total time may decrease, but more importantly, the quality of that time improves.

Iteration Quality Over Iteration Count

Iteration is foundational to design, but it shouldn't be mistaken for progress by default. One of the most reliable indicators of a healthy human–AI collaboration is how quickly I reach an "acceptable final state"—a point where the solution is directionally correct and ready to be refined, not rethought.

With Kiro, I pay close attention to how many revisions are needed, how often I restart a thread, and how quickly ideas converge. Fewer iterations don't mean less exploration; they signal that the exploration is better structured and more intentional.

Cognitive Load Is the Hidden Cost

Over time, I've come to see cognitive load as one of the most under-measured aspects of design work. When AI is supporting me well, I ask fewer clarification questions, spend less time circling the same ideas, and reach decisions with less mental friction.

I look at signals like how long exploratory conversations last before a decision, how many prompts it takes to reach clarity, and how often my initial intent is accepted without major revision. These aren't traditional metrics, but they reflect something critical: whether the tool is helping me think more clearly, not just faster.

Designing the Collaboration, Not Just the Output

Finally, I evaluate how the collaboration itself behaves. How often do I override Kiro's suggestions? How frequently do I accept them as-is or with minor adjustments?

A high override rate isn't a failure—it's a design insight. It highlights where human judgment, context, or taste still needs to lead, and where the system needs better constraints or framing. The goal isn't to defer thinking to AI, but to create a working relationship where each side plays to its strengths.

My Definition of Success

At this stage in my career, success with AI isn't about automation or acceleration alone. It's about reaching clarity sooner, reducing unnecessary cognitive load, and maintaining a high bar for quality and intent.

If an AI agent helps me do that—quietly, consistently, and without getting in the way—then it's doing its job.