Designing While Flying: Building Feedback Loops into AI-Powered Products

AI-powered products change the relationship between launch and learning.

With a traditional deterministic experience, teams can usually predict what the product will do when a customer takes a particular action. AI introduces more variability. Outputs can depend on context, data, prompts, model behavior, and situations the team didn't anticipate during design.

That means launch can't be the point where validation stops. Teams need feedback loops that help them understand what the AI is doing in the real world, where customers trust it, where they correct it, and whether it is actually helping them accomplish the job.

The design question isn't simply whether to add a thumbs-up or thumbs-down control. It's how to create a system that continuously turns customer behavior and feedback into useful evidence.

Feedback Is Part of the Product, Not an Add-On

One of the questions I originally had about AI products was whether feedback should be treated as a standalone capability or embedded directly into each workflow. I now think the stronger answer is both—but for different purposes.

Contextual feedback belongs close to the experience. If an AI recommendation is wrong, incomplete, confusing, or surprisingly useful, the customer's reaction is most valuable when we understand what they were trying to do and what the system produced.

Centralized feedback and analytics serve another purpose. They help teams see patterns across customers, workflows, and AI capabilities that may not be visible from an individual interaction.

The goal isn't choosing one approach. It's connecting them.

Don't Ask for Feedback Everywhere

More feedback prompts don't necessarily create better evidence. They can create fatigue and noise.

Instead, identify moments where feedback can answer a meaningful product question. That might be after an AI-generated recommendation, when a customer rejects or edits an output, when the system expresses uncertainty, when a task fails, or after a high-value workflow is completed.

The prompt should also match the decision the team needs to make. A generic satisfaction question may be less useful than understanding whether an answer was accurate, whether a recommendation was actionable, or whether the AI reduced the effort required to complete the job.

Behavior Is Feedback Too

Some of the strongest feedback signals aren't things customers explicitly tell us.

If customers repeatedly rewrite an AI-generated response, ignore a recommendation, undo an automated action, regenerate the same output several times, abandon a workflow, or consistently choose a manual path instead, those behaviors are telling us something.

Behavioral signals can reveal friction customers may never report. Explicit feedback can then help us understand why the behavior occurred.

This is where quantitative and qualitative evidence work together: analytics can surface the pattern, while research and contextual feedback help explain it.

Capture Enough Context to Make Feedback Useful

A thumbs-down without context is difficult to act on.

For AI feedback to be useful, teams often need to understand the task, relevant inputs, system output, customer action, and outcome surrounding the feedback. Depending on the product, they may also need model or version information so they can determine whether behavior changed over time.

That doesn't mean collecting everything. Privacy, security, and customer expectations should shape what is captured. The principle is simpler: collect enough appropriate context to turn a reaction into evidence the team can learn from.

Human Review Still Matters

Automating the collection and analysis of feedback can help teams operate at scale, but not every signal should be interpreted automatically.

Patterns involving high-risk workflows, unexpected model behavior, repeated customer corrections, or potentially harmful outputs may need human review. Product, Design, Research, Engineering, Data, and domain experts may each see something different in the same evidence.

The point of a feedback loop isn't simply to feed more data back into a model. It's to help the organization make a better decision about the product.

Measure the Job, Not Just the Model

AI teams can easily become focused on model-level measures. Those can be important, but customers aren't using a benchmark. They're trying to accomplish something.

A useful measurement strategy should connect AI performance to the customer outcome. Did the customer complete the task? Did it take less effort? Did they accept, edit, or reject the output? Did the experience reduce errors or create new ones? Did trust increase with repeated use, or did customers learn to avoid the AI?

This connects feedback directly to the Job to Be Done and prevents teams from declaring success because the model improved while the customer experience did not.

Close the Loop With Customers

Customers are more likely to provide meaningful feedback when they believe it goes somewhere.

Closing the loop doesn't require responding individually to every signal. Teams can communicate improvements through release notes, in-product messaging, customer conversations, or visible changes in the experience.

More importantly, the product itself should demonstrate learning. If customers repeatedly correct the same problem and nothing changes, the feedback mechanism can eventually undermine trust instead of building it.

Create a Continuous AI Learning Loop

For an AI-powered experience, I would design the learning system alongside the customer workflow:

• Define the customer Job to Be Done and intended outcome.

• Identify where AI introduces meaningful uncertainty or variability.

• Decide which explicit and behavioral signals will help evaluate the experience.

• Capture appropriate context around those signals.

• Look for patterns across customers and workflows.

• Use qualitative research or human review to understand why those patterns exist.

• Prioritize changes based on customer impact, risk, frequency, and strategic value.

• Measure whether the change actually improved the customer outcome.

• Repeat.

Designing AI Means Designing for Learning

The metaphor of building the plane while flying it still resonates with me, but I think the important part isn't simply that AI changes quickly.

It's that we can't anticipate every real-world interaction before launch.

Responsible AI product design requires teams to create the mechanisms that help them notice what they didn't predict, understand what customers are experiencing, and respond intentionally.

Feedback isn't a button we add at the end of the design. It's part of the product's learning system.

The better we design that system, the better chance we have of creating AI experiences that become more useful, trustworthy, and aligned with real human needs over time.