Leading Feedback in the Age of AI: Why Your AI Agents Need the RA-RA Model Too

We tend to think of feedback as a human-centered exchange with emotion, trust, and conversation. But what happens when your team includes more than just people?

Artificial intelligence is no longer an abstract tool. It’s a team member. Whether you're deploying agentic AI assistants, custom GPTs, or intelligent automations, these technologies are now shaping decisions, driving outputs, and influencing how work gets done. And like any member of your team, AI needs feedback too.

Not emotional validation. Not praise or correction. But structured, intentional recalibration.

That’s where the RA-RA Feedback Model still applies.

AI Isn’t Magic. It’s Momentum.

AI only performs as well as it’s guided. And guidance, like leadership, isn’t a one-time upload. It’s ongoing.

Leaders must recognize that intelligent systems still rely on human direction. The moment you stop aligning your AI agents with your evolving goals, your outcomes start to drift. AI doesn’t fail with bad intentions; it simply reflects what it's been given, or worse, what it hasn’t been told.

So, how do you lead it well?

You lead it with feedback, just like you would any high-performing team.

RA-RA for AI Teams: A Modern Feedback Blueprint

The RA-RA Feedback Model—Ready, Align, Reflect, Adjust—isn’t just a people-first framework. It’s a leadership-first system. And that means it extends naturally into the world of AI-driven work.

Here’s how each phase adapts to leading with AI:

Ready

Before AI contributes, it must be primed. Leaders set the direction through clear prompts, accurate data, and ethical boundaries. If you're not ready to lead it, it’s not ready to perform.

Have you given your AI the context, constraints, and clarity it needs to succeed?

Align

Once active, AI outputs must be assessed. Is the AI producing results that align with your mission, brand, and values? Misalignment isn’t just technical, it can be reputational.

Are the outputs supporting the outcomes you want to see?

Reflect

AI performance needs periodic review, not just for efficiency, but for quality, nuance, and long-term impact. This isn’t about emotional reflection; it’s strategic reflection.

Is the system helping or hindering your people, your processes, and your priorities?

Adjust

Feedback becomes real when it leads to change. With AI, that means refining prompts, updating parameters, improving datasets, or even removing unnecessary automation.

How are you closing the loop and evolving your AI based on what you're learning?

The Future of Feedback Is Human-Led and AI-Aware

The RA-RA Model was built to help leaders navigate feedback with clarity and courage. But it’s not just about people anymore. It's about the systems we lead, the tools we trust, and the outcomes we own.

AI doesn’t replace leadership, it reflects it. And if you’re leading in the age of AI, your feedback model needs to keep pace.

Use RA-RA not just to grow your team but to guide your tech.

About the Author

Clayton Thompson, Ph.D., is a Colonel in the U.S. Air Force with over 20 years of leadership experience. He is the author of the upcoming book RA-RA Feedback: It’s Not a Moment. It’s a System! for building trust, accelerating growth, and creating a leadership advantage.