SpatzAI: Enabling Converging Conversations Versus Micro-Managing Feedback

In today’s workplaces, feedback is often boxed into rigid frameworks, as shown in the graphic titled How to Give Feedback: 6 Essential Models for Every Situation. While these models—COIN, BOOST, GROW, FEED, CEDAR, and 360-Degree Review—are useful for some, they risk overcomplicating what should be a natural part of everyday communication. Here’s where the difference lies between micro-managing feedback and converging conversations using SpatzAI.

The Problem with Micro-Managing Feedback

The image offers several formulas that, at first glance, seem effective. Each has a purpose: behavior correction, coaching, performance reviews, and so on. Yet, the structure becomes a crutch. Feedback, in this format, may feel forced, impersonal, and overly scripted. The essence of genuine conversation—an organic back-and-forth exchange—gets lost in the pursuit of ticking boxes (Context, Observation, Impact, etc.).

While frameworks like COIN and FEED aim to ensure clarity, they often promote micro-managing interactions. Managers may become more concerned about whether they’ve followed the model rather than focusing on the feedback’s impact. The process may slow down necessary feedback or, worse, come across as performative rather than supportive.

SpatzAI and the Power of Converging Conversations

At the heart of SpatzAI lies the converging conversation—an adaptive, fluid approach that evolves naturally as a conflict or difference arises. Rather than adhering to rigid steps, SpatzAI encourages individuals to address feedback in real-time through conversation that moves toward resolution. This allows for more contextual nuance and personalization, making each interaction unique and relevant to the parties involved.

Why We Think SpatzAI is Superior to Structured Feedback Models:

1. Real-Time Adaptability: Converging conversations adapt to the moment. Unlike pre-defined models, they aren’t locked into a specific framework or checklist. They evolve based on the flow of the discussion, allowing for authentic engagement.
2. Context-Specific Insights: SpatzAI allows feedback to be tailored to the situation’s specifics, rather than shoehorning it into a rigid framework. Each interaction can consider subtleties of relationships, project dynamics, and individual communication styles.
3. Avoiding Prescriptive Language: Many models rely on specific terms and steps, which can feel scripted. SpatzAI focuses on natural language, fostering a more human, empathetic dialogue where both parties reach a point of convergence.
4. Psychological Safety Through SpatzAI: Instead of delivering feedback in a top-down way, SpatzAI promotes a psychologically safe environment through its Caution, Objection, Stop process. The goal is to converge toward mutual understanding, not simply to critique or correct.

The Flow of Converging Conversations

In a converging conversation, the focus is on active listening, allowing the conversation to move towards a shared understanding. Rather than emphasizing “steps” (e.g., Context, Observation, Impact), SpatzAI users learn to organically address objections and disagreements, seeking convergence instead of compliance with a model. This approach reflects how real conversations work—dynamic, responsive, and evolving in the moment.

While structured models for feedback have their place, they risk becoming rigid and impersonal. SpatzAI, with its focus on converging conversations, offers a more flexible, human-centered approach. It ensures that feedback is genuinely constructive, evolving naturally without the need to micro-manage every word spoken.