Myself and the model
Transactional Analysis of Human-AI Interaction
Ever feel like working with AI isn’t about managing technology: it’s more as managing a relationship. AI models don’t just “work.” They require constant nudging, oversight and interpretation. And just like human relationships, this dynamic is full of miscommunication, overcorrection and frustration.
The real challenge? Understanding the unspoken rules of this interaction.
Enter Transactional Analysis (TA)—a framework that explains how humans communicate and maybe the same rules can apply to operator-model interaction.
Let’s break it down.
What is Transactional Analysis?
Developed by Eric Berne in the 1950s, TA breaks down human communication into three ego states:
Parent – The rule-maker, enforcing structure and expectations.
Adult – The rational, data-driven problem solver.
Child – The creative, emotional, sometimes rebellious force.
These states don’t just apply to human conversations, they define how operators interact with AI models.
The Operator-AI Relationship Through TA
AI isn’t just a tool, it’s a probabilistic system that responds to inputs, corrections and feedback. And just like people, it gets “trained” based on how we engage with it.
Here’s how the three TA ego states map to AI-human interactions:
1. The Parent Operator: Over Controlling the AI
Mindset: “The model must follow strict rules, or it will fail.”
Behavior: Setting rigid parameters, micromanaging outputs, constantly correcting.
Result: The AI never improves because it’s not allowed to learn from uncertainty.
The Risk: Over-parenting an AI leads to stagnation. The model doesn’t adapt, it just follows orders.
The Fix: Shift to an Adult approach: let AI explore, learn, and refine outputs instead of policing every move.
2. The Child Operator: Overtrusting AI’s Capabilities
Mindset: “AI is magic, it knows more than I do.”
Behavior: Accepting AI outputs without questioning, blindly implementing recommendations.
Result: AI hallucinations go unchecked and bad decisions pile up.
The Risk: Over-trusting AI leads to high-confidence mistakes, bad data, biased models, and unchecked errors.
The Fix: Bring in the Adult mindset: validate AI’s decisions, set up guardrails, and recognize when human judgment is still required.
3. The Adult Operator: Treating AI as a Learning Partner
Mindset: “AI is a tool: it makes predictions, not decisions.”
Behavior: Testing, refining and guiding AI without micromanaging or over-relying on it.
Result: AI becomes a valuable assistant, improving through structured feedback loops.
The Goal: Keep the operator-AI interaction rational, iterative and adaptable.
Why AI PMs Need TA to Manage AI Teams
AI teams struggle not because AI is bad but because humans manage it the wrong way.
Here’s where TA helps AI PMs:
1. Avoiding AI Micromanagement (Parent Trap)
❌ Constant tweaking and restricting AI models kills adaptability.
✅ Instead, define clear success metrics and let AI adjust within those boundaries.
2. Preventing Blind Faith in AI (Child Trap)
❌ Trusting AI blindly leads to hallucinations, biased decisions, and failures.
✅ Instead, set up validation layers to test and refine outputs before acting on them.
3. Building a Balanced AI Strategy (Adult Approach)
✅ Define the AI’s role as an evolving system, not a fixed rule-based tool.
✅ Set up feedback loops where human insights refine AI performance.
Final Thoughts
AI doesn’t think, it predicts.
AI doesn’t decide, you do.
AI isn’t magic, it’s a system that learns from how you treat it.
The best AI PMs don’t just manage models, they manage the human-AI relationship.
So, ask yourself:
Are you guiding AI with clarity and logic? Or are you stuck micromanaging or over-trusting it?
Because in AI, how you communicate with your model determines how well it performs.