The theoretical need for an RL model to 'explore' new strategies is perceived by organizations as unpredictable, high-risk volatility. To gain trust, exploration cannot be a hidden technical function. It must be reframed and managed as a controlled, bounded, and explainable business decision with clear guardrails and manageable consequences.
Designing the reward function for an RL pricing model isn't just a technical task; it's a political one. It forces different departments (sales, operations, finance) to agree on a single definition of "good," thereby exposing and resolving hidden disagreements about strategic priorities like margin stability versus demand fulfillment.
When determining what data an RL model should consider, resist including every available feature. Instead, observe how experienced human decision-makers reason about the problem. Their simplified mental models reveal the core signals that truly drive outcomes, leading to more stable, faster-learning, and more interpretable AI systems.
When deploying AI for critical functions like pricing, operational safety is more important than algorithmic elegance. The ability to instantly roll back a model's decisions is the most crucial safety net. This makes a simpler, fully reversible system less risky and more valuable than a complex one that cannot be quickly controlled.