Pre‑training Process AI models sounds logical—but it would actually undermine predictive accuracy for the models that would be created by an extract of completed cases. This post explains why historical case data creates a single‑outcome trap, how Process AI builds stage‑specific models in real time, and why continuous learning is the only way to avoid 50/50 guessing in active cases.
Pega Community - How Real Time Learning Unlocks Predictive Accuracy in Process AI
Pre-training Process AI models is not the right approach because it can lock the system into past outcomes and reduce accuracy.
Historical case data often creates a single-outcome bias, where the model starts guessing around a fixed pattern instead of real variation.
The correct solution is to avoid pre-training and instead build models directly from active, stage-specific case data.
Each stage of the process should generate its own model based on current inputs and decisions.
This keeps predictions aligned with real case behavior instead of outdated historical patterns.
Continuous learning during execution ensures the system improves accuracy instead of falling into 50/50 guessing.