Meridian's fraud-detection team is choosing between a rule that says 'always flag a redemption over 50,000 miles' (deterministic) and a model that outputs 'this transaction has an 82% likelihood of being fraudulent' (probabilistic). What is the key project-management trade-off the PM should explain to stakeholders?

CPMAI Task 3 requires differentiating probabilistic and deterministic approaches, and the practical trade-off is exactly what a PM needs to explain to non-technical stakeholders: a deterministic rule is transparent and predictable but rigid, missing fraud patterns that fall outside its fixed threshold and potentially flagging legitimate high-mileage redemptions unnecessarily. A probabilistic model can capture more nuanced, learned patterns, but it produces a confidence score rather than a certainty, requiring the business to set and justify an action threshold (e.g., investigate anything above 70% likelihood) and to communicate that uncertainty honestly. The 'no meaningful difference, both produce yes/no' distractor glosses over a real and consequential design choice — how the flag is generated and how much nuance it captures differ substantially. The 'probabilistic is strictly worse because it's not 100% certain' distractor sets an impossible bar; almost no real-world predictive system offers certainty, and demanding it would make most of Meridian's AI portfolio unusable. The 'deterministic is unsupervised learning, probabilistic is reinforcement learning' distractor misclassifies both — a fixed threshold rule is not a learning approach at all, and a probability-output classifier is typically supervised learning, not reinforcement learning. This distinction directly informs how the PM sets acceptance criteria with the fraud team.