Choosing the Right AI Approach for the Problem
Not every business problem needs a complex model. This post breaks down how to choose an approach that fits the goal and the data—when simple rules are enough, when classical ML works best, and when it makes sense to use more advanced methods—so the system stays maintainable and delivers measurable value.
18 January 2026
The handoff is the product
In most real workflows, the model doesn't replace the human—it routes work. The quality of that routing, and the clarity of the handoff, is what determines whether people trust and adopt the system.
Thresholds and defaults
Confidence thresholds decide what's automated and what's escalated. Sensible defaults cover the gap when the model can't decide, so behavior stays predictable under uncertainty.
Keeping friction low
A good handoff tells the human what the system thinks, why, and what it needs next—inside the tool they already use. Low friction here is what turns a prototype into something people rely on daily.
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Questions About This Project
Tie it to the cost of errors versus the cost of escalation, then tune it against real cases rather than offline metrics alone.
Only if thresholds are set blindly. Calibrated thresholds plus good defaults keep escalations focused on the cases that genuinely need judgment.
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