Why most "let's add AI" projects stall halfway
Over the past year we've diagnosed more than thirty "let's add AI" scenarios. Fewer than half actually launched and stayed in daily use. The problem is almost never model capability - it's that the workflow was never actually decomposed.
A common mistake
Most teams start from "we need an AI support agent" instead of "which 20% of issues make up 80% of our ticket volume." The former jumps straight to a solution; the latter is what diagnosis is actually for.
The first thing we do at diagnosis is always pull real historical data - ticket logs, chat transcripts, screenshots of the approval flow - and look at it together, rather than assuming.
What's worth automating first
High-frequency, rule-clear, low-blast-radius steps always rank first. It's why scenarios like insurance Q&A or essay grading tend to show results faster than "automate the whole process" - the boundaries are clear and feedback is immediate.
Published by AI Plus Lab
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