We give concrete examples to illustrate how thinking with patterns leads naturally to AI adoption. We start with Judgment Call of operation members, i.e the intuition deriving decisions, without any proven causal relationship or empirical evidence. Pattern turns the intuition into a sharper hypothesis with more rigorous justification. Structure builds a toolkit or an AI engine based on the justified pattern.
Correlation
Judgment Call. A growth operation member in a marketplace seeks to increase conversion rate. She notices new retailers whose quotation requests are slowly responded are less likely to order. She therefore treats response speed of quotation allocation as a factor to prioritize new retailers.
Pattern. The operation decision presumes a relationship between retailer enrollment date, quotation-response time, and conversion rate. The same response delay may be more sensitive for a newly enrolled retailer than for an established retailer who trusts the marketplace system. Segmenting retailers by enrollment date reveals the pattern.
Structure. These observations naturally lead to Statistical Correlation. A technical member can construct a descriptive dashboard which sorts retailers by enrollment date, alongside a clear threshold beyond which, retailers are more sensitive to slow response. A further development may be an automated allocation system which prioritizes newly enrolled retailers based on some data-driven distribution.