AAI-M case studies
AAI-M · Put your knowledge to work in context.
The AAI-M (Artificial Intelligence Management) credential is aimed at professionals who need to direct AI work rather than build models themselves - team leads, product and operations managers, analysts moving into AI-adjacent roles, and consultants who advise on AI adoption. It tests whether you can speak precisely about AI systems and make sound decisions about them, not whether you can write training code. The AAI-M case studies are 5 cases and about 2,358 words.
1The Support Triage Pilot That Looked Great in Week One
A regional insurer deploys a ticket-classification model to route inbound support emails to 14 queues. In the two-week pilot, 1,200 tickets were processed.
457 words, about 2 min2The Churn Model With a Leaky Feature
A subscription fitness app builds a churn model. The data science team reports AUC 0.97 on a held-out test set and the marketing VP wants to ship it to prioritize retention offers.
487 words, about 2 min3The RAG Assistant That Cites Confidently Wrong Policy
An HR shared-services team deploys a retrieval-augmented generation assistant over 9,000 internal policy documents to answer employee questions about leave, expense, and conduct. In a 300-question evaluation, 88% of answers were rated 'helpful' by reviewers, but 6% contained a policy statement contradicted by the source document, and 4% cited a document that exists but does not contain the claim.
474 words, about 2 min4The Fairness Audit That Found a Proxy
A bank uses a model to shortlist small-business loan applications for manual review. The model was trained on five years of historical approvals.
476 words, about 2 min5The GenAI Pilot With No Owner
A 400-person logistics firm runs a 10-week generative AI pilot: 60 employees use an assistant to draft customer emails, summarize carrier contracts, and write SQL against the operations warehouse. Adoption is strong (48 daily active users by week 6).
464 words, about 2 min