Assess AI Readiness Across Key Dimensions CAIPM Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   An insurance firm wants to automate claims processing with AI. The claims team currently follows a manual, multi-step workflow with multiple approvals. The company has a strong data warehouse and a skilled data science team, but the claims process is not standardized across regional offices. What is the primary process readiness challenge?

   • ALack of standardized workflows across regions
   • BInadequate AI skills in the claims team
   • CLack of executive sponsorship
   • DInsufficient data quality for model training
   Reveal Solution Discussion Select an answer first
2. 7expert · hard

   A retail company's AI readiness assessment shows: strong technology, moderate data quality, and low workforce digital literacy. The company has a limited budget and must choose between a data quality project and a workforce upskilling program. The AI use case is customer churn prediction, which requires historical sales data. The workforce will need to interpret and act on the model's output. What is the best prioritization decision?

   • ASplit the budget equally between both initiatives
   • BPrioritize the workforce upskilling program first, then address data quality
   • CPrioritize the data quality project first, then plan a minimal upskilling program
   • DDefer both initiatives and start building the model with existing data
   Reveal Solution Discussion Select an answer first
4. 8expert · hard

   A global retailer wants to use AI to optimize pricing. The company's strategic goal is to increase market share, but the pricing AI would need to lower prices, which conflicts with the CFO's profit margin targets. The data science team has a model ready, but the marketing team is concerned about brand perception. The company has a mature data infrastructure. As the AI program manager, what is the best course of action?

   • AFacilitate a strategic alignment workshop to reconcile the market share and profit margin goals before deployment
   • BAdjust the AI model to prioritize profit margins over market share
   • CDelay the AI project until the CFO changes the profit targets
   • DDeploy the AI model in a limited market to test the impact on market share and margins
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A non-profit organization wants to use AI to optimize donation allocation. The organization has limited resources and no prior AI experience. The board wants a simple, low-cost way to assess readiness before committing funds. Which approach is most appropriate?

   • AUse a lightweight AI readiness checklist covering strategy, data, people, and processes
   • BAdopt a complex AI maturity model with detailed scoring
   • CCommission a full external AI maturity audit
   • DStart building a prototype immediately to learn by doing
   Reveal Solution Discussion Select an answer first
6. 10application · medium

   A media company wants to use AI to personalize content recommendations. The company has a modern cloud infrastructure and a data lake, but the data science team uses a variety of open-source tools that are not integrated with the company's security and compliance systems. The team wants to deploy models quickly. What is the most important technology readiness gap?

   • AInsufficient cloud storage capacity
   • BLack of GPU resources for model training
   • CInadequate data labeling tools
   • DLack of integration between AI tools and enterprise security/compliance systems
   Reveal Solution Discussion Select an answer first