Organizational Readiness and AI Maturity Assessment
CAIPM · 20 questions
- City IT rates AI readiness and finds no link between active AI pilots and the comprehensive plan. Which readiness dimension is primarily weak?
- County public health reviews whether nursing staff have time and skills for an AI charting assistant. Which readiness dimension is the focus?
- A transit authority scores AI readiness and finds ridership feeds lack consistent stop IDs. Which readiness gap is most directly indicated?
- A water utility checks whether cloud landing zones and identity controls exist before hosting an AI platform. What readiness dimension is being assessed?
- A clerk’s office discovers widespread ‘shadow ChatGPT’ use with no approved channels and flags a readiness concern. Which dimension is primarily implicated?
- A city AI office compares its program to peer cities using a published AI maturity model before budgeting a PMO. What practice is this?
- A housing authority rates its AI work as mostly one-off pilots with no shared portfolio metrics. Which maturity contrast best describes the gap?
- Emergency management benchmarks AI maturity on governance, data, and delivery practices against a published model. What is the primary purpose?
- A library system scores ‘initial’ AI maturity because every chatbot is a disconnected vendor trial. How should leaders interpret that result?
- A county board asks how AI maturity scores should influence next year’s investment sequencing. What is the sound program response?
- Digital services plans an AI readiness assessment using structured interviews and document reviews across departments. What does this approach emphasize?
- Public works designs a scored questionnaire covering strategy, data, technology, people, and risk for AI readiness. What assessment design principle is illustrated?
- A 311 readiness workshop surfaces that proposed AI services lack named product owners. What readiness insight should the program manager act on?
- An assessor office readiness report rates data as red and executive sponsorship as green. How should the AI program manager respond?
- A city attorney joins AI readiness fieldwork to review records-retention impacts of proposed GenAI pilots. Why include that stakeholder?
- Union concerns about job redesign emerge during planning for a permitting copilot. Which adoption risk category is most clearly indicated?
- A permit AI pilot stalls because inspection workflow SOPs were never updated for AI-assisted triage. What risk type does this illustrate?
- A proposed AI citation triage tool cannot reach needed data because the legacy mainframe lacks usable APIs. Which adoption risk is primary?
- Privacy counsel flags regulatory risk in a GenAI pilot that would ingest resident health notes. What should the program manager recognize?
- After a viral wrong answer from a city chatbot, public trust drops and the wider AI portfolio is questioned. Which risk category is most implicated?