Module 04 · AI Strategy
Module check
15 statements, one from each chapter of the module. Call each one myth or fact. Take it once before the module and again after it: the difference is what the module taught you. Your scores stay in this browser.
- An organization with fifty AI projects has, by definition, an AI strategy. From What Is an Enterprise AI Strategy?
- If most employees use AI, the AI strategy is working. From AI Strategy vs AI Adoption
- If competitors use AI in an area, we must use it there too. From Start With Business Strategy
- A higher AI ambition means a better strategy. From Defining AI Ambition
- A comprehensive list of AI use cases is a good first draft of an AI strategy. From Finding Strategic AI Opportunities
- If competitors could get exactly this capability tomorrow and we would still have an advantage, the capability is our edge. From Build vs Buy vs Partner
- Most organizations run every element of AI under the same model. From Centralized vs Federated AI
- An AI platform means everyone uses one standard model. From AI Platform Strategy
- We need a complete, modernized data platform before we can start with AI. From Data Strategy for AI
- The size of the AI team is a good measure of an organization's AI capability. From AI Talent and Capability Strategy
- An AI operating model is mainly a question of where the AI team sits on the organization chart. From AI Operating Model
- If a vendor's model caused the error, the vendor is accountable for the outcome. From AI Decision Rights and Accountability
- If an AI tool cuts our unit cost by 15 percent, we have gained a 15 percent cost advantage. From AI and Competitive Advantage
- The organization with the most data in a market will keep the lead. From Proprietary Data, AI Moats and Differentiation
- A portfolio of well-run AI pilots adds up to an AI strategy. From Module 04 Synthesis — The Executive AI Strategy