Module 05 · Enterprise AI Use Cases
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14 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.
- Generative AI is the right answer for almost every enterprise use case. From The Enterprise AI Use-Case Landscape
- Most AI projects fail because the models are not good enough. From AI Use-Case Discovery and Design
- Finance is rules-based, so most of it can be automated end to end. From AI in Finance
- Removing gender and age fields makes a hiring model fair. From AI in Human Resources
- A lead score tells sales who will buy. From AI in Sales and Marketing
- The best customer-service AI is the one that deflects the most contacts. From AI in Customer Service
- The bullwhip effect is mainly caused by poor forecasting models. From AI in Operations and Supply Chain
- If AI-generated code compiles and passes its tests, it is correct. From AI in Software Engineering
- AI-generated prototypes mean a team can skip product discovery. From AI in Product Development
- Attackers might start using AI once the technology matures. From AI in Cybersecurity
- Better enterprise search has largely solved the problem of finding internal information. From AI in Knowledge Management
- OCR and AI document processing are the same thing. From AI in Document and Data Processing
- A more accurate forecast automatically creates value. From AI in Prediction, Forecasting and Optimization
- The agent with the higher completion rate is the better one to scale. From AI Agents and Intelligent Workflows