In this course, you'll be introduced to the concepts, methodologies, and tools required for effectively and efficiently incorporating AI into your IT enterprise planning. You'll look at enterprise planning from an AI perspective, and view projects in tactical/strategic and current, intermediate, or future state contexts. You'll explore how to use an AI Maturity Model to conduct an AI Maturity Assessment of the current and future states of AI planning, and how to conduct a gap analysis between those states. Next, you'll learn about the components of a discovery map, project complexity, and a variety of graphs and tables that enable you to handle complexity. You'll see how complexity can be significantly reduced using AI accelerators and how they affect specific phases of the AI development lifecycle. You'll move on to examine how to create an AI enterprise roadmap using all of the artifacts just described, plus a KPIs/Value Metrics table, and how both of these can be used as inputs to an analytics dashboard. Finally, you'll explore numerous examples of AI applications of different types in diverse business areas.
- discover the key concepts covered in this course
- contrast AI enterprise planning with IT enterprise planning and enterprise planning
- describe differences between tactical, strategic, and tactical-strategic planning
- describe the AI Maturity Model and apply it to conduct an AI Maturity Assessment of current and future states to perform a gap analysis
- describe a discovery map, its components, and its role in AI enterprise planning
- recognize the importance of complexity in AI enterprise planning
- describe an AI accelerator and its effect on the AI development lifecycle, and identify some of the main AI accelerators
- describe how AI accelerators reduce the complexity of projects while shortening their timelines
- recognize how to create an AI enterprise roadmap using all previous artifacts plus a KPIs/Value Metrics table, and how both of these are used to feed an analytics dashboard
- describe the different types of AI applications, such as NLP, Analytics, Recommendation engine, etc.
- recognize how different business areas are benefiting from AI