- Demonstrate how analytics can support managerial-level decision-making.
- Examine the benefits of Artificial Intelligence (AI) and describe its importance for the future.
Full Credit for a Discussion (See grading rubric below):
Full Credit for a Discussion (See grading rubric below):
Third, analytics can help managers to optimize their operations. For example, a manager at a transportation company might use analytics to optimize delivery routes. This could help to reduce costs and improve efficiency.
Here are some specific examples of how analytics can be used to support managerial-level decision-making:
Benefits of Artificial Intelligence (AI)
AI has the potential to revolutionize the way that businesses operate. AI can be used to automate tasks, improve efficiency, and make better decisions.
Here are some of the specific benefits of AI for businesses:
Importance of AI for the future
AI is likely to play an increasingly important role in business in the future. As AI technology continues to develop, it will become more and more capable of performing tasks that are currently done by humans. This will have a significant impact on the workforce, as many jobs will be replaced by AI.
However, AI is also likely to create new jobs. As businesses adopt AI, they will need to hire employees who have the skills and knowledge to develop, implement, and manage AI systems. Additionally, AI will create new opportunities for businesses to innovate and grow.
Here are some specific examples of how AI is being used in business today:
Overall, AI is a powerful tool that can be used to improve business performance in a number of ways. As AI technology continues to develop, it is likely to play an even more important role in business in the future.
First, analytics can help managers to identify trends and patterns in data. This information can be used to make informed decisions about everything from product development to marketing campaigns. For example, a manager at a retail company might use analytics to identify which products are selling well and which ones are not. This information could then be used to make decisions about which products to stock and how to promote them.
Second, analytics can help managers to predict future outcomes. For example, a manager at a manufacturing company might use analytics to predict demand for a particular product. This information could then be used to make decisions about production levels and inventory.