AIF-C01 Question 21
Select 2A retail company wants to improve customer satisfaction by reducing response times for frequently asked questions. They also want to provide product recommendations based on customer behavior. Which of the following are practical AI use cases for addressing these requirements?
- A
Implementing a chatbot to handle customer inquiries in real-time
- B
Using a recommendation system to suggest products based on customer browsing and purchase history
- C
Deploying a blockchain-based solution to track inventory across the supply chain
- D
Setting up an AI-powered fraud detection system to monitor transactions
- E
Applying computer vision to identify defects in product images before listing them online
Show answer and explanation
Correct answers: A, B
Explanation
The company’s goals are to reduce response times for frequently asked questions and provide personalized product recommendations. AI-powered chatbots and recommendation systems directly address these objectives, making them practical use cases. Other options like blockchain, fraud detection, and computer vision are valid AI applications but are unrelated to the specific requirements of this scenario.
- A. Correct.
This is correct. Chatbots powered by AI can efficiently respond to customer inquiries in real-time, improving response times and customer satisfaction.
- B. Correct.
This is correct. Recommendation systems use AI to analyze customer behavior and provide personalized product suggestions, which aligns with the company’s goal of improving customer experience.
- C. Incorrect.
This is incorrect. Blockchain is not directly related to addressing customer satisfaction or product recommendation use cases in this scenario.
- D. Incorrect.
This is incorrect. While fraud detection is a valid AI use case, it is unrelated to the specific goals of reducing response times and providing product recommendations.
- E. Incorrect.
This is incorrect. Although computer vision is a practical AI use case, it is not relevant to improving customer satisfaction or personalizing product recommendations in this context.