A retailer is deciding where to invest in AI. Some tasks are simple rule lookups, while others involve analysing large, evolving datasets over time…

Machine learning is most valuable when dealing with problems that cannot be solved with simple, deterministic rules. It excels at identifying complex, evolving patterns in large datasets, such as customer behavior, fraud, or market trends. The other options describe tasks that are handled effectively by simple lookups or fixed business logic, where the overhead of machine learning is unnecessary.

1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (See Chapter 1, Introduction, pp. 1-5, which frames machine learning as a tool for discovering patterns in data to make predictions about new data, contrasting it with explicitly programmed instructions).

2. MIT OpenCourseWare. (2020). 6.036 Introduction to Machine Learning, Fall 2020. Lecture 1: Introduction. (This lecture introduces machine learning as the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world, highlighting its use for problems too complex for human-written rules). Available: https://ocw.mit.edu/courses/6-036-introduction-to-machine-learning-fall-2020/

3. Microsoft Documentation. (2023). What is Azure Machine Learning?. (The documentation states, "Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends," which aligns directly with spotting changing patterns). Available: https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning