How to Decide When Machine Learning Is the Right Solution for Your Project

Machine learning (ML) has traditionally been applied to scenarios involving repeatable and predictive patterns, especially in customer experience contexts. However, the rise of generative AI and large language models (LLMs) has expanded the possibilities, allowing ML applications even without extensive training datasets. Despite this, ML is not a universal solution, and project managers must carefully evaluate whether ML is the right fit for their customer’s needs.

Key considerations when deciding to implement ML include:

• Inputs and outputs: Understanding what customers provide as input and expect as output is fundamental. For example, a Spotify playlist generated by ML uses inputs like user preferences and liked songs.
• Combinations of inputs and outputs: The complexity and variety of input-output pairs influence whether ML or simpler rule-based systems are appropriate.
• Patterns in data: Detectable patterns in inputs and outputs guide the choice of ML models. Supervised or semi-supervised models may be more cost-effective than LLMs when patterns exist.
• Cost and precision: LLMs can be expensive and sometimes imprecise. For many applications, supervised models or rule-based systems offer better cost-efficiency and accuracy.

A practical framework helps project managers decide on ML use by categorizing customer needs:

1. Repetitive tasks with the same input-output pair often do not require ML; rule-based systems suffice (e.g., autofilling email forms).
2. Repetitive tasks with the same input but different outputs (like generating unique artworks per click) benefit from ML techniques such as image generation or recommendation algorithms.
3. Repetitive tasks with different inputs but similar outputs (like grading essays) may use classifiers or topic modeling if patterns exist; otherwise, LLMs might be needed for one-off cases.
4. Repetitive tasks with different inputs and different outputs (such as customer support queries) almost always require ML, including LLMs with retrieval-augmented generation or decision trees.
5. Non-repetitive tasks with varied outputs, like hotel or restaurant reviews, are well-suited for LLMs and advanced neural networks.

In conclusion, choosing ML should be a strategic decision based on the complexity of customer needs, cost constraints, and desired precision. Avoid over-engineering by selecting the simplest effective solution, whether that is a rule-based system, a supervised model, or an LLM. This approach ensures scalable, accurate, and cost-efficient AI-driven products.