When to Use Machine Learning: A Guide for AI Project Managers

By Netvora Tech News

---

The rise of generative AI has dramatically altered the landscape of machine learning (ML) applications. Traditionally, we've applied ML to repetitive, predictive patterns in customer experiences, but now, it's possible to leverage ML even without a comprehensive training dataset. However, the answer to the question "What customer needs requires an AI solution?" is not always "yes." Large language models (LLMs) can be prohibitively expensive for some organizations, and like all ML models, they're not always accurate. There will always be cases where implementing an ML solution is not the best path forward. So, how do AI project managers evaluate their customers' needs for AI implementation?

Evaluating Customer Needs for AI Implementation

To make this decision, consider the following key factors:

• Data availability**: Do you have sufficient data to train and validate an ML model?
• Business goals**: What are your organization's objectives, and can AI help achieve them?
• Competition**: How does your industry or market landscape impact the need for AI implementation?
• Budget constraints**: Are the costs associated with implementing and maintaining an AI solution feasible for your organization?
• Alternative solutions**: Are there alternative solutions that can achieve the desired results without AI?

By carefully evaluating these factors, AI project managers can determine whether a machine learning implementation is the best solution for their customers' needs.