The rise of generative AI, particularly large language models (LLMs), has transformed how machine learning (ML) is applied in customer-facing use cases. Traditionally, ML was reserved for situations with repeatable, predictable patterns and required large datasets. However, modern generative models now allow ML to be deployed even without complete training data. This shift has expanded the possibilities but also raised new questions about when ML is truly necessary.
Carefully Assess AI Suitability Based on Inputs, Outputs, Cost, and Scalability Factors
Despite AI’s growing capabilities, it’s not a universal answer for every customer problem. LLMs can be expensive and lack precision, making them unsuitable in many practical scenarios. AI project managers must carefully assess customer needs before deciding on implementation. The question isn’t just whether AI can be used—but whether it should be. Cost, accuracy, and scalability remain key factors in this evaluation.
Project managers should consider the types of inputs and outputs involved in a customer experience. For instance, if the output must adapt to a variety of different inputs—or if patterns emerge from input/output combinations—ML may be appropriate. If the combinations are few or straightforward, simpler systems like rule-based algorithms may suffice. Understanding the scale and variability of the task helps in choosing the right approach.
Use AI Wisely by Matching Machine Learning Tools to Customer Needs and Output ComplexityBalancing Accuracy, Cost, and Complexity to Choose the Right AI Implementation Approach
Cost and precision are also central to deciding between traditional models and LLMs. While LLMs offer impressive generative capabilities, they are not always accurate and can be cost-prohibitive at scale. In contrast, supervised learning models or classifiers can often deliver more reliable results with lower resource requirements. Sometimes, a hybrid approach—like using decision trees or retrieval-augmented generation (RAG)—strikes the right balance between performance and cost.
Ultimately, AI project managers must evaluate the complexity and nature of customer needs to determine the best-fit ML approach. The final takeaway: not every problem needs an advanced AI model. Use the right tool for the job by referring to a structured matrix of use cases, examples, and implementation strategies, which balances performance, scalability, and budgetary constraints.
Customer Needs Matrix
| Type of Customer Need | Example | ML Implementation | Type of ML Implementation |
|---|---|---|---|
| Same output for same input (repetitive) | Autofill email in forms | No | Rules-based system |
| Different outputs for same input | Generate new artwork per click; Discovery-based search | Yes | Image-generation LLMs; Recommendation algorithms |
| Similar outputs for different inputs | Grading essays; Extracting themes from feedback | Depends | Rules-based if simple; otherwise use classifiers, topic modeling; LLMs only for unstructured, one-off cases |
| Different outputs for different inputs | Customer support Q&A; Search functions | Yes | LLMs with RAG; Decision trees |
| Non-repetitive tasks with varied outputs | Hotel/restaurant reviews | Yes | LLMs; RNNs; LSTMs |
