Practical Scenarios for Using Other AI Techniques

Generative AI has its place in business today, but it is not always the best tool for every scenario. Below are six common scenarios where traditional AI techniques may be more suitable than generative AI:

1. Classification tasks: Spam detection, sentiment analysis, and image classification are examples of classification tasks that can be handled by traditional AI techniques such as support vector machines (SVM), decision tree approaches, neural networks, or logistic regression.

2. Prediction tasks: Credit card fraud detection is a great example of a prediction task, where previous customer purchase history data is analyzed to make a prediction on whether a new purchase is fraudulent. Prediction tasks rely on techniques such as linear models, neural networks, or gradient boosting techniques for predicting continuous values, such as housing prices, lifetime value, or sales forecasts. 

3. Anomaly detection: Industrial equipment monitoring often uses anomaly detection, whereby data is analyzed to identify unusual patterns that differ from the larger dataset. In this type of scenario, techniques like isolation forests, one-class SVM, or autoencoders are ideal for identifying unusual instances.

4. Recommendation systems: Improving the customer experience by recommending products or services based on the customer’s history is where recommendation systems come into play. Collaborative filtering or content-based filtering are typically the techniques used for these situations.

5. Reinforcement learning: Reinforcement learning involves machine learning models being taught to follow instructions and perform tasks. Advertising and marketing often use this technique to show online users ads based on what sites they’ve visited or from companies that are similar. Q-learning or policy gradients are required for these tasks that involve dynamic decision-making.

6. Interpretability and explainability: When using AI models, it’s important to understand how the model achieved the output, and to be able to trust the output. Interpretability and explainability are the models that provide that information and assurance. Healthcare and finance, for example, are industries that rely on critical outputs, such as algorithms to determine medication dosage or customer credit worthiness. These are situations that will opt for decisions trees, rules-based systems, or linear models.

Choosing the Right AI Approach

To select the right AI technique for your situation, you must understand the problem completely, assess the data available to you, and define your desired outcome. In many cases, it’s not a traditional AI vs generative AI answer, but rather a combination of both approaches. Both have unique strengths and combining techniques often provides a better result than using just one or the other. For example, traditional predictive AI might be used to identify customers who are receptive to a cross-sell offer, while generative AI could be used to craft personalized messaging for customers. Thoroughly understanding the problem’s requirements and limitations is essential when determining the most appropriate AI model.

Navigating the AI landscape can be confusing, but you don’t have to go it alone. Find out how Concentrix can help you leverage AI effectively and develop a tailored roadmap with the right technologies for your specific challenges.