5 Great Machine Learning Use Cases (not GenAI!)

2023 has been the year of GenAI, so much so that business leaders have almost forgotten about the other areas of machine learning. So I thought I would remind us all of the wonderful use cases in machine learning that are not GenAI focused. Here are 5 of the most common ones that can have such a huge impact in your business.

1. Fraud Detection: Machine learning algorithms can analyze historical data and identify patterns or anomalies that indicate fraudulent activities. For example, in finance, machine learning models can detect credit card fraud by analyzing transaction patterns, identifying unusual behavior, and flagging suspicious transactions for further investigation.
2. Recommender Systems: Recommender systems leverage machine learning to provide personalized recommendations to users. These systems can be found in e-commerce platforms, streaming services, and social media platforms. By analyzing user behavior, preferences, and past interactions, machine learning models can suggest relevant products, movies, music, or content tailored to individual users.
3. Predictive Maintenance: Machine learning can be used to predict equipment failures and schedule maintenance tasks accordingly. By analyzing sensor data, historical maintenance records, and other relevant information, machine learning models can identify patterns or indicators that precede equipment failures. This enables proactive maintenance to avoid unexpected downtime and reduce maintenance costs.
4. Customer Churn Prediction: Machine learning can help businesses predict customer churn (i.e., when customers are likely to stop using a product or service). By analyzing historical customer data, including demographics, purchase behavior, and interactions, machine learning models can identify patterns and indicators of potential churn. This allows businesses to take proactive measures to retain customers and improve customer satisfaction.
5. Demand Forecasting: Machine learning can help businesses accurately predict demand for products or services. By analyzing historical sales data, market trends, seasonality, and other relevant factors, machine learning models can generate forecasts to optimize inventory management, production planning, and pricing strategies. This is particularly valuable for retail, e-commerce, and supply chain management.

So while businesses scramble to see what they can do with Generative AI, remember the tried and tested ML solutions above and ensure your business is getting the benefits out of the other areas of machine learning.

Kevin Derman is the Strategic Alliance Director for AWS & Snowflake at Slalom Consulting. His passion for emerging technology and artificial intelligence is demonstrated through his role as podcast host of the Emerging Tech Podcast (www.emergingtech.cloud) and in his talks and presentations in the realm of AI.