AI can be applied in a variety of business and operational scenarios. However, it is important to understand where AI excels and where it may not be the best solution.
1. Use Cases Where AI and ML Shine
Repetitive Tasks and Workload Reduction:
- AI can automate tasks that are repetitive and tedious for humans, freeing up employees for more valuable and creative work.
- Example: AI-powered chatbots can handle basic customer service inquiries, allowing human agents to focus on more complex issues.
Analyzing Vast Amounts of Data:
- AI can process and analyze large datasets at a speed and scale that humans cannot. This capability is especially useful in sectors like finance, healthcare, and retail.
- Example: AI is used in stock trading, where it can analyze market trends and make predictions much faster than human traders.
Pattern Recognition and Fraud Detection:
- AI is great at recognizing patterns, which makes it ideal for detecting anomalies in data, such as fraud or security breaches.
- Example: AI models can be used by banks and credit card companies to detect fraudulent transactions based on patterns in spending behavior.
Forecasting and Waste Reduction:
- AI can predict demand for products and resources, helping companies avoid waste by optimizing inventory and production schedules.
- Example: Retailers use AI for inventory management, predicting which products will sell best and ensuring stock levels are appropriate.
Improving Decision-Making:
- By analyzing large sets of data and finding insights, AI can help businesses make more informed decisions.
- Example: AI models can help optimize pricing strategies, supply chain management, or marketing campaigns based on historical data and trends.
2. Scenarios Where AI May Not Be the Best Option
High Cost of Training and Resources:
- Training AI models, especially complex machine learning models, can be expensive in terms of computing power and resources. For some problems, the cost of developing and maintaining an AI model may not justify the benefits.
- Example: Developing a highly accurate AI model for fraud detection might require extensive data labeling, computing resources, and frequent retraining, which can be costly for small businesses.
Interpretability Issues in Complex Models:
- Deep learning models, which mimic human brain networks, are difficult to interpret. This can be a major issue when AI is used to make important decisions, such as approving loans or insurance claims.
- Example: A complex neural network might predict loan approval but won’t clearly explain why it made that decision. This lack of transparency can be a barrier for industries that require accountability or regulatory compliance.
When Deterministic Results Are Required:
- If a system needs to always provide the same output for the same input (i.e., deterministic behavior), rule-based systems are often a better choice than machine learning models.
- Example: In scenarios like credit scoring, a rule-based system can ensure consistent, predictable decisions, whereas an AI system might provide different results even with the same input due to its probabilistic nature.
Example of Rule-Based System:
- In a simple loan approval system, a rule could be set that all applicants with a credit score above 750 get approved for a loan, providing a clear, consistent decision-making process without the uncertainty introduced by AI models.
3. Key Takeaways
Best Use Cases for AI:
- AI excels at automating repetitive tasks, analyzing large datasets, detecting patterns, forecasting demand, and making data-driven decisions.
Limitations of AI:
- AI can be costly to develop and maintain, especially when resources are scarce. Additionally, the complexity of AI models can lead to issues with interpretability and transparency, particularly in regulated industries. If deterministic results are needed, rule-based systems may be a better solution.
4. Real-World AI Applications
MasterCard:
Uses AI to detect fraud in transactions, improving detection rates and reducing false positives.
In 2024, added generative AI to enhance fraud detection by 20%.
DoorDash:
Replaced outdated IVR system with Amazon Lex to allow customers to speak instead of pressing buttons.
Resulted in a better customer experience, reduced hold times, and increased self-service.
Laredo Petroleum:
Uses real-time monitoring with AWS and machine learning to track sensor data from oil and gas wells.
Helps reduce environmental impact and improve maintenance by detecting leaks and operational issues.
Booking.com:
Uses AI and generative AI in the AI Trip Planner app to offer personalized booking recommendations.
Combines customer data with generative AI for better, more current recommendations.
Pinterest:
Uses machine learning with Amazon services like S3 and SageMaker to identify objects in images.
Pinterest Lens allows users to take a picture and find similar products in online catalogs.
AffordableTours.com:
Uses Amazon Forecast to predict call volumes and optimize customer service agent staffing.
Reduced missed call rates by 20% through better forecasting and resource management.
