AI Applications, Capabilities, and Limitations

5.1 AI in Everyday Life

AI is often integrated into applications rather than presented as a separate tool. People encounter it when receiving product recommendations, filtering messages, navigating roads, searching for information, or interacting with virtual assistants.

Different applications use different capabilities depending on the task.

5.2 AI Across Industries

These are potential applications, not guarantees of effectiveness. Every real deployment requires appropriate validation and safeguards.

5.3 Benefits of AI

AI can provide several benefits:

• Process large volumes of data.
• Reduce repetitive manual work.
• Identify patterns that may be difficult to detect manually.
• Personalize recommendations and experiences.
• Assist with decision-making.
• Generate drafts and accelerate content creation.

The benefits depend on the task, data quality, system design, and how results are used.

5.4 Limitations and Risks

AI systems also have important limitations.

Accuracy: Predictions and generated responses can be incorrect.

Bias: Training data may not adequately represent all users or situations.

Privacy: Sensitive information must be handled appropriately.

Security: AI applications can be vulnerable to malicious inputs or unsafe integrations.

Cost: Training, inference, infrastructure, and maintenance can be expensive.

Explainability: Complex models may not provide simple explanations for every output.

Changing conditions: A model's performance may decline when real-world data changes.

For these reasons, high-impact applications may require human review, testing, monitoring, and clear accountability.

5.5 Example: AI in Customer Support

Consider an online store that receives thousands of support tickets each month.

An AI-assisted workflow could:

1. Classify incoming tickets.
2. Estimate their urgency.
3. Route them to the appropriate team.
4. Retrieve relevant information from approved support documentation.
5. Draft a suggested response.
6. Allow an employee to review the response before sending it when required.

This solution combines machine learning, generative AI, conventional programming, and automation.