AI pillar · Module 3 of 6

AI in the real world

Enough theory. Let’s talk about what AI is actually being used for today—the good, the overhyped, and the genuinely transformative.

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Where AI is actually delivering value

The proven use cases

  • Search and recommendations: Google, Netflix, Spotify. This is AI at scale, working.
  • Spam and fraud detection: Your email filter catches most spam. Your bank catches most fraud. Both rely heavily on ML.
  • Translation: Google Translate is genuinely useful. Not perfect, but transformative.
  • Voice assistants: Siri, Alexa, Google Assistant. Useful for basic tasks.
  • Image recognition: Photo organisation, medical imaging analysis, quality control in manufacturing.

The emerging applications

  • Coding assistance: GitHub Copilot, Cursor. Genuinely helpful for many developers.
  • Writing assistance: Drafting, editing, brainstorming. Useful with human oversight.
  • Customer service: Chatbots handling routine queries. Mixed results.
  • Data analysis: Finding patterns in large datasets. Promising but needs expertise.
  • Drug discovery: Accelerating molecular research. Early but exciting.

The overhyped and the problematic

Let’s be honest about where reality doesn’t match the marketing:

  • “AI will replace your job tomorrow.” Some jobs will change significantly. Complete replacement is slower and harder than headlines suggest. The real story is augmentation, not replacement.
  • “Autonomous vehicles are almost here.” We’ve been “5 years away” from full self-driving for about 10 years. The edge cases are hard.
  • “AI can do anything a human can do.” It can’t. It excels at specific tasks it’s trained for. General-purpose intelligence remains elusive.

🎯 The practical lens

When someone pitches you an AI solution, ask: “What problem does this actually solve? What are the failure modes? What happens when it’s wrong? Who’s accountable?” These questions cut through the hype.

Free resources to go deeper