What AI Really Is (PM Perspective)

As a Product Manager, you don't need to understand the math behind AI. You need to understand what it can do for your users and your business.

AI in Plain English

Artificial Intelligence is software that learns patterns from data and makes predictions or decisions based on those patterns.

Think of it like this:

Traditional Software	AI Software
You write exact rules	Software learns rules from examples
"If email contains 'lottery', mark as spam"	"Learn what spam looks like from 10,000 examples"
Predictable, rigid	Flexible, probabilistic

The Key Terms You'll Hear

Term	What It Means	PM Translation
Machine Learning (ML)	AI that learns from data	The engine that powers AI features
Model	The learned patterns	The "brain" you're deploying
Training	Teaching the model	The expensive part before launch
Inference	Using the model	The cost-per-user part after launch
Generative AI	AI that creates new content	ChatGPT, DALL-E, Midjourney

Generative vs Traditional AI

This distinction matters for product decisions:

Traditional AI (Classification, Prediction):

• "Is this transaction fraudulent?" (Yes/No)
• "Will this user churn?" (Probability)
• "What product should we recommend?" (Ranking)

Generative AI (Content Creation):

• "Write a product description"
• "Generate an image"
• "Summarize this document"

PM Insight: Generative AI is exciting but expensive. Traditional AI often provides better ROI for specific business problems.

When AI is Overkill

AI isn't always the answer. Use this quick test:

Consider AI when:

• Rules are too complex to write manually
• Patterns exist in data but aren't obvious
• You need personalization at scale
• Human review is a bottleneck

Skip AI when:

• Simple rules work fine
• You don't have enough data
• Decisions need to be 100% explainable
• The cost outweighs the benefit

Key Takeaway

AI is a tool, not magic. Your job as a PM is to identify where pattern recognition can solve user problems better than traditional approaches.

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Next: Let's explore what AI is actually good at—and where it consistently fails. :::