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Intro: AI Isn't Always the Answer
Alright, product managers, gather around. It's time to have a heart-to-heart about AI. In the glamorous world of tech, AI is like the shiny new toy that everyone wants. But just because you can slap AI on a problem doesn't mean you should. If you're considering AI just because it's trending, this lesson's for you.
When AI Is Overkill
1. The 'Simple Math Would Do' Scenario:
Imagine you're at a dinner party and someone asks you to solve 2 + 2. Sure, you could whip out your phone, open a calculator app, and get the answer, but a pencil and paper would do just fine. Similarly, if your problem can be solved with basic algorithms or even a good old-fashioned spreadsheet, AI might be overkill.
What comes next
Why this matters for PMs: If you can solve a problem with less complexity, do it. Choosing AI when it's unnecessary can result in wasted resources and increased maintenance costs.
2. The 'Data Desert' Dilemma:
AI feeds on data like a teenager on pizza. If you're trying to implement AI without sufficient, high-quality data, you're setting yourself up for disappointment. Remember, garbage in, garbage out.
Why this matters for PMs: Evaluate the data you have before committing to an AI solution. If you don't have the right data, the AI won't perform well, and your project might flop.
Finish: When NOT to Use AI (Seriously)
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