I've watched dozens of AI projects fail. Not because the technology didn't work. Because organizations approached them wrong.
Here's what I've learned:
1. Start with the problem, not the solution Too many teams say "we need AI" without asking "what problem are we solving?"
2. Build trust incrementally Don't try to automate everything at once. Start small. Prove value. Expand.
3. Invest in infrastructure first The sexiest AI model is useless without clean data and reliable pipelines.
4. Embrace the hybrid approach The best AI systems augment human decision-making. They don't replace it.
What patterns have you seen in successful AI implementations?
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