AI failures are making headlines—from McDonald's drive-thru disasters to Air Canada's costly chatbot mistakes—but these aren't just technology problems, they're implementation failures. As 42% of CIOs prioritize AI and ML for 2025, the gap between innovation and responsibility is widening. Bias in training data, unexplainable "black box" decisions, poor data provenance, and misuse of AI tools are creating legal, ethical, and reputational risks that can't be ignored. The solution isn't slowing down—it's building smarter. By embedding responsible AI practices into every stage of your SDLC, establishing clear cross-functional ownership, and integrating ethics checkpoints into existing workflows, you can harness AI's potential without the pitfalls.
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