What are the three golden rules of AI?
Start from a business problem, not a tool. Respect your data (quality, security, consent). Keep a human in the loop for anything that matters.
Rule one is the hardest to follow. Most failed AI projects started with "we need to use AI" instead of "we need to fix X." Tools are not strategies. Pick the problem first, then the approach, then the tool.
Rule two is about foundations. If your data is messy, ungoverned, or full of PII that has not been handled properly, no model will save you. Clean data and good access controls are prerequisites, not optional.
Rule three is about accountability. AI systems should help humans make decisions faster and better, not replace human judgment on things that affect customers, employees, or your reputation. The more consequential the output, the more oversight it needs.
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Related questions
What is the 30% rule in AI?
There is no standard, official "30% rule" in AI. The phrase is used loosely online. The rule of thumb we use: start by automating the roughly 30% of a workflow that is repetitive and low-judgment, and leave the 70% that needs context, approval, or nuance to humans.
What are the 5 things AI cannot do?
AI cannot feel empathy, take responsibility, reason well in novel situations with no data, set strategy or values, or be held legally accountable for outcomes.
What is the biggest flaw of AI?
It sounds confident even when it is wrong. Fluent output is not the same as accurate output, and that gap is where most AI-caused damage happens.
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