A CTO I know greenlit an AI project with a $150,000 price tag. Eighteen months later, the actual spend was closer to $600,000. The vendor hadn't lied—their platform really did cost $150K. But that number didn't include the data engineering work, the model retraining, the integration costs, the additional cloud infrastructure, or the three people now managing the system full-time.
A friend who runs a mid-sized fashion retailer told me something interesting last month. Their homepage conversion rate jumped 34% after they stopped showing everyone the same hero image. Instead, they started showing people products similar to what they'd browsed before—or, for new visitors, items trending among people with similar browsing patterns.
Last Tuesday, I watched a CMO explain to her board why their AI-powered recruitment tool had been systematically rejecting qualified candidates from certain zip codes. The tool had learned patterns from historical hiring data—patterns that reflected decades of unconscious bias. Nobody had programmed it to discriminate. It just learned what it was shown.
Imagine you're managing a warehouse in Memphis during peak holiday season. You've got thousands of packages moving through your facility daily, but half your workforce just called in sick, a snowstorm is bearing down on your primary delivery routes, and your biggest customer just tripled their usual order. Twenty years ago, this scenario would have meant frantic phone calls, educated guesses about which routes might still be passable, and probably some disappointed customers. Today, a well-implemented AI system can handle this complexity before you've finished your morning coffee.
Last Tuesday, your support team handled 247 tickets. Thirty-eight of them were password resets. Twenty-two were about shipping times. Nineteen asked where to find a feature that's clearly labeled in your navigation menu. Your most experienced agent spent forty minutes explaining something your documentation already covers beautifully. Meanwhile, three genuinely complex issues sat in the queue for hours because everyone was buried in routine questions.
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