The AI landscape in 2025 is flooded with potential and false starts.

Across industries, companies have launched initiatives with high hopes. But many stall before they ever deliver real value.

The pattern is consistent: early excitement, a flashy demo, and then… nothing. The model can’t scale. The results aren’t consistent. The data isn’t usable. The project dies quietly.

And this pattern is accelerating.

This year, S&P Global reports that 42% of companies have shut down the majority of their AI initiatives—nearly triple the rate from the year before. MIT adds another layer: 95% of generative AI pilots fail to exit the testing phase.

These aren’t growing pains. They’re signs of a deeper design flaw.

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If you’re still processing orders manually, you’re paying a price; and not just in dollars.

Manual order entry drains time, inflates costs, and invites mistakes. According to McKinsey, businesses that rely on manual processing spend 30% more on operational costs than those that automate. That’s money lost to inefficiency — time spent on repetitive tasks, corrections, and rework.

And it’s not just the cost. It’s the risk. Manual data entry has an average error rate of 3% — that’s 3 mistakes for every 100 orders. In fast-moving sectors, those mistakes ripple out: botched deliveries, frustrated customers, and lost trust. In a market where speed is currency, even one wrong digit in an order can turn into a reputation problem.
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