Virtually every organization is trying its hand at AI, yet very few are seeing the payoff. Despite massive investment, most organizations aren’t seeing the results they were hoping for. According to MIT’s State of AI in Business 2025 report, 95% of enterprise AI initiatives are failing to deliver measurable P&L impact, and only 5% of pilots make it into production with real value creation.
So why are so many companies coming up short with their AI projects despite the large amounts of money, time, and resources being poured into these efforts? Here are seven common mistakes being made across corporate America and how to avoid them.
Mistake #1: The business goal isn’t crystal clear
Before even starting an AI project, ask yourself: what’s the problem that we’re trying to solve? Many projects fail simply because the precise goals aren’t defined upfront. If things are left too loosey-goosey and vague, that can result in mixed expectations within your organizati
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