Even as we emerge from generative AI’s tire-kicking phase, it’s still true that many (most?) enterprise artificial intelligence and machine learning projects will derail before delivering real value.

Executives have poured billions into artificial intelligence, only to discover that most of those projects never make it past the pilot stage or fail to deliver meaningful returns. A recent wave of ...

Overview:  Most AI strategies fail due to unclear goals, poor data quality, and weak execution, not because of limitations in ...

This week, an exercise in separating truth from hype. I am old enough to remember when generative AI (genAI) was the best thing since sliced bread — destined to solve any and all problems. But CIO.com ...

The claim that “AI projects are failing” has become a familiar headline—and a valid one. But while the failure rate may be high, it’s not necessarily cause for alarm. In fact, understanding why these ...

Projects don't just fail because of bad luck; they fail because we can't calculate the ripple effects of change as quickly as ...

In 2025, to borrow a phrase: the AI revolution is already here; it's just not evenly distributed. While individuals are seeing productivity gains from LLMs or newer agentic systems, larger projects ...

American enterprises spent an estimated $40 billion on artificial intelligence systems in 2024, according to MIT research. Yet the same study found that 95% of companies are seeing zero measurable ...

Most AI projects don't fail at the start. They get approved. They get built. In some cases, they even look impressive. And then, a few months later, the business is still running the same way. That's ...