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 ...

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 ...

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 ...

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 ...

Most tech projects fail because they rush to build the final product before testing the basics or drowning the team in too ...

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 writing about AI focuses on why projects fail, but in my experience, that misses the real issue. Most AI initiatives don’t just fail. They never even begin. They get approved in principle, ...