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.

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

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 ...

Failure is part and parcel of research, but discussing it sometimes seems to be taboo in science. It doesn’t need to be.

Spread the loveIn recent years, the buzz surrounding artificial intelligence (AI) has escalated to unprecedented levels, with businesses across various sectors rushing to implement AI solutions.

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 ...

In many technical projects, failure is not caused by a lack of talent, but by a lack of structure. Despite the availability ...

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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, ...

Many businesses launch AI chatbots with high hopes, only to see them underperform or fail to scale. Common causes include poor integration with systems, unclear goals, and lack of performance tracking ...