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When companies focus on practical, user-centered implementation, AI can stop being an experiment and start having a real impact.
Forrester predicts that, in 2026, one-quarter of CIOs will be asked to bail out business-led AI failures in their organizations. With the recent wave of generative AI and LLMs changing how AI is ...
Community driven content discussing all aspects of software development from DevOps to design patterns. Agile software development is one of the most proven approaches to building software and ...
Renowned venture capitalist and influencer Naval Ravikant shared his perspective on why most cryptocurrency projects end up failing. What Happened: On Sunday, Ravikant, a respected figure in the ...
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MIT explains why most AI projects are failing
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 ...
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.
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 ...
Agentic AI is not just another iteration of automation or generative AI. Agentic AI systems can autonomously manage complex tasks, optimize processes, and proactively identify opportunities or risks, ...
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Why more than half of AI projects could fail in 2026
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 ...
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