Failure
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Why are so many AI projects destined for failure? Inexperienced staff, poor planning, and a shoehorned approach to agile development are all stifling innovation
AI projects fail because of technical challenges, people misunderstanding what’s possible, and agile development, according to research from the RAND institute. Research cited by RAND suggests as many as 80% of AI projects fail — that’s twice the rate of other technology projects and a serious problem for the industry given the costs involved with AI. RAND notes that the…
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Generative AI Projects Risking Failure Without Business Executive Understanding
Chris Hillman, international data science director at data management firm Teradata, has recently seen more attention directed towards the cost of data science and AI teams, as businesses seek to demonstrate value from their investments in emerging technology. However, he believes that data scientists are capable of building AI models on a technical level, and it is often business stakeholders…
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