New RAND Research – Why do AI Projects Fail?

By some estimates, 80% of AI projects fail – more than double the rate of non-AI IT projects – but why? A group of RAND researchers investigated this issue and found five key problems leading to failure:

• Misunderstanding or miscommunicating the problem the AI needs to solve
• A lack of data need to adequately train an effective AI model
• A bias toward the latest and greatest technology, rather than focusing on solving real problems for their users
• A lack of adequate infrastructure to manage data and deploy completed AI models
• Applying the technology to problems too difficult for AI to solve

To avoid the same pitfalls, the authors offer a handful of recommendations for both industry and academia including choosing enduring problems; focusing on the problems instead of the technology; investing in infrastructure up front; understanding AI’s limitations and creating partnerships between academia and government.

See the full report here.

Sign up for the free insideAI News newsletter.

Join us on Twitter: https://twitter.com/InsideBigData1

Join us on LinkedIn: https://www.linkedin.com/company/insideainews/

Join us on Facebook: https://www.facebook.com/insideAINEWSNOW