65 data scientists/engineers were interviewed to determine what makes AI/ML projects fail:
👉🏽 industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI.
👉🏽 the organization often lacks the necessary data to adequately train an effective AI model.
👉🏽 AI projects may fail because the organization focuses more on using the latest and greatest technology than on solving real problems for their intended users.
👉🏽 organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure.
👉🏽 AI projects may fail because the technology is applied to problems that are too difficult for AI to solve.
🔗 LINK:
https://lnkd.in/gr78q5iF