{"id":6691,"date":"2024-12-10T16:28:26","date_gmt":"2024-12-10T21:28:26","guid":{"rendered":"https:\/\/www.stratospherenetworks.com\/blog\/?p=6691"},"modified":"2025-06-18T16:38:50","modified_gmt":"2025-06-18T21:38:50","slug":"why-ai-projects-fail-18-must-know-reasons","status":"publish","type":"post","link":"https:\/\/www.stratospherenetworks.com\/blog\/why-ai-projects-fail-18-must-know-reasons\/","title":{"rendered":"Why AI projects fail: 18 must-know reasons"},"content":{"rendered":"

\"AFailure is sometimes unavoidable. As Captain Jean-Luc Picard of \u201cStar Trek: The Next Generation\u201d explains in one episode<\/a>, \u201cIt is possible to commit no mistakes and still lose. That is not a weakness. That is life.\u201d<\/p>\n

However, in other instances, it\u2019s possible to pinpoint missteps that caused a project to derail. Reflecting on the mistakes that hampered a professional endeavor and formulating a plan to avoid committing the same errors in the future is vital if you want to achieve long-term success.<\/p>\n

While failure is a possibility for all kinds of projects, ventures focused on artificial intelligence (AI)<\/a> fall short at exceptionally high rates. For example, one common reason for AI project failure is a mismatch between your strategic business and AI initiative objectives. This can lead to misunderstandings surrounding the business value of the AI project or failure to integrate AI with pre-existing processes and systems. Other potential problems include unrealistic expectations, subpar data quality, and a lack of change management efforts.<\/p>\n

Some estimates place the failure rate for AI projects at more than 80 percent, according to a research report<\/a> published in August 2024 by the non-profit think tank RAND. That\u2019s roughly twice the failure rate for IT endeavors that don\u2019t involve AI.<\/p>\n

If you\u2019re planning to launch an AI-related project, it\u2019s wise to research common mistakes that hinder artificial intelligence deployments. Then, you can craft a strategy that includes safeguards to ensure your team won\u2019t commit those errors.<\/p>\n

Industry experts and researchers have identified the following as some of the top reasons why AI projects fail, according to our partner Quiq<\/a>, RAND<\/a> and Deloitte\u2019s 2024 Q3 State of Generative AI in the Enterprise report<\/a>.<\/p>\n

    \n
  1. Lack of specific key performance indicators to measure generative AI (genAI) performance and impact<\/li>\n
  2. Risk, regulation and governance issues<\/li>\n<\/ol>\n
      \n
    1. Data-related problems (e.g., lack of quantity or quality, maintaining security and privacy, and utilizing sensitive data in models)<\/li>\n
    2. Reluctance among upper management to adopt and overall internal staff resistance to change<\/li>\n
    3. Shortfall of change management efforts<\/li>\n
    4. Unrealistic and overambitious expectations<\/li>\n
    5. Lack of internal AI expertise and comprehensive skill combinations<\/li>\n
    6. Recruitment efforts focused on advanced degrees<\/li>\n
    7. Dominance by the IT department in AI projects<\/li>\n
    8. Little or no cross-functional support for the project within the organization<\/li>\n
    9. Lack of team coordination<\/li>\n
    10. Integration issues<\/li>\n
    11. Restrictive use cases<\/li>\n
    12. Disconnect with the overall business and customers<\/li>\n
    13. Misunderstanding or miscommunication of which problems the project aims to address with AI<\/li>\n
    14. Prioritization of utilizing the latest tech over solving end users\u2019 problems<\/li>\n
    15. Focus on issues that are too complex for AI to address<\/li>\n
    16. Lack of infrastructure required to manage data for and deploy AI models<\/li>\n<\/ol>\n

      Ultimately, to successfully complete an AI project, you must create a detailed plan and strategy defining your venture\u2019s purpose and context, including specific and enduring pain points that you plan to remedy and that AI can feasibly address.<\/p>\n

      Don\u2019t forget to include change management efforts and end user adoption promotion in your project plan, and don\u2019t get so caught up in the idea of finding the best tech with all the bells and whistles that you forget what truly matters: Achieving your clearly defined objectives and mitigating or eliminating the problems you set out to solve.<\/p>\n

      If you want to deploy AI to improve outcomes for your business but aren\u2019t sure where to start, our trusted technology advisors<\/a> are here to help. We\u2019ll leverage our 20+ years of IT industry experience, the latest market data, and a partner network that includes best-in-class conversational AI<\/a> and generative AI<\/a> suppliers to craft an AI strategy that aligns with your unique needs and objectives.<\/p>\n

      For more information, please visit our artificial intelligence for businesses page<\/a>. You can also connect with our technology advisors by calling 877-599-3999 or emailing sales@stratospherenetworks.com<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

      Failure is sometimes unavoidable. As Captain Jean-Luc Picard of \u201cStar Trek: The Next Generation\u201d explains in one episode, \u201cIt is possible to commit no mistakes and still lose. That is not a weakness. That is life.\u201d However, in other instances, … Read more ><\/span><\/a><\/p>\n","protected":false},"author":5,"featured_media":6692,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[261],"tags":[],"class_list":["post-6691","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-tips-from-the-president"],"yoast_head":"18 reasons why AI projects fail<\/title>\n<meta name=\"description\" content=\"Some estimates place the failure rate for AI ventures at over 80 percent. 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