{"id":58423,"date":"2026-05-01T23:43:40","date_gmt":"2026-05-02T06:43:40","guid":{"rendered":"https:\/\/svch.io\/ai-project-abandonment-six-factors-executive-framework-145-cases-analysis\/"},"modified":"2026-07-15T11:34:47","modified_gmt":"2026-07-15T18:34:47","slug":"ai-project-abandonment-six-factors-executive-framework-145-cases-analysis","status":"publish","type":"post","link":"https:\/\/svch.io\/es\/ai-project-abandonment-six-factors-executive-framework-145-cases-analysis\/","title":{"rendered":"Why Most AI Projects Die Before They Finish"},"content":{"rendered":"
\n AI Adoption Strategy & Investment Decision-Making<\/span><\/p>\n

Your Board Says Build AI. Here Is Why Most AI Projects Die Before They Finish.<\/h1>\n

Every boardroom is hearing the same message: build AI or be left behind. There is just one problem with that narrative. Most AI projects never make it to production.<\/strong><\/p>\n

New research published April 30 by Shreya Chappidi<\/strong> and Jatinder Singh<\/strong> analyzed 145 real-world cases of AI systems that were never built or were abandoned mid-development. The findings directly challenge the hype.<\/p>\n

The single biggest reason AI projects die is not ethics. It is not regulation. It is not data quality or model performance.<\/p>\n

It is organizational dynamics<\/strong>. Lack of executive support. Internal politics. Teams that cannot align.<\/p>\n

The authors identify six categories of factors that drive AI project abandonment. The most surprising finding is not that any single factor dominates. It is how the factors interact \u2014 and which ones organizations systematically underestimate.<\/p>\n

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If you are writing AI investment checks, you need this framework. It will save you more money than any AI system you are considering building.<\/strong><\/p>\n<\/p>\n<\/div>\n

Executive Summary<\/h2>\n

This paper provides the first comprehensive, evidence-based taxonomy of why organizations do not build or abandon AI systems.<\/p>\n

The six factors that kill AI projects:<\/strong><\/p>\n

    \n
  1. Organizational dynamics<\/strong> \u2014 The #1 killer. Lack of executive support, internal politics, misalignment between teams, organizational inertia.<\/li>\n
  2. Development lifecycle challenges<\/strong> \u2014 Data quality problems, model performance gaps, integration complexity, maintenance burden.<\/li>\n
  3. Resource constraints<\/strong> \u2014 Talent shortages, budget overruns, infrastructure costs, competing priorities.<\/li>\n
  4. Ethical concerns<\/strong> \u2014 Bias, fairness, privacy, transparency, accountability.<\/li>\n
  5. Stakeholder feedback<\/strong> \u2014 User resistance, customer pushback, employee displacement fears, partner objections.<\/li>\n
  6. Legal and regulatory concerns<\/strong> \u2014 Compliance uncertainty, liability questions, regulatory ambiguity.<\/li>\n<\/ol>\n

    The critical finding:<\/strong> Ethical concerns dominate the public narrative about AI failure. But organizational dynamics, development lifecycle challenges, and resource constraints are cited more frequently as the actual causes of abandonment.<\/p>\n

    The practical implication:<\/strong> Executive sponsorship is the single highest-impact lever for AI project success. If the C-suite is not genuinely aligned \u2014 not just approving, but actively championing \u2014 the project is already at risk.<\/p>\n

    Paper at a Glance<\/h2>\n\n\n\n\n\n\n\n\n\n\n
    Metric<\/th>\nValue<\/th>\n<\/tr>\n
    Title<\/strong><\/td>\nTo Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems<\/td>\n<\/tr>\n
    Authors<\/strong><\/td>\nShreya Chappidi, Jatinder Singh<\/td>\n<\/tr>\n
    Published<\/strong><\/td>\nApril 30, 2026<\/td>\n<\/tr>\n
    Venue<\/strong><\/td>\narXiv (Computer Science)<\/td>\n<\/tr>\n
    Cases Analyzed<\/strong><\/td>\n145 systematic literature review<\/td>\n<\/tr>\n
    Relevance Score<\/strong><\/td>\n94\/100<\/td>\n<\/tr>\n
    Focus Domain<\/strong><\/td>\nAI adoption strategy, investment decision-making<\/td>\n<\/tr>\n
    Paper URL<\/strong><\/td>\narxiv.org\/abs\/2604.28053<\/a><\/td>\n<\/tr>\n<\/table>\n

    The Six Factors in Detail<\/h2>\n
    \n

    \u26a0\ufe0f Factor 1: Organizational Dynamics \u2014 The #1 Killer<\/h3>\n

    This is the factor executives can most directly control \u2014 and the one they most frequently overlook.<\/p>\n

    When executive sponsorship evaporates \u2014 because leadership changes, strategic priorities shift, or the champion leaves \u2014 AI projects lose the air cover they need to survive the inevitable challenges of development.<\/p>\n

    The paper’s finding is blunt:<\/strong> organizations that build AI successfully have sustained executive sponsorship that extends beyond the initial approval. Approval is not sponsorship.<\/p>\n<\/p>\n<\/div>\n

    \n

    Factor 2: Development Lifecycle Challenges<\/h3>\n

    Data quality is the perennial problem. Organizations discover mid-project that their data is not clean enough or available at sufficient scale. Model performance in production rarely matches prototype results.<\/p>\n

    The pattern:<\/strong> organizations underestimate the gap between prototype and production by a factor of three to five in both time and cost.<\/p>\n<\/p>\n<\/div>\n

    \n

    Factor 3: Resource Constraints<\/h3>\n

    AI talent is scarce and expensive. Budget overruns trigger funding reviews. Competing priorities pull team members. Infrastructure costs grow as projects scale.<\/p>\n

    Compounding effect:<\/strong> Resource constraints amplify every other factor. A project with weak sponsorship that loses its lead data scientist is far more likely to be abandoned.<\/p>\n<\/p>\n<\/div>\n

    \n

    Factor 4: Ethical Concerns<\/h3>\n

    The factor that dominates headlines. Algorithmic bias, privacy violations, lack of transparency. Real and consequential \u2014 but not the primary reason projects get abandoned in practice.<\/p>\n

    The lesson:<\/strong> Do not let the ethics narrative distract from the organizational and practical factors more likely to kill your AI initiative.<\/p>\n<\/p>\n<\/div>\n

    \n

    Factor 5: Stakeholder Feedback<\/h3>\n

    Users resist. Customers push back. Employees fear displacement. Stakeholder resistance often reflects that the AI project was designed without sufficient stakeholder engagement.<\/p>\n

    The insight:<\/strong> Stakeholder feedback failure is a failure of change management, not technology.<\/p>\n<\/p>\n<\/div>\n

    \n

    Factor 6: Legal and Regulatory Concerns<\/h3>\n

    Compliance uncertainty. Unclear liability. Regulatory ambiguity \u2014 especially in regulated industries. Often cited alongside other factors in multi-factor abandonment scenarios.<\/p>\n<\/p>\n<\/div>\n

    The Factor That Gets All the Headlines \u2014 and the Factors That Actually Matter<\/h2>\n

    The conventional narrative says ethics kills AI projects. The data tells a different story.<\/p>\n

    Ethical concerns are real and consequential. But they are not the primary driver of AI abandonment in practice.<\/p>\n

    This changes your risk mitigation priorities:<\/strong><\/p>\n