Why 80% of Enterprise AI Projects Fail to Launch—and How Governance Can Bridge the Gap

Global spending on artificial intelligence is set to skyrocket, with IDC forecasting that investments in AI and generative AI (GenAI) will more than double to $631 billion by 2028. But for all the boardroom enthusiasm and ambitious roadmaps, most enterprise AI projects never make it past the planning phase.

According to the 2025 AI Governance Benchmark Report by ModelOp, which surveyed 100 senior AI and data leaders from Fortune 500 companies, over 80% of enterprises have more than 50 generative AI projects in the pipeline. Yet only 18% have successfully deployed more than 20 models into production.

This discrepancy, often referred to as the "AI execution gap," highlights a sobering reality: while the ambition is high, operational follow-through remains elusive.

Why the AI Execution Gap Persists

Despite rapid advances in AI technology, the biggest barriers to AI scalability aren’t technical—they’re operational. ModelOp’s report pinpoints four major challenges:

• Fragmented Systems:
  58% of organizations cite fragmented infrastructure as the primary roadblock. Departments often use incompatible tools, creating data silos and making governance across teams nearly impossible.
• Manual Workflows:
  Over half of enterprises still rely on outdated tools like spreadsheets and email to manage AI project intake. These manual processes slow down execution, introduce errors, and choke scalability.
• Lack of Standardization:
  Only 23% of enterprises have consistent processes for intake, development, and model management. Every AI initiative becomes a one-off, demanding heavy coordination across departments.
• Limited Oversight:
  Just 14% perform AI assurance at the enterprise level. This lack of centralized governance leads to duplication, gaps in accountability, and increased risk.

A Shift in Strategy: From Compliance to Competitive Advantage

However, a new trend is emerging. Forward-thinking companies are reframing governance not as a compliance drag, but as a driver of innovation and speed.

• Leadership Buy-In:
  Nearly half (46%) of enterprises now place AI governance responsibility under a Chief Innovation Officer rather than Legal or Compliance. This signals a pivot toward viewing governance as strategic rather than restrictive.
• Dedicated Budgets:
  36% of companies are investing over $1 million annually in AI governance platforms. More than half have earmarked funds specifically for AI Portfolio Intelligence to measure ROI and value contribution.

What Leading Enterprises Are Doing Differently

Organizations that are successfully scaling AI initiatives tend to follow a few key principles:

• Standardized Workflows:
  From intake to deployment, successful enterprises establish repeatable, structured processes that allow for seamless project execution.
• Centralized Asset Management:
  Maintaining a unified inventory of AI models ensures transparency around usage, performance, and compliance.
• Automated Governance:
  Embedding automated checks throughout the AI lifecycle helps streamline compliance and flag issues early—before they become liabilities.
• Full Traceability:
  Robust governance includes tracking every aspect of a model’s development, from training data and algorithms to validation results and real-world performance.

The Payoff: Speed, Scale, and Confidence

The benefits are tangible. One financial services firm profiled in the report cut its time to production in half and reduced issue resolution time by 80% after implementing governance automation. That kind of operational efficiency translates directly into faster time-to-value and improved confidence among both internal and external stakeholders.