The artificial intelligence (AI) landscape is plagued by a significant disparity between the number of AI projects initiated and those that successfully reach production. According to a ModelOp survey, approximately 80% of AI projects fail to make it to production, with only 18% of enterprises deploying more than 20 models into production. This phenomenon is known as the AI execution gap.
One of the primary causes of the AI execution gap is the prevalence of fragmented systems within organizations. A staggering 58% of organizations cite fragmented systems as a major obstacle to adopting governance platforms, creating silos that hinder consistent oversight of AI initiatives. Furthermore, 55% of enterprises still rely on manual processes like spreadsheets and email to manage AI use case intake, leading to bottlenecks and errors.
The lack of standardization is another significant contributor to the AI execution gap. Only 23% of organizations implement standardized intake, development, and model management processes, making each AI project a unique challenge requiring custom solutions and extensive coordination. Moreover, just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight.
To bridge the AI execution gap, organizations must prioritize standardized processes for AI use case intake, development, and deployment. This includes maintaining centralized documentation and inventory, providing visibility into every model's status, performance, and compliance posture. Automated governance checkpoints should be embedded throughout the AI lifecycle to ensure compliance requirements and risk assessments are addressed systematically.
By adopting these strategies, organizations can improve operational efficiency, reduce time-to-market, and increase confidence among business stakeholders. Ultimately, bridging the AI execution gap will enable organizations to unlock the full potential of AI and drive business success.
