While investment in enterprise AI is surging, a substantial execution gap is preventing many projects from reaching production. IDC forecasts that worldwide spending on AI and GenAI will soar to $631 billion by 2028, yet organizations are facing significant challenges in operationalizing their AI initiatives.
According to ModelOp’s 2025 AI Governance Benchmark Report, a stark contrast exists between AI ambition and actual deployment. Over 80% of surveyed enterprises reported having more than 50 generative AI projects in the pipeline, but only 18% have successfully deployed over 20 models into production. This disparity is largely due to systemic challenges such as siloed systems, reliance on manual processes, and a lack of standardized procedures.
Forward-thinking companies are now viewing AI governance not as a compliance hurdle, but as a catalyst for scalability and accelerated deployment. These organizations are implementing strategies such as standardizing workflows, centralizing documentation, automating governance checkpoints, and establishing comprehensive end-to-end traceability. Companies leveraging lifecycle automation platforms are experiencing tangible gains in operational efficiency and achieving improved business results.