Why Daily AI Deployments are Rare – and How Platform Teams Can Fix That

Despite the rapid advancement of AI, only 7% of organizations deploy AI models daily, a stark contrast to the frequent deployments in traditional software engineering. This disparity is primarily due to the lack of AI-ready delivery infrastructure, which encompasses CI/CD automation, GitOps workflows, and robust observability—practices that are standard in application delivery but often absent in AI operations. AI models necessitate unique deployment considerations, such as statistical validation, large artifact management, and specialized resource orchestration, which traditional systems struggle to accommodate. Research shows a correlation between higher AI adoption and decreased engineering performance, emphasizing the need for platform teams to evolve their systems. By adopting practices like platform engineering and leveraging existing cloud-native tools like Kubernetes, organizations can bridge the gap between data science experimentation and production-grade AI deployment. This transformation not only improves deployment velocity but also enhances innovation capacity and financial performance, as evidenced by organizations that have successfully integrated MLOps best practices.

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