Despite the growing importance of artificial intelligence (AI), only 7% of organizations achieve daily AI deployments, highlighting a significant delivery infrastructure gap. According to the CNCF’s 2025 Annual Survey, a staggering 93% of organizations deploy AI models infrequently, underscoring the need for improved CI/CD automation, GitOps, and observability tailored for AI. Traditional software delivery systems falter in supporting the unique demands of AI model serving, which requires specialized validation, larger model files, and dedicated hardware.
To bridge this gap, organizations must adapt existing infrastructures to be AI-ready. Key components include CI/CD pipelines for models, tailored GitOps workflows, and Kubernetes orchestration. Implementing these systems can lead to significant ROI, as demonstrated by a Brazilian bank that reduced ML time-to-impact by 30%. By assessing AI deployment readiness and addressing infrastructure weaknesses, organizations can enhance delivery velocity and drive innovation in AI. Implementing MLOps principles is crucial for success in today’s AI-driven landscape.
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