Business

AI Scaling

Definition

The process of expanding a successful AI pilot to broader organisational adoption—handling increased data volumes, additional use cases, more users, and tighter integration with business processes. Scaling typically exposes infrastructure, data governance, and change management challenges invisible at pilot scale.

Related Terms

Production AI

AI systems that are live in enterprise environments, processing real transactions and influencing business decisions at scale. Production AI requires robust MLOps practices, monitoring, compliance controls, and SLAs—a significantly higher bar than prototype or pilot systems.

AI Pilot

A time-boxed, scope-limited real-world test of an AI solution designed to validate assumptions, measure business impact, and identify operational challenges before full deployment. AI pilots typically run 6–12 weeks and are structured to produce clear go/no-go criteria.

MLOps

The practice of combining Machine Learning, DevOps, and data engineering to streamline the deployment, monitoring, and maintenance of ML models in production. MLOps ensures reliable, scalable, and reproducible ML systems across their entire lifecycle.

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