# Production Phase

> The production phase is the deployment, operation, and continuous improvement of an AI system in live use, with the controls and monitoring required for the workflow's risk class.

Category: Methodology
Source: https://impetora.com/glossary/production-phase
Part of: Impetora AI consulting glossary (https://impetora.com/glossary)

## What is Production Phase?

Production phase activities include canary deployment, observability wiring, drift monitoring, on-call rotation, incident process, retraining pipeline, change management, user feedback loop, and quarterly review. The first 90 days post-launch are the most critical: this is when most surprises surface. Production phase is ongoing, not a milestone that ends.

## How does Production Phase apply to enterprise AI?

The production phase is where AI value is actually realised and where compliance evidence is generated. Enterprises should plan for 12 months of operating cost, not just build cost, in their AI budget.

## Related terms

- [Pilot Phase](https://impetora.com/glossary/pilot-phase) - The pilot phase is the staged build of a working AI system on a narrow, real workflow with real data, evaluated against success criteria agreed in discovery.
- [MLOps](https://impetora.com/glossary/mlops) - MLOps is the discipline of operating machine learning systems in production: versioning, deployment, monitoring, retraining, and governance.
- [Observability](https://impetora.com/glossary/observability) - Observability for AI is the ability to understand what an AI system did, why it did it, and at what cost, by inspecting its inputs, outputs, intermediate steps, and metrics.
- [Model Drift](https://impetora.com/glossary/model-drift) - Model drift is the gradual or sudden degradation of a model's performance in production caused by changes in input data, target distribution, or operating context.

## External references

- [Impetora methodology](https://impetora.com/methodology)

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