Building an AI Prototype

Purpose

A scenario for developing an AI solution prototype with limited scope: integration with processes/systems and preparation for an industrial pilot (Gate 3).

Core Ideas

• A prototype is a working solution built on a subset of data/processes, taking into account requirements for quality, reproducibility, and compatibility with governance.
• It includes: stabilization of the model and pipeline, interfaces with existing systems, basic monitoring, and a scaling plan.
• Gate 3 verifies readiness for a production pilot: stability, alignment with risks and regulations.

How It Works

1. Prototype specification: scope boundaries, target users/processes, pilot success criteria.
2. Development: model, data pipeline, API/integrations, minimal monitoring and logging.
3. Governance compliance: review of data, model, and AI ethics (see ai-governance-model).
4. Pilot: launch within a limited perimeter, collection of feedback and metrics.
5. Gate 3: decision to move to "Production" (industrial deployment) or to refine/close.

A successful Gate 3 is the entry point into the deploy-ai-solution playbook.