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1. AI Project Cycle

Purpose

Description of the complete five-phase AI lifecycle that serves as the central roadmap for every AI project.

1. Objective

This document defines the complete methodology for AI projects and forms the foundation of the AI project cycle. It describes the 5 phases of AI projects and serves as the central roadmap for the team.

Applicability

This project cycle applies to both project types: projects that use AI as part of the development process (Type A — building with AI) and projects where AI functionality is part of the end product (Type B — AI in the product). The phasing, gates and evidence standards are identical; the difference lies in the nature of the deliverables per phase. See Project Type Classification for details.

2. Overview of the AI Lifecycle

A successful AI project is not a linear process, but an iterative cycle in which technology, business and compliance are continuously aligned. The AI lifecycle consists of 5 phases that overlap and reinforce one another:

graph TD
 A[Discovery & Strategy] --> B[Validation]
 B --> C[Development]
 C --> D[Delivery]
 D --> E[Monitoring & Optimisation]
 E --> A

Key Characteristics

  • Iterative: Each phase learns from the previous and feeds the next.
  • Hybrid: Combines predictable planning with agile execution (see Hybrid Methodology).
  • Compliance-First: EU AI Act compliance is integrated into every phase.
  • Traceability: Every decision is supported by evidence.
  • Human Oversight: Humans remain responsible for AI decisions.

3. The Five Lifecycle Phases

[!TIP] The Fast Lane (The Innovation Route) For projects with a Minimal/Limited Risk level and an Instrumental/Advisory mode (Mode 1 & 2) we offer an accelerated route. Following a positive Risk Pre-Scan (Gate 1), a limited Validation pilot can be started directly, without an extensive business case.

Discovery & Strategy

📍 Objective: Identifying the right problem and verifying that we are ready to start.

Core Activities

  • Problem Exploration: Define the problem from the user's perspective, not from the technology's perspective.
  • Data Evaluation: Assessing Access, Quality and Relevance of the data.
  • Risk Inventory: Determining whether the application falls under the EU AI Act (high risk).

Validation

📍 Objective: Proving that the idea works and is financially viable before making a major investment.

Core Activities

  • Validation Pilot (PoV): Small-scale experiment to test the hypothesis.
  • Cost Overview: Estimating investment versus ROI.
  • Fairness Check (Bias Detection): Initial scan for undesired bias in the model.

Development

📍 Objective: Building a robust, production-ready solution.

Core Activities

  • Specification-First Method: Write tests first, then implement.
  • Knowledge Coupling: Connecting the AI to internal business information.
  • Model Fine-Tuning: Optimising the parameters and Steering Instructions.

Delivery

📍 Objective: A safe Go-live and acceptance by the organisation.

Core Activities

  • Go-live Plan: Phased rollout to production.
  • Human Oversight: Implementing supervision protocols.
  • Adoption & Training: Training users in the new way of working.

Monitoring & Optimisation

📍 Objective: Retaining value and keeping the solution current.

Core Activities

  • Performance Degradation Monitoring: Continuously monitoring accuracy and drift.
  • Cost Control: Optimising consumption and resources.
  • Feedback Loop: Feeding user experiences back to Phase 1.