🧱 Contextual Scaffolding Framework
Transparency label: AI-assisted
The development of a knowledge-based product in collaboration with an AI requires structured contextual scaffolding. This scaffolding takes the form of two complementary models: a phase model to structure the overall lifecycle, and a project model to guide collaborative work within each phase.
The phase and project models are flexible and should be applied with as light a touch as possible. The AI will assist in mapping out this programme of work — a key part of its contextual scaffolding.
🔁 Phase Model (Cradle-to-Grave Structure)
The phase model provides end-to-end structure across the full lifecycle of the product.
- Concept — Explore potential benefits, clarify purpose, and draft a business case.
- Definition — Specify the intended final deliverable in sufficient detail to support structured development.
- Development — Develop and test the final product.
- Acceptance — Validate the deliverable in the hands of its intended users, ensure it meets defined needs, and bring it into use.
- Operation and Maintenance — Use, monitor, and refine the product until it is retired.
Not every phase will require a full project structure, but where structured collaboration is needed, the project model applies.
🧱 Project Model (Within-Phase Scaffolding)
Within each phase, a project-based approach provides structure for collaborative delivery.
1. Defined Goal
The project is established to deliver a clear, knowledge-based product. Typically, this will be the output required from a phase.
2. Project-Based Structure
Work within the phase is organised into discrete stages, each with defined objectives and outputs.
3. Stage Gates
Progression from one stage to the next requires a quality review of outputs. Only satisfactory products allow forward movement.
4. Product-Focused
Each stage produces one or more interim products. Most are “technical” in nature and build towards the final deliverable; some are to manage progress.
5. Human-AI Collaboration
The AI acts as a collaborator.
- The human is the decision-maker.
- The AI adapts to the nature of work at hand.
- Because contextual scaffolding keeps pace with the progress of work, the AI remains grounded and its responses stay relevant and precise.
6. Adapted PRINCE2 Principles
Draws selectively from PRINCE2, including:
- Product-based planning
- Management by exception (e.g. gates)
- Clear roles and responsibilities
- Embedded quality control
7. Quality Management
A simplified quality management process ensures control:
- Each product has a defined description or acceptance criteria
- Quality is assessed at stage gates
- Feedback loops enable iteration and correction
📄 Example: Preparing a Competitive Tender
When preparing a competitive tender, the knowledge product is a complete, high-quality bid submission. However, at the outset, the scope, fulfillment strategy, resource requirements, and pricing model will not be clear.
Phase Model in Action
The work progresses through phases – starting with a concept phase to assess feasibility and value, followed by definition (specifying scope and win themes), development (producing the actual bid), acceptance (review and sign-off), and finally operation and maintenance (post-submission follow-up or revision).
Project Model in Each Phase
Within each phase, a lightweight project structure is applied to guide collaboration with the AI. For example, in the definition phase, the AI helps analyse requirements, map obligations, and develop outline responses. In the development phase, it helps draft, refine, and format bid content.
Contextual Scaffolding
At every stage, contextual scaffolding ensures the AI is working with the right background, priorities, and current materials. Thus it can focus on what matters most, and contribute in ways that are coherent, precise, and aligned with both the tender requirements and internal strategy.
Transparency label justification. This document was developed through structured collaboration between Alec Fearon and ChatGPT. Alec provided the core ideas, framing, and sequence. ChatGPT contributed to the organisation, refinement, and drafting of the text, under Alec’s direction. The content reflects a co-developed understanding, with human oversight and final decisions throughout.