GenAI Foundations – Chapter 5: Project Planning with the Generative AI Canvas

#ai #data #rag #agile

👉 “A structured framework to design and validate AI initiatives”

Introduction

AI Project Planning

When managing and planning an AI project, it is essential to prepare key questions for the user in advance and clearly define the scope of the work.
In this context, I decided to develop a specific canvas for generative AI projects, inspired by tools such as the Business Model Canvas (used to design business models) and the ML Canvas (focused on the planning of machine learning projects). As in those methodologies, the objective is to offer a visual and structured representation that facilitates the organization of ideas and the definition of priorities.

Canvas Structure

The canvas is organized into four main blocks:
Define Value (Why?)

At the center, the purpose of the project is established. Not everything that can be automated should be this block allows identifying which problems are truly priority and high impact.

Build (What?)

Once the purpose is defined, what to build is determined: requirements, limits, and scope of the solution. Here data dependencies are considered (what information is needed, how it is obtained, and under what conditions it can be used) along with the costs and the required investment.

Deliver (How?)

This block answers how the solution will be implemented. It includes the selection of the appropriate model, the deployment architecture, and the integration with other systems. However, Deliver is not limited to the technical implementation, but also incorporates the definition of evaluation metrics that allow measuring, correcting, and continuously improving the system.

Validate

Finally, the project must be constantly validated. This block covers the comparison of the results with the initial objectives, the monitoring of risks, and the verification that the solution is safe, reliable, and sustainable over time.

From Concept to Canvas: Structuring AI Projects

Below you can see that each section of the Generative AI Project Canvas is accompanied by a series of questions and subtopics that help guide the definition of the project. These guides allow going deeper into the key aspects, from the purpose and value to the data strategy, implementation, and risks, in a structured way.

We must mainly answer

🎯 Define Value (Why?)

Value Proposition: What unique value are we creating with this solution?

🛠️ Build (What?)

Output: What will the AI generate as the final deliverable?
Data Strategy: How will we source, prepare, and update the data?
Costs & ROI: What will it cost and what return will it bring?

⚙️ Deliver (How?)

Implementation: How will we deploy, integrate, and maintain it?
Model Approach: Which model will we use and how will we adapt it?

✅ Validate

Evaluation Metrics: How will we evaluate quality and success?
Risks & Monitoring: What risks exist and how will we mitigate them?

💡 You can access the complete template in its editable version on Google Presentation to reuse it directly.

With this canvas, you now have a complete toolkit: from prompt design to RAG architectures, evaluation, and project planning. The GenAI Foundations series is a starting point to explore, adapt, and responsibly scale generative AI in your domain.