AI Project Canvas: The best way to deploy AI in the organization

AI Project Canvas will help you succeed with the next AI initiative. Visualize important components, activities, and potential risks long before the first line of code.

Why I need this?

As a deep-tech consultant, I have noticed that Project Managers struggle with IT projects powered by AI. That causes a very high rate of failed projects that don't go beyond the piloting stage. One of the main reasons why they have problems is a lack of awareness that AI projects are very different from traditional IT projects.

I found that there is a need for a tool that might help managers collect and combine all major components of the whole project in one place. Looking at the bigger picture might help AI teams explore the main goal, potential scope, and difficulties long before writing the first line of code.

Taking inspiration from a Business Model Canvas, created by Alexander Osterwalder I have build an AI Project Canvas.

What is an AI Project canvas?

An AI Project Canvas is a detailed plan for Team Leaders, Project Managers who create products and services powered by AI.

It consists of nine building blocks that combined together create a three-stage workflow Build-Deploy-Manage. The Build part relates to the Exploratory Data Analysis that needs to be performed in order to extract information from data. The Deploy part is to put the Machine Learning model into production. The Manage part is related to human and financial resources need to finish the project with success.

It is worth to use it each time when you ...

1. Create a concept and plan a roadmap of the project
2. Gather proper resources and estimate cost structure
3. Need to convenience the management to fund the idea

Inspired? Let's explore the whole concept in detail!

How to use it?

Spent no more than 30min to fill a canvas. The main goal is to give you a basic understanding of the project and familiarize you with all the missing puzzles. Is not a detailed plan!

Follow the sample questions below to get the best experience.

1. Concept

1. What is the problem you are trying to solve?
2. What is the solution you want to build?
3. How big is the impact on the business of that project? Who is it for, and will benefit most?

2. Data

1. What internal and external Data Sources do you need?
2. What databases, tables you want to use?
3. How will you connect to Data Sources?

3. Model

1. What is the type of the task (prediction, classification, ...)?
2. Which model architecture you want to use?
3. What will be the input/output of the model?

4. Metrics

1. How will you evaluate data correctness?
2. What metrics will you use for models, and how will you evaluate them?
3. What is your business KPI of success (Minimum Justifiable Improvement)?
4. How will you track progress?

5. Infrastructure

1. What tools and services do you want to use to perform operations on data, models, and code?
2. Where will you deploy the whole system - on cloud or on-prem?

6. Pipeline

1. How will you connect all services, and what will be the workflow?
2. Which parts and how will be automated?
3. Will you run a model in the batch or real-time?

7. Maintenance

1. How will you take care of Versioning, Reproducibility, Testing, and Monitoring of Data, Model, and Code?

8. Team

1. Who will be the end-user of the product?
2. Who will benefit from the product?
3. How customer will benefit from the outcome?
4. Who is needed to perform the Build & Deploy phase?

9. Costs

1. What we have to pay for?
2. How much do we have to pay?
3. What is the expected revenue, or non-financial added value?
4. What is the problem you are trying to solve?

Summing up

AI is a great technology that empowers more and more companies. However, it important to be aware that AI-powered projects differ from traditional software engineering projects.

Introduced AI Project Canvas aims to help grasp a big picture of the project in a fast and easy way. We highly encourage you to use a template before each AI project so that you will have a general overview of all potential difficulties and weaknesses that might occur during sprints.