The AI Project Canvas consists of four distinct parts: the Value Proposition as the central part of your project, the Ingredients on the left, the integration for Customers on the right, and Financing on the bottom. All these parts are vital aspects of any AI project. Let’s go through each part and start with the Value Proposition.

The heart of the AI Project Canvas is the Value Proposition. It explains the value that the project will add to your organization. A value-add can be a new AI-powered product to generate revenue, or improving an existing process to cut costs. What customer pain is the AI project solving? Which vitamins are you adding to enhance your customer’s life? Ideally, you can describe the Value Proposition in one concise bullet point. Listing many Value Propositions risks watering down the impact or failing to focus on the most important one.

Side note: Trying out a new paper because it sounds cool will not get you far. What is the value-add of your AI project? Sufficiently answering this question help you focus on the task at hand and get you halfway towards securing funding for your AI project. Let’s look at the Ingredients block next.

The Ingredients part consist of the Data, Skills, and Output blocks.

Data is the main element that every AI project relies on. The better you can explain what data you need to create the value proposition, the better for your AI project. How much data do you need? Do you have already a prepared dataset or do you need to source it? Does it have to be labeled? What data format are you expecting?

In the Skills block, you will define the expertise you need. Is it a computer vision or natural language understanding task? Do you need Data Engineers to help you write efficient software? Maybe even a Product Manager and a UX Designer to gather customer requirements and to design a workflow?

The Output block shows the single key metric you’re evaluating on. Andrew Ng recommends in his book Machine Learning Yearning chapter 8 to define a single-number evaluation metric before starting the project. This helps you choose a good model in the first place and then to compare the performance of different models based on this metric. Output metrics could be accuracy, f1-score, precision or recall, minutes spent using the service, etc. The output metric could be supplemented with a sufficing metric, e.g. that accuracy has to exceed 95% (key metric) while taking no longer than 1s inference time (sufficing metric).

After explaining the Ingredients part of your AI project, let’s talk about how you will bring your AI project to the Customer next.

The right part of the AI Project Canvas covers the integration of your project into the current infrastructure, for stakeholders and the customer.

AI products rarely live in an isolated world, hardly ever in a Jupyter Notebook. They always have to be integrated into an existing architecture. Explain where and how the project will be used. Where does it fit into the backend? How will the customer engage with your model? Will you use a microservice, monolith, or predict on-the-fly during streaming? Answering these questions will make it clear how the project will be brought into production.

Listing the Key Stakeholders will give you an overview of important decision makers. Key Stakeholders can be internal departments like legal, UX, management or even external stakeholders like contractors, owners, political or non-profit groups.

The right-most block is the second most important block after the Value Proposition. Who is the Customer that you are designing the project for? Too often, Data Scientists fall in love with technical details of their model but lose track of who they are developing the model for. Does the customer really care about an accuracy improvement from 99.2% to 99.3% or would faster inference time suit them better? Write in detail about your different customer groups to guide your decision-making throughout the process.

After defining how to bring the project to the Customer, let’s finally explore the Financial requirements of your AI project.

Any project needs to have a sound financial footing. Answering questions about Costs and Revenue will put you in a strong position to explain why your AI project should be funded.

The Cost block details which costs will occur. Do you need to outsource labeling? Will you incur compute costs? Don’t forget to mention the time or salary spent from your team members. You don’t necessarily have to write down absolute costs, giving an overview over cost categories is enough.

Lastly, the Revenue block shows how your AI project will enable the company to get new income. Will the product be sold as a service or as a new feature category for users? Will the project reduce internal costs through automating processes or support an innovation initiative? Mentioning the type of revenue is an important part of any AI project.

To summarize the AI Project Canvas, look at the circular dependencies in the image above. You will incur costs to wrangle data with key skills to arrive at the target output. With this model, you will propose new value to the organization. You then incorporate the product into the existing architecture while communicating with stakeholders. The customer will benefit from your project, thus generating revenue for your company.

Enough theory. After explaining the theory behind the AI Project Canvas, let’s look at a real-world example next.