Introducing – The AI Project Canvas
The AI Project Canvas consists of four distinct sections: 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 section, starting with the Value Proposition.
The main section of your AI project canvas is the Value proposition.
Value Proposition
In the Value Proposition section, you define the benefits that the AI project will bring to the organisation or end-users. This includes improvements in efficiency, cost savings, enhanced decision-making capabilities, or new capabilities using an AI-powered solution. The aim is to clearly articulate why the project is worth pursuing and what specific customer problems it will solve. Ideally, the valuable proposition should be described as crisply as possible. Look to address questions such as
- What customer pain is the AI project solving?
- What are the key benefits customers will receive?
Data
The foundation of any AI project is its data. This section focuses on identifying the types of data needed, their sources, how they will be collected, and any preprocessing requirements. The purpose is to ensure that the project has access to high-quality, relevant data in sufficient quantity to train and validate the AI models effectively. Look to address questions such as
- How much data do you need?
- Do you already have a prepared dataset, or do you need to source it?
- Does it have to be labelled? What data format are you expecting?
Skills
AI projects require a variety of skills, ranging from data science and machine learning expertise to domain knowledge and project management. In this section, you identify the skills and expertise needed to successfully execute the project, determine the current capabilities of your team, and plan for any hiring or training requirements.
- Is it a computer vision or natural language understanding task?
- Do you need Data Engineers to help you write efficient software?
- Do you need Product Managers and a UX Designer to gather customer requirements and to design a workflow?
Output
The Output section details the expected results of the AI project. Identify the single key metric you’re evaluating your project on. This helps you choose a good AI model in the first place and then to compare the performance of different models based on this metric. Output AI metrics typically include accuracy, f1-score, precision or recall, minutes spent using the service, etc. See the post to learn more about model metrics.
Integration
Integration is about planning how the AI solution will fit into the existing systems and processes. This includes technical integration with IT infrastructure, alignment with business processes, and ensuring compatibility with other tools and platforms. The purpose of this section is to ensure a smooth deployment and operation of the AI solution within the organization.
- 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?
Stakeholders
Identifying and engaging with stakeholders is crucial for the success of an AI project. This section involves mapping out who the stakeholders are, their interests, and how they will be affected by the project. The goal is to ensure that the project has the necessary support, input, and buy-in from all relevant parties.
Customers
In the Customers section, you define who will benefit from or use the AI solution. Understanding the customers’ needs, preferences, and pain points is essential for designing an AI solution that delivers real value and ensures user adoption.
Cost
Planning the financial aspects of an AI project is addressed in the Cost section. This includes estimating the budget required for development, deployment, and ongoing maintenance of the AI solution. It’s important to account for all potential expenses to ensure the project is financially viable.
- Do you need to outsource labelling?
- Will you incur compute costs?
- Don’t forget to mention the time or salary spent from your team members.
Revenue
Finally, the Revenue section is about projecting the financial returns from the AI project. This could be through direct income generation, cost savings, efficiency improvements, or other value-added benefits. The purpose is to demonstrate the economic feasibility and potential return on investment (ROI) of the project.
Conclusion
The AI Project Canvas serves as a roadmap for planning AI projects, helping to structure the approach and ensure that all critical factors are considered. By systematically addressing each section, from data to revenue, you can create a comprehensive plan that maximizes the chances of the project’s success and ensures its alignment with the business objectives.
To download the AI Project Canvas templates follow these links : Google Docs Template or Notion Template
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