![]() If you discover these projects, hold them dearly and focus your efforts on making them work. These projects are both likely to succeed and have a major impact on business objectives. These projects can be helpful to start your AI Journey but are not relevant once your company reaches an advanced level of AI Maturity.ĪI Projects that are high-impact, low-risk, are the “Holy Grail” projects to search for. AI Projects can be clustered in four groups depending on their estimated level of impact and risk.ĪI Projects that are low-impact, low-risk fall into the “Solid Start” category. Impact defines how valuable the AI Project is, risk how likely it is to fail. The main dimensions to evaluate AI Projects are impact and risk. The insights are derived through discussions with industry-experts at Porsche AG, the Silicon Valley startup Xcalar, and my own experience. Depending on your position along the AI Maturity Journey, the trade-offs between impact and risks of AI Projects become more important. Every organization has limited resources which they need to allocate to maximize impact. Ai project canvas how to#This article explains how to approach evaluating and ranking different AI Projects. Where should you start your AI Journey? How do you rank different AI Projects? What AI Projects should you focus on depending on the level of AI Maturity of your business? The private equity sector invested more than 50bn $ in AI startups in the last six years. Companies are becoming increasingly aware of the benefits of AI. Ai project canvas full#With a sea full of Artificial Intelligence (AI) Project ideas, it is hard to figure out where to start fishing. 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.Īfter defining how to bring the project to the Customer, let’s finally explore the Financial requirements of your AI project.About the art of ranking and doubling-down on the next best AI Starship. 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. The right-most block is the second most important block after the Value Proposition. Key Stakeholders can be internal departments like legal, UX, management or even external stakeholders like contractors, owners, political or non-profit groups. Listing the Key Stakeholders will give you an overview of important decision makers. 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. Explain where and how the project will be used. They always have to be integrated into an existing architecture. The right part of the AI Project Canvas covers the integration of your project into the current infrastructure, for stakeholders and the customer.ĪI products rarely live in an isolated world, hardly ever in a Jupyter Notebook. that accuracy has to exceed 95% (key metric) while taking no longer than 1s inference time (sufficing metric).Īfter explaining the Ingredients part of your AI project, let’s talk about how you will bring your AI project to the Customer next. The output metric could be supplemented with a sufficing metric, e.g. Output metrics could be accuracy, f1-score, precision or recall, minutes spent using the service, etc. This helps you choose a good model in the first place and then to compare the performance of different models based on this metric. Andrew Ng recommends in his book Machine Learning Yearning chapter 8 to define a single-number evaluation metric before starting the project. The Output block shows the single key metric you’re evaluating on. 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? In the Skills block, you will define the expertise you need. 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? The better you can explain what data you need to create the value proposition, the better for your AI project. The Ingredients part consist of the Data, Skills, and Output blocks.ĭata is the main element that every AI project relies on. ![]()
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