Get inspired - Use cases for your AI initiative

There’s so much buzz around AI but still very little understanding. It’s easy to get overwhelmed when thinking of your first project. You might be wondering: Where can I implement AI? Which use case should I start with? That’s understandable. One of the easiest ways for demystifying AI is to see examples of how you can use it to amplify your day-to-day work. This blog post provides you with some resources and things to consider before starting your first AI initiative. 

Get inspired

Try to think of a business problem that you’re facing and look at some of the existing use cases to get a practical understanding of what can be achieved; Dataiku’s Customer Stories or recent submissions for Dataiku Frontrunner Awards have plenty of examples of impactful solutions.

Have a quick reality check 

Creating predictive models requires a lot of relevant, clean data. Think of your data sources. What do they contain? Could they be helpful for the task? Is there data missing and would need to be collected specifically for the new project? How much data is there? While it’s difficult to say how much data you exactly need, more data usually improves your model’s predictive power.

Consider the time to value ratio

If you’re beginning to work with Dataiku and planning to develop the project yourself, focus on creating one quick, easy prototype to get a gist and iterating on it later. Quick wins are crucial to building momentum. I’m suggesting three use cases that work for almost everyone in another blog post.

If you’re interested in advanced use cases, take a look at Dataiku’s Industry Solutions. They contain customisable templates that help reduce time to deployment to a minimum. You can mix and match different projects and quickly adjust them to use your data.