AI Projects: Lifecycle and Best Practices

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Presented by

Vincent De Stoecklin, Customer Success Director, APAC

About this talk

As companies around the world look to get a jump on AI efforts, there’s one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects? Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this webinar, we’ll share a comprehensive framework for: 1) Choosing relevant projects: defining needs from business lines and cost/benefit analysis. 2) Advanced project scoping: aligning stakeholders, anticipating key steps, defining success metrics. 3) Operationalization of AI projects: challenges and best practices. In this session, we will walk you through the methodology we use at Dataiku to guide and accompany our customers in defining and executing a roadmap of high-value AI projects.
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Dataiku is the world’s leading platform for Everyday AI, systemizing the use of data for exceptional business results. Organizations that use Dataiku elevate their people (whether technical and working in code or on the business side and low- or no-code) to extraordinary, arming them with the ability to make better day-to-day decisions with data. More than 450 companies worldwide use Dataiku to systemize their use of data and AI, driving diverse use cases from fraud detection to customer churn prevention, predictive maintenance to supply chain optimization, and everything in between.