3 Key Pillars to Scaling AI Successfully

Imagine a steady stream of insights to fuel intelligent technologies, 360-degree customer views to boost relevance and revenue, or faster, smarter decisions to accelerate innovation and reduce costs. According to McKinsey, by building machine learning into processes, leading organizations are increasing process efficiency by 30% or more while also increasing revenues by 5%-10%. However, if AI models are low performing, lost track of and poorly managed, undocumented, and uncontrolled, it’s likely you will experience inefficiencies, missed opportunities, and risks across the value chain. This leads us to the following questions: -What separates AI high performers from the rest? -How can your organization best govern your AI projects to scale successfully? -What are the key drivers to operationalize AI effectively and in a way that’s reproducible? In this session, join Dataiku’s David Talaga (Product Marketing), Melanie Reversat (Product Management), and Jacob Beswick (Governance Solutions) as they discuss the key ingredients to achieve AI at scale and how Dataiku can enable that scale with speed and control. You’ll learn how to tackle the growing complexity of AI models and projects and control your AI models to enable (and not disable) your AI projects.