
AI Maturity: Defining, Measuring, and Prioritizing Enterprise Initiatives
Data and analytics leaders share AI maturity frameworks and challenges in measuring the impact and progress of AI efforts for both data consumers and producers.
In depth presentations and panel discussions from IIA leaders, clients and experts on concepts, topics and trends essential for analytics and AI success.
Data and analytics leaders share AI maturity frameworks and challenges in measuring the impact and progress of AI efforts for both data consumers and producers.
Data and analytics leaders will share best practices in designing and delivering internal analytics conferences to enhance internal knowledge sharing, boost data literacy, and align with organizational goals.
Data and analytics leaders will discuss their approach to change management as they navigate the promise—and hype—of enterprise AI.
Data and analytics leaders will discuss how AI and automation are reshaping roles, impacting job responsibilities, and redefining what work is carried out by humans versus machines.
Explore innovative strategies for assembling and guiding top-tier AI teams that drive your organization forward in the digital age. Key discussion questions include: What are the key characteristics to look…
In this roundtable discussion, data and analytics leaders will explore the interplay between automation and personalization in customer analytics. This session aims to equip data and analytics leaders with strategies…
In this roundtable discussion, data and analytics leaders discuss how they’re currently leveraging AI within their existing data strategy and the challenges they face with this integration. Discussion questions include:…
This roundtable discussion is an extension of Kathleen Maley’s April session on analytics engagement models. This conversation among data and analytics leaders will explore how to effectively identify potential consultants…
As others wade through the AI hype, Eric Siegel reveals the bizML framework, a six-step practice designed to take machine learning projects from conception to deployment.