ABOUT THE TALK
At the foundation of AI project failures lies a critical gap between data teams and business reality. On top of this gap, data quality issues, unexpected privacy concerns, and tools that don't align with actual business problems arise to hinder or block implementation. As we've built our own AI product—AI Decisioning—and implemented it with customers, we've learned that successful AI implementations depend on embedding data teams within business units. Embedding doesn't mean breaking apart your data team and dispersing it throughout every other department. It means establishing focused partnerships where data team members are deeply integrated into business teams' daily workflows and decision-making processes while remaining connected to the central data organization. This embedding creates a virtuous cycle: data teams gain deep domain knowledge, business professionals see improved data quality and gain data facility, and together, data and business teams implement AI solutions that solve real problems. In this talk, we'll share concrete examples of how data teams (especially data scientists) and marketing have worked together in successful AI Decisioning implementations. We’ll derive strategies to implement this organizational pattern and enable a company to move from analytics to actions and from data teams as service providers to active collaborators. While our case studies focus primarily on marketing partnerships, the embedded partnership model we present applies equally to other business functions including product development, operations, and customer service teams.
