Making AI Work: Aligning Data, Teams, and Business Goals
AI adoption often stalls not due to a lack of tools, but because of what I call “frozen yoghurt syndrome”—an overwhelming number of choices that leads to confusion, misalignment, and stalled progress. This session offers a pragmatic framework for building a scalable data strategy that cuts through the noise and focuses on delivering business value. We’ll explore four key pillars: - Identify the Problem and Bigger Picture – Start with the business need. AI isn’t always the right answer; sometimes automation or simpler analytics are more effective. - Design the Strategy Around Data, AI, and the Use Case Together – Align initiatives with clear business goals and define success metrics that matter to both technical and non-technical teams. - Build the Foundation – Encourage cross-functional collaboration, select scalable tools, and document everything from data sources to model assumptions. - Create a Positive Feedback Loop Between Tech and Non-Tech Teams – Foster a shared product language, enable self-service access to data, and build trust through transparency and iteration. Attendees will leave with practical frameworks to ensure AI efforts are not just technically sound — but strategically aligned, collaborative, and sustainable.
