Omar Khawaja

AI and Machine Learning have become the go-to buzzwords in boardrooms, but not every data product needs a neural network to deliver value. This panel will explore examples of enterprise leaders separating genuine opportunity from costly overengineering, helping teams identify where AI/ML truly enhances functionality and adds value. Dive deeper into:

• Evaluating the functional and financial impact of AI/ML before implementation.
• Aligning executive enthusiasm with realistic, cost-effective AI strategies.
• Identifying high-impact use cases where AI/ML drives measurable product value.

Despite its growing importance, the term “data product” still lacks a universal definition across industries which creates confusion, misalignment, and missed opportunities. This session explores how enterprises can clarify the relationship between data products and business outcomes, and why internal roles like platform architects, product managers, and data engineers are essential to building and sustaining them. Gain a more in-depth understanding of:

• Establishing the definition of ‘data product’, cross-industry.
• Building cross-functional roles to support the full lifecycle of data products.
• Connecting data products to business strategy to maximise impact and relevance.