Paul is a data and AI practitioner focused on how machine learning and intelligent automation can reshape the way organisations make decisions. His work centres on moving analytics beyond static reporting toward systems that anticipate outcomes, adapt to change, and support better decisions at scale.

He has contributed to initiatives where forecasting, automation, and decision-support systems reduced operational friction, improved planning accuracy, and enabled teams to respond more effectively to real-world constraints. Paul is particularly interested in how AI can be embedded into everyday processes so that insights translate directly into action, rather than living in dashboards or reports.

Drawing from experience across analytics, engineering, and system design, Paul approaches AI adoption with a strong emphasis on practicality and sustainability. He highlights the importance of building machine learning solutions that can operate reliably in production, evolve over time, and align with organisational realities rather than experimental ideals.

As a speaker, Paul shares grounded perspectives on applied AI and machine learning, focusing on lessons learned, architectural thinking, and the cultural and operational shifts required to make AI successful. His goal is to help audiences cut through hype and understand what meaningful, responsible AI impact looks like in practice.