The world of data has been forever changed by the rise of AI. In an industry evolving rapidly, with new tools emerging nonstop, it can feel almost impossible to keep up. But one quality remains essential to staying ahead: a commitment to curiosity and continuous learning.

In that spirit, we’re excited to introduce Data and AI Mastery, a brand-new podcast, where we’ll share inspiring lessons from leaders shaping the future of data and AI worldwide.

In one of our very first episodes, host Raoul-Gabriel Urma, founder of Cambridge Spark and a leader in transformational data and AI upskilling, welcomes Peter Laflin, Director of Data and Analytics at Morrisons. With over 20 years of experience, Peter has helped countless business leaders translate their visions into data-driven strategies and solutions.

Raoul and Peter dive deep into the many different ways AI is revolutionising business operations, with a particular focus on demand forecasting and supply chain optimisation as very powerful applications transforming retail.

As someone who’s been using AI tools long before the hype, Peter gives us the inside scoop on the real-world challenges and benefits of AI adoption.

He also offers expert tips for finding the sweet spot between theory and practice, always keeping the ultimate goal in sight: building customer-centric, valuable solutions that solve real problems.

As he wisely puts it, sometimes “good enough is good enough." So, how do you know that’s the case for your customer?

Tune into this episode to find out. You’ll get loads of insights on effectively deploying and optimising AI to deliver real customer value, so don’t miss out!

Chapters

[2:07] Introduction to Peter’s career journey in data and analytics, from early passion for numbers to becoming the Director of Data Analytics at Morrisons

[7:12] Leveraging AI to improve retail operations, specifically focusing on demand forecasting and supply chain optimisation

[11:40] The critical role of accurate demand forecasting in supply chain optimisation

[14:30] Navigating the trade-offs between data quality and model improvement

[17:52] Balancing between academic theory and real-world application in data science

[19:49] Applying theory to practice and solving real-world problems

[23:56] Using computer vision and AI to improve retail operations and tackle the biggest challenges

[27:00] Advice for fellow data scientists

[28:20] Quickfire questions

[28:55] Key takeaways from this episodes

Links

Raoul-Gabriel Urma LinkedIn

Peter Laflin LinkedIn

Morrisons Website