Leveraging AI for Business Transformation

Glyn began his career within the recruitment sector and has led the scale-up of several successful businesses. This includes his role as MD and Co-founder of Northstar, which later sold to The Access Group and, most recently, COO of Amdaris, an international tech solutions business, which scaled to 1,000 employees and recently sold to the Fortune 500-listed tech giant, Insight.
 
As COO at La Fosse, Glyn focuses primarily on La Fosse’s operational strategy, efficiency improvement, and ensuring the development and progression of numerous operational teams.

With over 15 years of experience in leading private, public, and third sector organisations through significant periods of change, Natalie has spent the last five years leading a data and AI consultancy. She takes an active role in the community, chairing a cross-industry data ethics advisory board, chairing Women in Data‘s Women’s Health initiative, and as a member of the Mayor’s Data for London Advisory Board.

Daniel is a globally recognised AI expert with over 25 years of experience in applied AI. Regarded as one of the top ten Chief AI Officers in the world, he is the CAIO at WPP, CEO at Satalia, faculty member at SingularityU, and Entrepreneur-in-residence at UCL.

Daniel is a serial TEDx and prolific keynote speaker. He is passionate about how technology helps govern organisations and can make a positive social impact.

John has a 30-year operator and advisory career focused in the resources, energy, water utilities, and industrials sector. He has successfully delivered multiple studies and transformations through an integrated operations performance improvement approach enabled by the application of digital technology.

John’s digital experience lies in the strategy, design, and implementation of I4.0 technologies including Industrial Internet of Things, Digital Twin, Applied AI/Machine Learning, AR/VR, Additive Manufacturing, Smart Connected Devices, and Computer Vision.

John has led data science teams and clients through exploratory data analysis, target and value definition, model development and deployment, and the closed-loop operations phase of the applied machine learning and AI cycle.