Webinar: Why AI projects
often fail in Asset
Management &
how to fix it
AI is often positioned as the answer to every reliability and maintenance challenge. Yet across manufacturing many AI initiatives fail to deliver lasting value or scale beyond early trials.
This webinar challenges the idea of AI as a silver bullet for asset management and explains why success depends far more on organisational readiness than algorithms alone, highlighting critical factors that prevent organisations from jumping from reactive maintenance straight to advanced AI prediction without the right foundations in place.
Make AI powered Asset maintenance work for you
This webinar goes beyond theory, sharing practical strategies and real industrial lessons on why AI projects fail and how to fix them, helping you build the right foundations before scaling predictive analytics and Industrial AI.
Ideal for leaders in maintenance, reliability and digital transformation who want AI to deliver real operational value rather than stalled pilots or costly complexity.
What you’ll learn
From overcoming the maturity trap and escaping Pilot Purgatory, to establishing the right data foundations and cutting through costly complexity, this webinar gives you the practical tools to move AI from stalled pilots to real operational impact across hundreds of assets.
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About the Speaker
Cranford Johnstone
Cranford is a reliability and condition monitoring specialist with more than 30 years of experience helping organisations improve asset performance and maintenance strategy. He specialises in predictive maintenance, digital technologies and AI-driven insights that transform industrial data into practical actions, enabling smarter decisions, improved reliability and more efficient operations.
David Hoven
David is an experienced industrial digital advisor working across sectors including chemicals, life sciences, food and beverage, and manufacturing. He joined OSIsoft eight years ago, which later became part of AVEVA. His work focuses on supporting organisations with modern data use cases including cloud platforms and industrial AI.
