Executive Summary
As original equipment manufacturers (OEMs) race to digitize their offerings and deliver higher-value services, the integration of Internet of Things (IoT) data into artificial intelligence (AI) and machine learning (ML) systems is no longer optional — it’s strategic. This whitepaper explores the indispensable role of IoT data in powering AI/ML capabilities, how OEMs can harness this data to transform operations and how Scante provides a scalable, secure, and OEM-branded platform to operationalize these initiatives.
IoT for AI and Machine Learning Key Points
- Why AI Needs IoT
- AI Is Useless Without Context
- The Feeder Pipe for AI: Long-Term IoT Data
- The Role of Structured Diagnostics in Predictive AI
- From Connectivity to Intelligence
- The OEM Imperative: Why IoT Data is Critical for AI/ML
- OEM Value Chain Transformation with IoT and AI Diagram
- Case Study: Global Finishing Solutions (Predictive Maintenance in the Field)
- The Data Challenge: What Holds OEMs Back?
- AI Use Cases Fueled by IoT Data
- A Roadmap for OEMs
- Layering AI on Top of Your Existing IoT Stack
- Practical Applications of AI and IoT for OEMs
- Data-Driven Differentiation for the Next Decade
Data-Driven Differentiation for the Next Decade
The future of OEM success lies not just in building great equipment but in surrounding it with
intelligence. IoT data is the raw material; AI is the refinery; Scante is the platform that turns
this into a marketable advantage.
OEM executives must act now to build data strategies that will underpin competitive differentiation for the next decade.
The companies that win won’t just build better machines. They’ll build better insights – and better customer experiences – powered by data. – Jon Prescott, CEO of Scante
