Industrial AI Conversation with SAS: Transforming Data into Outcomes

Digital Transformation Viewpoints

In this episode, Colin Masson, Research Director for Industrial AI at ARC Advisory Group, and Bryan Saunders, Global Director and Head of IoT Industry Consulting at SAS, delve into the critical aspects of the AI and analytics lifecycle in the industrial sector. They explore key areas, including:

• Data Management: The discussion emphasizes the importance of managing data quality for improved decision-making and outcomes. They address the challenges of dealing with diverse data types, including sensor data, text, video, and acoustic data, highlighting the need for a flexible "any data, anywhere" approach. The experts advise against a "big bang" approach to data fabric implementation and recommend focusing on specific use cases to realize value quickly.
• Model Development: The conversation moves to model development, where they stress that selecting the right tool for the job is essential. It is noted that most AI deployments in the industrial sector are not using Gen AI, but more traditional, narrow AI techniques and algorithms, partly due to cost and explainability. They also highlight the need for more accessible tools that allow subject matter experts to contribute to model development.
• Deployment of Insights: The podcast explores how to operationalize and scale insights by tying analytic investments into existing business systems. The discussion highlights common use cases, like predictive maintenance, quality control, energy optimization and worker safety and cites examples of companies like Georgia Pacific and Lockheed Martin.

The Role of Gen AI: During their conversation, Colin and Bryan also discuss the transformative potential of Gen AI, particularly in synthetic data generation, assisting with code creation, and as a digital assistant to upskill the workforce. Gen AI is also seen as a way to address the skills gap and improve knowledge transfer in the industrial sector.

This episode offers a pragmatic view of AI in the industrial sector, emphasizing the need to focus on business outcomes, use cases, and the importance of data governance. It provides valuable insights for those looking to leverage AI and analytics effectively in their operations.