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Welcome to the final in our three-part SPARC (Short Podcasts by ARC) series with SAS, where we’ve been unpacking the complexities of adopting artificial intelligence (AI) in the industrial sector. In this series, I’ve been exploring Industrial AI challenges with Bryan Saunders, Global Director and Head of IoT Industry Consulting at SAS to share some key insights. In this final episode, we dive deeper into the practical applications of AI and analytics in the industrial sector, and I believe it's a must-listen for anyone looking to drive real value from their digital transformation efforts.
Watch or listen to Part 3.
Tune into our SPARCs Channel on YouTube for Parts 1 and 2, and future SPARCs.
SPARC SUMMARY
The Digital Divide is Real
As I discussed with Bryan, a significant digital divide is emerging. Companies that have already digitized their data and connected their sensors, machines, and factories are pulling ahead. Those who are behind on this journey risk being left behind. It's not just about having the technology; it's about how you use it to drive tangible business outcomes.
Use Cases That Deliver Real ROI
Bryan and I explore several use cases that are consistently delivering strong returns on investment. Here are some of the key areas:
Predictive Maintenance: This is often referred to as the "killer app" for IoT. It's not hard to see why when you consider the potential for reducing downtime and improving overall equipment effectiveness (OEE). Bryan mentions that Georgia Pacific achieved a 30 percent reduction in downtime and a 10 percent improvement in OEE by implementing predictive maintenance across their facilities.
Manufacturing Quality and Control: Improving production quality is a key driver for manufacturers. AI can help identify and address critical issues, leading to more efficient operations.
Energy Optimization: With rising energy costs, optimizing energy consumption is crucial. A large brick manufacturer, for example, achieved a 15 percent reduction in their natural gas bill by using analytics to identify areas of waste.
Worker Safety: By using cameras and other technologies to monitor workforce behavior, companies are seeing significant reductions in workplace injuries. Proactive training, based on this data, can lead to a 50 percent reduction in near misses and a 20 percent reduction in reportable incidents.
Heavy Utilities: AI is helping utilities with load forecasting, vegetation management, and predictive maintenance, and helping them manage the complexities of a shifting energy mix.
It's About Outcomes, Not Just Technology
One of the most crucial points Bryan emphasizes is that leading companies are focused on aligning their technology investments with their desired business outcomes. They are not just investing in technology for technology's sake but are instead focusing on how to operationalize and scale insights by tying them back into existing business systems. This ensures that employees can use the insights without learning entirely new systems.
The Transformative Role of Gen AI
In this final episode, Bryan and I also delve into the potential of Generative AI (Gen AI). While it’s not always about large language models, Gen AI is proving to be a transformative force. It is showing great promise by simplifying interactions with complex industrial systems and is becoming a "digital assistant" or "co-pilot" that can help upskill the workforce and transfer knowledge.
Synthetic Data Generation: Gen AI can generate synthetic data to overcome data scarcity, which helps in model development, tuning, and accuracy.
Digital Assistants: Gen AI is also making complex systems more user-friendly, with co-pilots that help subject matter experts navigate new technologies. It is helping companies address the skills gap that is prevalent across the industrial sector, especially in manufacturing, where there is significant workforce turnover.
Recap of the 3-Part SPARC Industrial AI Series with SAS
In this 3 part series, I’ve been exploring Industrial AI challenges with Bryan Saunders, Global Director and Head of IoT Industry Consulting at SAS.
The discussion emphasized the importance of managing data quality for improved decision-making and outcomes. It addresses 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. Bryan advised against a "big bang" approach to data fabric implementation and recommend focusing on specific use cases to realize value quickly.
The conversation moved to model development, where Bryan stressed that selecting the right tool for the job is essential. It was 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. Bryan also highlighted the need for more accessible tools that allow subject matter experts to contribute to model development.
Part 3: Deployment of Insights
The final podcast in the series 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.
Along the way, Bryan and I explored The Role of Gen AI, discussing the transformative potential of Gen AI, particularly in synthetic data generation, assisting with code creation, and as a digital assistant to upskill the workforce. We also discussed Gen AI as a way to address the skills gap and improve knowledge transfer in the industrial sector.
You can now LISTEN to the full-length audio podcast:
Learn More About ARC and SAS Views on the AI and Analytics Lifecycle
Download the ARC View titled "Industrial-Grade AI: Transforming Data into Insights and Outcomes”.
Follow Colin Masson and Bryan Saunders on LinkedIn for more insights and a transcript of today's conversation.
Interest SPARCed?
Thanks for tuning in to this SPARC! Share these episodes with others who are passionate about applying technology the right way to address skills gaps, create more intelligent processes, while building a more profitable and sustainable future. For those looking to go deeper, ARC Advisory Group offers longer format Digital Transformation and Sustainability Podcasts, and unparalleled guidance in developing digital transformation and sustainability strategies for industrial organizations. Reach out to us for expert insights and support that can help you turn these ideas into action and lead your organization toward a more profitable and sustainable path.
To contribute to SPARCs, or the Digital Transformation and Sustainability Podcasts, contact Jim Frazer and Colin Masson at ARC Advisory Group through your client manager.
For ARC Advisory Group recommendations for closing the digital divide by embracing Industrial AI, and governing and guiding major decisions about enterprise, cloud, industrial edge and AI software, please contact Colin Masson at [email protected].