Vice President Network and Edge Group, General Manager Federal & Industrial Solutions at Intel Corporation.
Not even a year ago, AI—particularly at the edge—was the hot topic in manufacturing. Now, with all the hype around generative AI (GenAI), it feels a bit like AI just got kicked to the curb. GenAI does hold the potential to disrupt and reshape the industrial landscape. However, manufacturers haven’t even tapped into the full benefits of deploying AI solutions yet. So, let’s look beyond the hype and see how GenAI fits into the overall manufacturing picture today.
We are still in the early phases of deployment, but we already see business value in advanced automation, optimization and innovation. Companies already use GenAI to improve efficiency and productivity in their overall business processes. They are streamlining design and prototyping activities, reducing downtime through smarter predictive maintenance, improving supply chain management and working with advanced analytics for decision-making.
AI and edge devices have modernized industrial operations, and they are expected to remain the most common digital technologies in manufacturing.
AI Versus GenAI Debate
The ongoing debate centers around the distinction between these technologies. AI primarily analyzes existing data patterns to make predictions and perform specific tasks. For example, AI algorithms can identify manufacturing anomalies, predict machinery maintenance needs and optimize processes based on historical data.
In contrast, GenAI is characterized by its ability to create new content based on learned patterns and user inputs, such as text, images or even entire simulations. It goes beyond AI’s predictive capabilities by generating novel outputs tailored to specific needs or requests.
While AI remains prevalent in many industries, including manufacturing, GenAI offers unique opportunities for innovation and creativity. The debate between AI and GenAI is not about choosing one over the other; instead, it’s about understanding their respective strengths and applications. In manufacturing, for instance, AI excels in tasks like predictive maintenance, while GenAI can be used for designing new products, optimizing processes and even simulating potential scenarios.
Ultimately, the synergy between AI and GenAI can lead to enhanced productivity, innovation and problem-solving capabilities across various industries. Therefore, rather than viewing them as competing technologies, it’s essential to recognize their complementary roles in driving progress and achieving business objectives.
Benefits To Manufacturers
Introducing digital infrastructure that can handle GenAI and other digital technologies doesn’t require reconfiguring a manufacturing facility from the ground up. It’s not a greenfield-only proposition. It’s only a matter of time before the technology resolves the challenges of incorporating legacy systems and data into modern technology ecosystems. Interoperability challenges will be a thing of the past.
There are greater opportunities for a manufacturer to customize operations defined by its needs. Current large language models are trained on expansive data sets. With GenAI, smaller language models can be customized on data sets from a manufacturer’s specific industrial IoT (IIoT) devices. This allows older equipment not using open-source AI tools to exchange data with GenAI systems. Additionally, these smaller language models can be fine-tuned and work closer to the edge, where latency and security are important to IIoT solutions.
GenAI introduces “what if” scenarios to digital twins. AI has allowed manufacturing enterprises to create digital twins—virtual copies of a physical system. These static digital twins can accurately replicate a real-world system to allow for tests on how individual reconfigurations or changes might affect the system’s operation.
GenAI now allows for much more dynamic use and experimentation with digital twins, as it can simulate a wide range of alternate scenarios to determine how more efficient process paths, different energy levels, safety improvements and other factors might change daily operations.
Immediate Value
Manufacturing comprises a wide variety of use cases, ranging from the production of oil and gas to 3D printing of biocompatible human joint replacement parts and even aerospace applications. However, here are the “Big Three” use cases to think about today.
• Coding Programmable Logic Controllers (PLCs): According to McKinsey, AI’s direct impact on software engineering productivity could range from 20% to 45% of current annual spending, thanks to reduced time spent on generating initial code drafts, correction and refactoring, root-cause analysis and other activities. For manufacturers, AI could reduce time spent coding PLCs, which are the fundamental controls of machinery around the globe.
• Using Natural Language With Robotics: The value of robotics is twofold. Besides the expanded use of digital twins to model robotics design and behaviors, the use of natural language prompts inherent in GenAI allows the average machine operator, who is not necessarily a robotic expert, to interact with the machine using everyday language. Additionally, GenAI gives these operators the power to adjust processes to meet challenges as they arise without being expert software coders.
• Optimizing Processes Before They Reach The Factory Floor: Imagine knowing everything about a new product before it comes off the assembly line. GenAI lets designers model characteristics and behaviors of possible materials, configure and reconfigure assembly lines and evaluate bottlenecks or resource waste, all in the design phase. Additionally, data and lessons learned on one product inform future product designs, shortening the time between ideation and production.
The Future
GenAI is evolving rapidly, and we are only scratching the surface of its potential uses in manufacturing. Today, the technology can help to code PLCs, enhance the usability of robotic equipment and help factories optimize a range of processes. In the future, it will be able to create adaptive factory systems to address changing consumer demands, shifts in supply chains and regulatory reporting.
We have seen AI’s transformative power and importance in manufacturing. GenAI is another step forward, and those who don’t keep up risk getting left behind. It isn’t a required technology, but it will be a vital tool to help you remain competitive and ready for the future.
Keeping up with GenAI advancements can be challenging. Best practices and additional research will continue to emerge. Your job will be to consider how the technology impacts those use cases specific to your organization that deliver value today.
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