These days it’s hard to do much of anything without artificial intelligence (AI) being mentioned. Personally, most of us have experienced AI in one way or another, while professionally, depending on your industry, things may be moving a bit slower. Many manufacturers are beginning to educate themselves about the possibilities AI can bring to their business, but little action has been taken to date. One thing is clear, realizing the full value of AI in manufacturing will require new, collaborative thinking and business models centered around gathering data at scale to generate network effects.

In an AI solution, three critical components stand out: data, algorithms, and subject matter expertise. Each is indispensable, as we require the expertise to discern crucial data, select appropriate tools for various scenarios, and possess subject matter knowledge to evaluate outcomes from AI applications.

Today’s session brings together leaders representing a team of semiconductor companies who are forging a new business model for AI-enabled smart manufacturing. It all starts with data sharing and aggregation across the participating companies.

1. If we do this right, the U.S. economy can benefit from manufacturing’s ability to innovate and seize leadership in AI and value creation. AI should be viewed as a necessary strategy that will lead to further investments to address industry-specific challenges.
2. Manufacturers need to get the data right to be successful with AI. Manufacturing systems are extremely data-rich but not all the data is being used, or not all “right” data is being collected. The industry is still asking critical questions about what data to get, where you get the data and what structure it needs to be in.
3. The companies involved in AI-enabled SM have found that the true value comes with scale. Implementing one-off AI solutions on the factory floor will bring sizeable benefits, but stakeholders are now coming together to envision the future of AI-enabled assets, operations, interoperability, and connected supply chains.
4. Manufacturing can learn from other industries that are successfully using AI. While use cases should be tailored, other industries have prioritized data and provided valuable services to their potential customers. Manufacturing has every opportunity to do this as well.
5. Interest is at an all-time high and the industry is ready for AI recommendations and guidance. It’s time for action! Get involved, find your use cases, get your data structured properly, identify the right AI tools and join the community working together to bring business value through AI-enabled manufacturing.