In March of 2021, Walmart announced a plan to invest $350 billion over 10 years in products grown, made, or assembled in the U.S. The retailer’s goal: to create 750,000 U.S.-based jobs, the lion’s share in the manufacturing sector. If only there were 750,000 people eager to take those jobs.

For decades now, the U.S. has experienced a chronic shortage of skilled labor, a situation made more acute by the COVID-19 pandemic. Simply put: Nobody wants to work in a factory.

Over the past two years, many things have changed for manufacturers, mostly for the worse. They’re scrambling to deal with the labor shortage, along with other urgent issues such as disruption of the supply chain (from sheet metal to computer chips), unpredictable consumer demand, and pressures to reduce waste. A series of concurrent factors have greatly limited the number of people on the factory floor, while job openings remain at a record high.

Take the example of visually inspecting the quality of production. Statistics show that globally, this task will see a net decrease of tens of thousands of human workers by 2030. How confident are we that millions of Americans are looking forward to performing these repetitive tasks, especially when alternatives are available on the job market?

Given that throwing more workers at the problem isn’t an option, today’s manufacturers are looking at artificial intelligence and automation. In the last two years, the industrial internet of things (IIoT) has become commonplace. With dozens of inexpensive sensors gathering data, manufacturers are working to embed human-level AI that can extract insights directly on the production floor, enabling systems to detect good versus bad production cycles based on machine data as it varies across runs.

Today’s AI applications are available in many consumer and enterprise realms. They’re heightening the quality of smartphone pictures, deleting spam messages, recognizing faces, translating languages, optimizing sales engagements, and making video games more appealing. As a result, one might assume that AI can be easily applied to industrial manufacturing.

This is unfortunately not the case.

While much progress has been made in making AI more understandable and easier to use, it’s still the province of experts. Many nuances are involved in turning a proof of concept into a real-world deployment. There simply aren’t enough AI PhDs in the world, and it still takes four years of hard study to get that degree.

The state of AI in manufacturing today has a remarkable resemblance to what we experienced in the evolution of the internet from 30 years ago, when the World Wide Web was invented. Barriers to entry are still severely limiting AI’s usage in manufacturing, chief among them the need to make it accessible to a wide audience of non-experts.

When I built my first website in the 1990s, I had to learn how to write Hypertext Markup Language, or HTML. As the internet gained millions of users, new tools made the process less source-code dependent. Fast forward to 2000, when web design became the province of literally anybody with enough skills to point and click with a mouse. Then, around 2003, came WordPress, which thanks to a community of users devoted to open-source computing dramatically lowered the barrier to website creation and updating, eliminating the need for code-writing expertise and hours of work.

AI adoption in manufacturing today is in a similar situation, with only a fraction of companies and system integrators being AI-enabled or fluent. An estimated 30% of businesses are planning to incorporate AI into their operations within the next few years, and 91% of those responding to a recent study foresee significant barriers and roadblocks for AI adoption. Among the main obstacles is the paucity of AI talent to guide the transition.

So, looking ahead to the new year, what’s in store for the world of manufacturing? Here are my predictions for 2022:

• In order to unleash the AI revolution and lower the barrier to adoption worldwide, we must understand that we don’t need a new coding language, but a true “no-AI-expertise-required” tool that will be the basis for unleashing the AI revolution.
• We’re entering the era of “WordPress for manufacturing AI.” Software platforms are finally emerging that simplify a complex problem, providing integration hooks, hardware flexibility, ease of use, the ability to work with little data and, crucially, a low-cost entry point to make the technology viable for manufacturers.
• We’ll continue to see the acceleration of AI adoption in manufacturing — from predictive maintenance to product-quality assurance, demand forecasting and inventory control — while facing the challenge of workforce scarcity, with or without a pandemic.
• Real change will happen when AI access finally becomes a low- or no-code endeavor for manufacturers. Similar to WordPress and other common applications that are becoming platformized, we’ll take a step toward a manufacturing economy that can withstand upheaval and re-emerge in the most challenging times.
• AI in manufacturing is coming — powered, this time, by a “WordPress for AI.”

Max Versace is co-founder and chief executive officer of vision AI company Neurala.

In March of 2021, Walmart announced a plan to invest $350 billion over 10 years in products grown, made, or assembled in the U.S. The retailer’s goal: to create 750,000 U.S.-based jobs, the lion’s share in the manufacturing sector. If only there were 750,000 people eager to take those jobs.

For decades now, the U.S. has experienced a chronic shortage of skilled labor, a situation made more acute by the COVID-19 pandemic. Simply put: Nobody wants to work in a factory.

Over the past two years, many things have changed for manufacturers, mostly for the worse. They’re scrambling to deal with the labor shortage, along with other urgent issues such as disruption of the supply chain (from sheet metal to computer chips), unpredictable consumer demand, and pressures to reduce waste. A series of concurrent factors have greatly limited the number of people on the factory floor, while job openings remain at a record high.

Take the example of visually inspecting the quality of production. Statistics show that globally, this task will see a net decrease of tens of thousands of human workers by 2030. How confident are we that millions of Americans are looking forward to performing these repetitive tasks, especially when alternatives are available on the job market?

Given that throwing more workers at the problem isn’t an option, today’s manufacturers are looking at artificial intelligence and automation. In the last two years, the industrial internet of things (IIoT) has become commonplace. With dozens of inexpensive sensors gathering data, manufacturers are working to embed human-level AI that can extract insights directly on the production floor, enabling systems to detect good versus bad production cycles based on machine data as it varies across runs.

Today’s AI applications are available in many consumer and enterprise realms. They’re heightening the quality of smartphone pictures, deleting spam messages, recognizing faces, translating languages, optimizing sales engagements, and making video games more appealing. As a result, one might assume that AI can be easily applied to industrial manufacturing.

This is unfortunately not the case.

While much progress has been made in making AI more understandable and easier to use, it’s still the province of experts. Many nuances are involved in turning a proof of concept into a real-world deployment. There simply aren’t enough AI PhDs in the world, and it still takes four years of hard study to get that degree.

The state of AI in manufacturing today has a remarkable resemblance to what we experienced in the evolution of the internet from 30 years ago, when the World Wide Web was invented. Barriers to entry are still severely limiting AI’s usage in manufacturing, chief among them the need to make it accessible to a wide audience of non-experts.

When I built my first website in the 1990s, I had to learn how to write Hypertext Markup Language, or HTML. As the internet gained millions of users, new tools made the process less source-code dependent. Fast forward to 2000, when web design became the province of literally anybody with enough skills to point and click with a mouse. Then, around 2003, came WordPress, which thanks to a community of users devoted to open-source computing dramatically lowered the barrier to website creation and updating, eliminating the need for code-writing expertise and hours of work.

AI adoption in manufacturing today is in a similar situation, with only a fraction of companies and system integrators being AI-enabled or fluent. An estimated 30% of businesses are planning to incorporate AI into their operations within the next few years, and 91% of those responding to a recent study foresee significant barriers and roadblocks for AI adoption. Among the main obstacles is the paucity of AI talent to guide the transition.

So, looking ahead to the new year, what’s in store for the world of manufacturing? Here are my predictions for 2022:

• In order to unleash the AI revolution and lower the barrier to adoption worldwide, we must understand that we don’t need a new coding language, but a true “no-AI-expertise-required” tool that will be the basis for unleashing the AI revolution.
• We’re entering the era of “WordPress for manufacturing AI.” Software platforms are finally emerging that simplify a complex problem, providing integration hooks, hardware flexibility, ease of use, the ability to work with little data and, crucially, a low-cost entry point to make the technology viable for manufacturers.
• We’ll continue to see the acceleration of AI adoption in manufacturing — from predictive maintenance to product-quality assurance, demand forecasting and inventory control — while facing the challenge of workforce scarcity, with or without a pandemic.
• Real change will happen when AI access finally becomes a low- or no-code endeavor for manufacturers. Similar to WordPress and other common applications that are becoming platformized, we’ll take a step toward a manufacturing economy that can withstand upheaval and re-emerge in the most challenging times.
• AI in manufacturing is coming — powered, this time, by a “WordPress for AI.”

Max Versace is co-founder and chief executive officer of vision AI company Neurala.