Large companies can benefit tremendously from a solid AI implementation, but smaller companies can make a difference more quickly.
This scene plays out every day in American manufacturing: In a massive factory, the hum of machinery creates a metallic symphony that can only be produced with the tools of advanced industrial progress. But amid the whir of automated systems, cutting-edge robots, and small autonomous vehicles gliding across the floor to their destinations, a surprising scene unfolds: A worker stands in the middle of the shop floor, busily jotting down real-time production data on a whiteboard.
This paradox – a high-tech factory still reliant on humanity’s oldest method of recording data – illustrates a larger problem within the manufacturing sector. Despite significant investments in modern software, machinery and automation, many factories are lagging behind in digitization and missing out on the efficiency gains that digital transformation and the latest AI technology could offer.
Alex Sandoval, founder and CEO of Allie Systems, a Mexico-based AI developer for manufacturing, has seen this scenario many times. His company specializes in developing autonomous AI agents for manufacturing and aims to redefine how large-scale manufacturers interact with the latest technology. The mission is to bridge the gap between high-tech machines and the outdated data practices that are still used, even in many factories that would rightly be considered smart manufacturing facilities.
Decoding AI in Manufacturing
Allie’s AI approach is to add another dose of intelligence to this already intelligent manufacturing environment.
“Manufacturing involves a lot of machines and computers that generate huge amounts of data,” Sandoval explains. “Our job is to tap into this data, which is often underutilized, and turn it into actionable insights.”
Allie AI connects to the various machines in a manufacturing plant and collects data on production quality, process variables, and machine health. This data is then used to train an AI agent—essentially a highly specialized software system that becomes an expert in the specific operations of a factory. This AI agent can predict and identify problems, suggest improvements, and even take action autonomously.
The challenge of digitalisation
Despite the lure of advanced technologies, the manufacturing sector faces significant hurdles. Sandoval highlights the irony that modern factories with cutting-edge robotics still rely on outdated methods of data collection. “I visited a large beverage manufacturer, and even though their production line was fully automated, employees had to enter data manually,” he says. “It’s a stark reminder that automation does not automatically mean digitization.”
This disconnect between sophisticated equipment and data management systems creates inefficiencies. The promise of AI in manufacturing lies in its ability to analyze massive amounts of data and provide real-time solutions, but many companies have not yet implemented the kind of cohesive digital strategy needed to effectively train an AI agent.
Big versus small
Allie AI’s customers are primarily large corporations across America. These are giants in industries like food and beverage, where the cost of inefficiency can be astronomical. Sandoval’s company employs 35 full-time employees and an additional 40 implementation engineers who visit factories to physically install and connect the data collection systems. This on-site approach is critical to understanding and optimizing complex manufacturing processes.
But smaller companies can also benefit from AI. According to Sandoval, mid-sized companies benefit from agility, while potentially valuable projects at large companies can lie dormant for months. “Large companies can leverage more data, but often struggle with bureaucratic delays,” he explains. “Smaller companies can react quickly, implement solutions with fewer obstacles, and adapt more quickly.”
Hype or reality
The promise of AI has been met with a lot of skepticism. Some argue that it has not yet delivered significant value. Sandoval counters this view, pointing to the enormous potential of AI in manufacturing. “Globally, the manufacturing sector is wasting trillions of dollars due to inefficiencies,” he notes. “AI can solve these problems, but only if it is applied to the right problems.”
He highlights a real-world example from a cement company in Mexico, where the failure of a single kiln can cost $550,000 per hour. By using AI to predict and prevent such problems, companies can achieve significant cost savings and operational improvements.
It is unlikely that the benefits will have a comparable dollar value for smaller organizations. But the speed of implementation combined with a flatter organizational structure means that the benefits, although smaller, are likely to be realized much faster and with just as much impact.
Looking ahead, Sandoval joins the chorus of those who foresee a future where AI becomes an integral part of manufacturing. “We are moving toward a time when factories will be managed by AI agents that make the majority of operational decisions,” he says. “These agents will analyze data, suggest optimizations, and even implement changes autonomously.” Sandoval also recognizes the human factor in this technological shift. The success of AI in manufacturing depends on upskilling the workforce and overcoming resistance to new technologies. “Training and adaptation are critical,” Sandoval emphasizes. “As AI evolves, our approach to managing it and integrating it into our systems must also change.”
