AI Behind the Assembly Line

Seeing the Unseen

When Tom Gerdes stepped into his role as the first CIO at The Heico Companies, he entered a vast manufacturing empire of more than 70 operating businesses spread across 19 countries. What he found was not a high-tech playground but a patchwork of legacy systems and analog practices, where modern IT hadn’t yet taken root across the board. 

"We needed to help transform the organization and build out different capabilities to really push and disrupt a bit of what we've been doing in the past," Gerdes said. For a $2.5 billion company rooted in traditional heavy industries like wire galvanization and crane manufacturing, transformation would require more than just cloud tools; it meant rethinking quality, visibility, and even language itself.

Machines That Watch Machines

One of the most transformative efforts under Gerdes’ leadership has been the deployment of computer vision technologies on the shop floor. In one of Heico’s wire manufacturing businesses, the team began applying AI-driven visual inspection tools to one of the company’s oldest industrial processes: drawing and galvanizing wire.

"You take a rod. That rod gets drawn out to wire, then you galvanize the wire, and then it gets coiled into different configurations based on customer need," he explained. The quality control process, which was once dependent on human inspection, now leverages computer vision to detect anomalies in real-time. "That's where that digital vision starts to give the opportunity to look at and indicate if you've got a process failure on a real-time basis."

This shift represents more than automation. It enables a level of consistency and responsiveness impossible through manual means alone. According to Gerdes, "applying that technology on a real-time basis gives us the ability to alert to quality issues, remediate those quality issues, and at the end of the day, it drives better yield, better product quality, and all of that delivers better value to our customers."

Smart Products in the Field

Gerdes is equally focused on the intelligence that comes after the product leaves the factory. Some of Heico’s more advanced equipment lines, such as cranes and forklifts, are now embedded with telematics systems that communicate performance data back to the company. This connectivity not only informs the business of how its products are used but also brings manufacturers into closer partnership with their customers.

"Taking that information and being far more aware of the status of the equipment, how it's being operated, and ensuring that you're getting the maximum value for your end consumer out of that product," said Gerdes. For industries not typically seen as tech-first, the benefits of this data feedback loop mirror innovations in the airline industry: "Uptime and availability of equipment and really putting that across our portfolio of products."

He emphasized that this kind of intelligence reshapes the customer relationship and creates a pathway for continual improvement: "It becomes a bit of a transformation to take both our business through but, as well, our customer base around the additional value that you can get out of those services and solutions."

Towers Translated by AI

With 70 operating entities across 19 countries, one of the less visible but deeply complex problems Gerdes faces is reconciling disparate data environments. The Heico Companies run on a sprawling web of ERP systems, each with its own schema, format, and naming conventions.

"We're looking at, say, 40 different ERP data sets we deal with across all of our locations, trying to get a viewpoint of how we understand our core customer base. Who are we serving? Who are those customers?" he said. The manual effort required to unify this data would be massive. But Generative AI offers a potential leap over this barrier.

"I'm hopeful that this is a way in which we can create a different way to solve those kinds of issues," Gerdes noted. Rather than spending years mapping schemas and cleansing fields, Heico is exploring AI models trained on enterprise-wide data to recognize, translate, and correlate information across systems.

He envisions a future where interoperability doesn’t demand conformity: "The opportunity to look at how you leap over the work effort to create an enterprise data schema and use these tools to gain insights that might be a bit more challenging for us as an organization."

Multilingual, Multi-Market, Fully Automated

Generative AI is also helping Heico speak the languages of their global markets better. With operations in nearly 20 countries, creating multilingual content was historically a slow and error-prone process. AI is now speeding up and scaling this capability.

"Content creation is a big one," said Gerdes. "The ability to start looking at how we build out product catalogs and how we think about creating descriptors of our products. Utilizing Generative AI in that space, I think, is a key area that we see value in."

This extends directly to internationalization efforts. "Being able to go through a process and do forward and reverse translation into foreign languages and utilizing Generative AI to accelerate that process of work, it’s hugely impactful," he added. Not only does this enhance speed to market, but it also positions the organization to compete more nimbly in foreign markets without the need for a dramatic expansion of local marketing or translation teams.

Beyond Standardization Toward Fluid Automation

A key revelation in Gerdes’ AI approach is that the organization may not need to fully standardize everything before automating. Historically, enterprise automation demanded a foundation of consistent process and data models. This is a precondition that was often time-consuming and costly to achieve. But Generative AI could flip this dynamic.

"Hopefully, this moves beyond and gives us the opportunity to start seeing some of these automation gains without having to do everything as a bespoke one-off automation routine," he said. The goal isn’t to just work faster, but smarter: "Automation that doesn't require me to go to the same level of process standardization across the board."

Rewiring Trust in the Age of AI

As these systems begin making decisions in milliseconds, from quality inspection to multilingual catalog creation, Gerdes recognizes that building trust in AI will be one of the defining challenges ahead. "Building the confidence with our employee base and with our customers that the parts they receive are parts that they can trust... becomes an interesting component," he reflected.

But for now, the path forward is clear. From embedded sensors in cranes to digital vision in wire plants and language-agnostic AI data mapping, The Heico Companies are using AI to tackle the complexity of industrial operations head-on. As Gerdes summed it up: "It's going to be really interesting to see how that evolves here."

In the world of manufacturing, where change is slow and margins are tight, this kind of agile, distributed innovation may well be the blueprint for modern industrial transformation.