By Christopher Bolender, Senior Program Manager, Engineering and AI Programs, ACR Electronics, Fort Lauderdale, FL
AI adoption in manufacturing is the focus of this article and explores why successful industrial AI is driven more by culture and people than by technology alone.
Before diving in, consider the core challenge the author highlights:
“The technology almost always works. The question is whether your people will use it.”
The Adoption Gap. Why AI Success Has Nothing to Do with AI
Every few months, another headline announces that artificial intelligence has achieved some new milestone. Faster processing. Better accuracy. More sophisticated reasoning. And every few months, I watch another organization invest in these tools only to see them gather dust because nobody on the floor trusts them.
After building an AI program from scratch at a regulated aerospace and marine manufacturer, I’ve learned something that rarely makes it into the trade publications: the technology almost always works. The question is whether your people will use it.
Technology Is Only as Strong as Its Weakest Adopter
In manufacturing, we understand that a production line moves at the pace of its slowest station. The same principle applies to AI adoption. You can deploy the most sophisticated forecasting model or the most elegant automation system, but if your most experienced planner refuses to look at the output, you’ve purchased expensive shelfware.
This isn’t a criticism of the skeptics. The people most resistant to new technology are often the ones who understand the operation best. They’ve spent years developing intuition that keeps things running when systems fail. Asking them to trust a black box that contradicts their judgment isn’t a training problem. It’s a trust problem.
And trust isn’t built through PowerPoint presentations or mandatory training sessions. It’s built case by case, tool by tool, proving that the technology makes their expertise more valuable rather than obsolete.
Legitimacy Before Policy
I learned this lesson long before I ever touched an AI system. As a young Lieutenant in the Marine Corps, I served as a squadron safety officer. Part of that job meant enforcing rules that seemed absurd on their face. One regulation required that if you propped a door open with a rock, that rock had to be spray painted red.
Imagine explaining that to a Gunnery Sergeant who’s been in the Corps for fifteen years, has a lip full of tobacco, and has forgotten more about running a flight line than you’ve ever learned. I walked up to deliver this guidance, and before I could finish my second sentence, he spit on the ground and stared at me with the kind of patience that isn’t patient at all.
The only way through that moment was legitimacy. Not rank. Not policy citations. I had to acknowledge that yes, this sounds ridiculous. I had to show that I understood his world well enough to know why it sounded ridiculous. And then I had to be forthright about the fact that quirky or not, there was a reason behind it, and we were going to do it anyway.
The same dynamic plays out in every AI implementation I’ve led. You cannot mandate trust. You earn it by demonstrating that you understand what people actually do, by being honest about the limitations and oddities of what you’re asking them to adopt, and by proving you’re not just another person from corporate pushing something that makes their job harder.
The Innovation Gap
Most AI initiatives get this wrong: they treat innovation as something that happens in a lab or a vendor’s office, then gets delivered to the people who actually do the work. The engineers build the system, hand it off, and move on. Meanwhile, the operators who are supposed to use it never had a voice in what problem it was solving.
The innovators who create these tools need to stay involved long after the code is written. Implementation isn’t a phase that comes after development. It is development. The real work begins when the system meets the messy reality of actual operations.
When a company develops AI capabilities internally, something different happens. The people building the tools are the same people who understand the pain points. They sit in the same meetings, deal with the same frustrations, and have credibility when they say this tool will help. That investment translates to adoption in ways that no external vendor relationship can replicate.
Small Tools, Big Trust
Most people hear “AI” and think of ChatGPT or some omniscient system that’s going to revolutionize everything overnight. That misconception is one of the biggest obstacles to practical adoption.
The AI implementations that actually stick aren’t sweeping transformations. They’re targeted solutions to specific problems. A forecasting model that reduces manual data reconciliation. A document processor that pulls specifications without hunting through folders. An estimating tool that gives planners a starting point instead of a blank spreadsheet.
When I rolled out AI forecasting at our facility, we didn’t lead with the technology. We led with the problem: planners were spending hours every week reconciling demand signals from systems that didn’t talk to each other. The AI didn’t replace their judgment. It gave them better inputs so they could focus on decisions that actually required human expertise.
Workers need to see the benefit in concrete terms. Not “increased efficiency” in some quarterly report, but fewer hours doing the tedious work they’ve always hated. When the tool delivers that, trust follows.
Leadership and Humility
None of this happens without leadership that’s willing to approach AI with humility rather than hubris. The ego problem runs in both directions. Some leaders resist AI because they’ve built their careers on the current way of doing things. Others embrace it too eagerly, announcing transformation initiatives without understanding what the technology can actually do.
The leaders who succeed at AI adoption share a few characteristics. They’re curious enough to understand the tools without needing to be the expert. They’re humble enough to listen when floor level workers explain why something won’t work. And they’re patient enough to let trust build organically rather than mandating adoption by deadline.
AI as Ecosystem, Not Silver Bullet
The mental model that serves best isn’t AI as a single transformative technology, but AI as an ecosystem of tools, each solving a specific problem within a larger operational context. At our facility, AI touches forecasting, document processing, compliance workflows, and engineering knowledge retrieval. None of these systems replaced a human function entirely. Each one augmented existing capabilities while keeping human judgment at the center of consequential decisions.
The Real Measure
At the end of the day, the question isn’t how sophisticated your AI is. It’s how well your organization adapts to working alongside it.
That adaptation requires organic development that includes the people whose work will change. It requires small, practical tools that deliver immediate benefit with minimal learning curve. It requires leaders humble enough to listen and patient enough to build trust over time. And it requires understanding AI not as a single solution but as an ecosystem that evolves with your operation.
The gap between AI capability and AI value isn’t technical. It’s cultural. The organizations that close that gap won’t be the ones with the most advanced algorithms. They’ll be the ones that figured out how to bring their people along for the journey.
About the Author
Christopher Bolender is Senior Program Manager for Engineering and AI Programs at ACR Electronics in Fort Lauderdale, Florida. He has hands on experience leading industrial AI adoption inside regulated aerospace and marine manufacturing environments. His work focuses on practical implementation, cultural adoption, and building AI systems that strengthen human expertise in high consequence engineering operations.
