AI-nertia: You Bought AI and No One Is Using It. Now What?

Your manufacturing company invested six figures in new technology. The demos were impressive. Leadership was excited. Six months later, your adoption dashboard still shows single-digit usage.

Familiar?

After embedding at dozens of manufacturers and distributors to implement AI systems, we've observed a widespread challenge we’ve coined as AI-nertia (AI + inertia): the tendency of organizations to uniquely resist AI adoption, even more so than previous technologies.

Efficiency gains from AI are massive—but if the industry adopts AI as slowly as it did e-commerce in the 2010s, America risks squandering one of the greatest opportunities manufacturing has seen in decades.

The Three Adoption Killers Nobody Talks About

Unknown unknowns: In technical fields, the idea of unknown unknowns is widely accepted. Things you don’t know that you don’t know tend to influence the end results of any decision drastically. This phenomenon is even stronger with AI because data-driven takeaways and machine-learning-model suggestions can be unpredictable, oftentimes even to experts like myself.

Manufacturing teams excel at tracking concrete metrics like production efficiency, defect rates and delivery timelines. They are accustomed to predictable ROI calculations for investments. As a result, many people and teams err against spending the time to understand AI when more deterministic, short-term options exist. A common mindset is “Why should I invest in learning about this model which may help me tomorrow, when I can reliably make an extra sale instead?”

Loss aversion: We were shocked by what we found at one manufacturer: they had over $2 million in open quotes at any given time simply dying—no follow-up, no tracking, no closure. So, when we introduced AI to automate those follow-ups, we expected enthusiasm. Instead, the sales team hesitated even to press send.

Why? Well, what if their name was wrong? Or we got their order details wrong? Even though this information is being pulled directly from their own systems, the fear of being blamed for a mistake far outweighed the potential reward from being the one to reclaim that revenue. Even with superhuman accuracy, no one wanted to be the person who approved a system that they could not be 100% confident would not mess up.

This isn’t just a human bias; it’s institutional. Organizations foster AI-nertia because the downside of failure (getting fired) looms much larger than the upside of success (recognition or even promotion). For AI—or any high-ROI tech—to succeed, organizations need to start thinking in terms of maximizing expected value and embrace smart risk, not avoid it.

The incentive gap: At a building supply distributor we worked with, inside sales reps used to process orders all day. They're not paid on commission. When we introduced AI to automate order entry, the reps didn’t actually spend their four to five hours of extra time cross-selling or reaching out to dormant accounts like we expected. The AI looked like it wasn’t providing any value, but the issue is not the technology. Believing that forms the seed of AI-nertia. The real problem is incentives.

This isn't laziness. It's economics. If successfully adopting AI means finishing work faster with no additional reward, rational workers will resist. Manufacturing has built decades of compensation structures around time-based work, not outcome-based results. AI disrupts this model—there’s more leverage on agency now. More effort leads to far more results.

The Solution: Align AI With Human Nature

Forget forcing adoption. Make it inevitable by working with human incentives, not against them. While the best solutions for AI-nertia are personalized and creative, here are successful examples we’ve suggested to various companies.

Start with read-only reality

Don't hand workers a new system and expect gratitude. Begin with AI that observes and reports. At one manufacturer, we spent four weeks just tracking quote patterns before automating a single step. Workers saw daily reports showing $50,000 in quotes about to expire. Suddenly, they proactively asked for the AI to step in.

Create parallel wins: Run AI alongside existing workflows. Let managers control the system while workers see the benefits. When our AI texted customers about cross-sell opportunities, responses went to the original salesperson. They got credit for "their" follow-up without lifting a finger. Adoption went from 10% to 85% in six weeks.

Share the spoils: One client created an "AI Assist Bonus"—2% of any revenue generated through AI tools. Another added "technology adoption" to performance reviews, weighted at 15%. This directly affected compensation, so the same workers who resisted for months suddenly became power users.

The heart of any company is still its people; don’t expect that to change anytime soon. So, the key? Make AI success equal human success. If the machine wins and workers lose, expect rebellion.

Red Flags That Predict Failure

Through countless implementations, we’ve found these phrases predict disaster:

"We're between ERPs right now." Translation: We haven’t been able to convince our teams to use a technology we decided on many years ago, so we are trying our best to keep two systems up to date.

"Our workers aren't tech-savvy." We've seen 60-year-old warehouse managers master AI tools when it meant their job became easier. The problem is usually not the workers; it’s how you motivate them.

"We’re so excited to improve [insert complex process here]." This one is tricky because the excitement is actually positive. However, AI-nertia blossoms in complexity. Start where the pain is obvious and the solution is simple.

"We don't need to sit with the teams." In two cases, we saw the embedding phase skipped to “save time.” Both times adoption failed. You can't automate workflows you don't deeply understand, and you can’t rally support without standing with the adopters.

"This will help us reduce headcount." Workers aren't naive. If AI means fewer jobs, expect maximum resistance. Position it as augmentation, not replacement. As a bonus, we’ve found these plans also achieve higher long-term return on investment.

The Path Forward

The manufacturers thriving with AI aren't the ones with the best technology. They're the ones who understood a simple truth: Tools alone don't transform companies. Motivated people using these tools transform companies.

Your AI investment isn't dead. It's waiting for you to answer the only question that matters to your workforce: "What's in it for me?"

Answer that clearly, and adoption becomes inevitable. Ignore it, and accept that AI-nertia will dictate the future.