Overcoming AI-nertia: What To Do When Your AI Tools Sit Unused 

Most AI implementations fail not for lack of capability, but for lack of adoption. The term AI-nertia captures the phenomenon: You deploy an AI solution, but usage hovers in the single digits despite technical success. At its root is a misalignment of human psychology, organizational incentives, and change management.

To break inertia, you must design adoption as carefully as the model. That means staging visibility first, running AI in parallel with existing workflows, and tying rewards to usage. When workers see value and feel rewarded, the tool evolves from an experiment to a trusted assistant. Below is an excerpt illustrating how real companies overcame these barriers.

As explained by Ishir Vaidyanath and Kasyap Chakra in “AI-nertia: You Bought AI and No One Is Using It. Now What?” on IndustryWeek:

“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.

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.”

Continue reading “AI-nertia: You Bought AI and No One Is Using It. Now What?” by Ishir Vaidyanath & Kasyap Chakra on IndustryWeek. 

Why It Matters to You

This is a must-read for executives investing in AI: deploying a model doesn’t guarantee value — usage does. Many AI failures trace back not to algorithms but to human resistance, misaligned incentives, and rollout design. If your organization wants ROI, you must ensure adoption is baked into design from day one.

Without adoption strategies, even the best AI can sit idle, turning investment into sunk cost. Use tactics such as gradual visibility, parallel workflows, and incentive alignment as required phases of any AI program.

Next Steps 

• CEO/Digital Lead: Map out your AI-nertia risk: list adoption barriers (fear, incentives, oversight) before scaling. 
  
• Operations/Line Managers: Deploy a pilot in read-only mode first; share AI predictions to show value before full deployment. 
  
• HR/Rewards/Compensation: Tie part of compensation or bonus to AI usage outcomes (e.g., revenue generated, time saved). 
  
• IT/AI Teams: Run AI in parallel workflows, credit human users for AI-supported outcomes, then gradually shift control. 
  
• Change/Training Leaders: Hold “ride-along” sessions where users test AI outputs, ask questions, and build trust.