Accountability: The AI Issue No One Is Talking About

Since 1999, Bill Rokos has spearheaded the development of Parsec’s manufacturing operations management (MOM) platform, TrakSYS.

AI-based automation is becoming an expectation across industries. Even manufacturing—which has traditionally been slower to adopt emerging technologies—has been relatively quick to invest in and explore the tool’s potential. A study by my company found that AI adoption in the sector has skyrocketed over the past two years.​

Nearly three-quarters (72%) of industry leaders now say their organizations use the technology in their operations, a significant boost from the 53% who said the same in 2024. This is undeniable progress. It’s the mark of an industry ready to move into a new, more precise and data-driven era of operations.​

There’s a catch, though. Few respondents have fully extended AI tools across departments, and only 10% are using AI-/ML-enabled automation at scale. So, what’s holding up the rest?​

The Obstacle Of Ownership

The answer is as psychological as it is operational, and we’ve been largely ignoring the former with attention so focused on the latter. As AI has worked its way into workplaces and production lines, much of the discussion has centered on infrastructure, data, tooling and computational potential. All fair, but it’s left an equally critical factor out of the conversation: the people, of course.​

I hear you, and I’m aware—plenty of leaders have opined on the impacts of AI on those who interact with it. We’ve discussed the labor market implications, effects on work quality and worker skills, and new models emerging in the age of augmented productivity. I’m not talking about any of that.​

The issue underlying this gap at this moment is both more complex and more basic than the traditional sticking points outlined above. When autonomous AIs are in the mix, we all struggle to answer what have always been relatively simple questions: What if it makes a mistake? Who gets the blame? Who handles the fallout?​