Ethical AI: Why Humans Matter More Than Ever in AI-Driven Manufacturing

Summary

While artificial intelligence feels like society’s shiniest new toy, manufacturers have been wielding AI since the first computer-assisted design machines in the ‘70s. From robotic arms that build aircraft engines to computer vision that spots micro-defects in real time, almost every modern assembly line depends on automation. Take AI out of manufacturing and you’d see slower production, higher costs, lower quality control and a significant loss in competitive edge.

What has changed recently, however, are AI’s deep-thinking capacities. Today, technology is no longer a mere driver of efficiency; it is increasingly capable of complex reasoning that uncovers innovative manufacturing solutions. Some factories are leveraging AI to achieve a 2-3x increase in productivity, a 50% improvement in service levels, and a 30% decrease in energy consumption. It’s why more than three-quarters (78%) of organizations worldwide already use AI in at least one business function, with many more set to follow suit.

But this new level of dependence also introduces questions of ethics. Unless manufacturers show transparency when building out their tech stack, can we trust AI to make bias-free decisions, manage resources sustainably, and prioritize human safety?

How human and machine intelligence can complement each other

Before handing over more responsibilities to AI, companies must decide upon the principles and guardrails that guide its application. Firstly, some obvious criteria must be observed. AI products and their applications should not violate the principles outlined in the Universal Declaration of Human Rights, and their use must comply with the laws of the countries for which they are designed. Legal requirements must guide development and implementation, leaving room for adaptation—should rules tighten or new risks arise.

Next, manufacturers need to determine the level of involvement and influence their AI will have. There are three approaches to the role of AI in decision-making:

• Human-in-command (HIC): Here, the AI product is used purely as a tool. At all times, people decide when and how to use the results presented by it. One example is when a machine classifies raw materials based on quality grades, but a human worker reviews the classifications and always makes the final decision on their use.
• Human-in-the-loop (HITL): In this approach, people can directly influence or change decisions made by an AI product. For instance, an AI-powered predictive maintenance system may recommend when a machine needs servicing, but a human technician reviews the AI’s recommendation. Then, they might consider additional factors like recent performance anomalies, for example, and decide whether to schedule maintenance immediately or override the suggestion.
• Human-on-the-loop (HOTL): This approach concerns autonomous intelligent technology, such as emergency braking systems. Humans will define the parameters for decisions during the manufacturing design process, but decisions themselves are delegated to the AI product. However, it also allows those affected by the decision to appeal for review, ensuring retrospective checks over whether processes were carried out in the intended sense.

While all three approaches give AI varying degrees of autonomy, they each share a significant qualifier: ‘human’. AI is set to enable a far more level industry playing field, with SMEs and startups increasingly competing with legacy companies through new, resource-light capabilities. But artificial intelligence still needs to serve people, not the other way around.

Engaging employees in the optimization of AI

According to a behavioural consultancy, Behave, even as AI literacy is growing, there is a significant gap between perceived and actual proficiency in AI skills amongst professionals. While the overall competency of AI across industries sits at 80%, organizations must adopt intensive re-skilling programs to actually enable their employees to derive the maximum potential of AI. 

The paradox here is that skilled on-the-ground workers will have a far greater grasp of what AI tools they need to augment their expertise, and eliminate the repetitive tasks that slow daily productivity, than almost anyone at an executive level. So, leadership must create structured collaboration between their on-ground staff and subject-matter experts in all discussions about how AI can be best implemented.

First, host regular, small-group sessions where employees and SMEs can share pain points and where they see AI’s potential. Make these sessions feel low-stakes and exploratory, or alternatively use anonymous surveys and feedback platforms, ensuring comprehensive, honest feedback. The key is visibly acting on feedback so that workers feel heard and invested in the organization’s transformation journey.

Next, bring workers into the prototyping phases of AI tools. Ask them to test, tweak, and validate tools for better solutions and greater buy-in.
 

Development of trust will guide the development of tech

To achieve true buy-in and engagement from employees, businesses must not just declare but also clearly demonstrate that the goal of AI is not replacement, but augmentation of human abilities. Rather than mere cost savings, frame success in terms that resonate with frontline workers—fewer late nights, reduced manual entry, and more time for creative or high-value work.

To help bridge gaps, identify respected technicians to act as liaisons between leadership and on-the-ground teams. These “AI ambassadors” can help translate technical needs into strategic priorities. Meanwhile, organizations that invest in training and developing their people often boost loyalty and engagement, leaving them better prepared for the major technological changes ahead.

AI-focused roles are emerging fast. New skill sets like data labelling (tagging data so AI systems can understand and learn from it) and prompt engineering (crafting effective questions or instructions for AI tools like language models) require organizations to recognize and support fresh opportunities for growth.

Ultimately, employee trust is the foundation of sustainable business success. It’s how we reinforce and illustrate AI’s role in enhancing our world—sparking enthusiasm, improving economic prospects and promoting the responsible use of natural resources. In the long term, the AI challenge lies in striking the right balance between economic growth and social responsibilities. An incremental transformation, built upon total transparency, is the only way to ensure AI benefits business, society, and the environment.