Why humans are more important than ever, is important in AI-driven manufacturing

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Artificial intelligence feels like the shiny new toys in society, but manufacturers have been wielding AI since their first computer-aided design machines in the ’70s. From robotic arms building aircraft engines to computer vision that discovers microdefects in real time, almost all modern assembly lines rely on automation. Remove AI from manufacturing, slow production, high cost, low quality control and significant losses in competitiveness.

However, what has recently changed is the deep thinking ability of AI. Today, technology is no longer just a factor in efficiency. Complex inferences that uncover innovative manufacturing solutions are increasingly capable. Some factories are leveraging AI to achieve a 2-3x increase in productivity, a 50% improvement in service levels, and a 30% reduction in energy consumption. So, over three-quarters of the world (78%) already use AI in at least one business feature, and more is on track.

However, this new level of dependency also introduces ethical issues. Unless manufacturers are transparent when building high-tech stacks, can they trust AI, make unbiased decisions, manage resources sustainably, and prioritize human safety?

How human and machine intelligence complement each other

Before handing over any further responsibility to AI, companies must decide which principles and guardrails will guide their applications. First, we need to observe some obvious criteria. AI products and their applications should not violate the principles outlined in the Universal Declaration of Human Rights, and their use must be in compliance with the laws of the country in which they are designed. Legal requirements should guide development and implementation and leave room for adaptation. Regulations will be strengthened or new risks will arise.

Next, manufacturers need to determine the level of involvement and influence the impact AI has. There are three approaches to the role of AI in decision-making.

Human-in-Command (HIC): Here, AI products are used purely as tools. Always, people decide when and how to use the results presented by it. One example is when the machine classifies raw materials based on quality grade, but human workers check the classification and always make a final decision. Human-in-the-Loop (HITL): This approach allows people to directly influence or change decisions made by AI products. For example, a predictive maintenance system with AI may be recommended when a machine needs service, but human technicians review AI recommendations. You may then consider additional factors, such as recent performance anomalies, and decide whether to schedule maintenance immediately or override suggestions. Human-on-the-Loop (HOTL): This approach is about autonomous intelligent technologies such as emergency braking systems. Humans define the parameters of the decision during the manufacturing design process, but the decision itself is delegated to the AI product. However, those affected by decisions seeking reviews will also be possible, allowing retrospective checks to see if the process was carried out in the intended sense.

All three approaches show AI varying degrees of autonomy, but each shares an important qualifying “human.” AI is set up to allow a much higher level of industry competitive field, with small and medium-sized businesses and startups increasingly competing with legacy companies through new resource-lite capabilities. However, artificial intelligence still needs to serve people, not the other way around.

Attract employees to AI optimization

According to behavioral consultants, despite the growing AI literacy, there is a huge gap between the actual proficiency of AI skills among Professionals. Although the overall AI capabilities across the industry are located at 80%, organizations need to employ intensive reskilling programs so that employees can actually derive the greatest potential of AI.

The paradox here is that skilled ground workers have a much better grasp of the AI tools needed to enhance their expertise, eliminating the repetitive tasks that, in most cases, slower daily productivity than anyone at the executive level. Therefore, leadership should create structured collaborations between ground staff and subject matter experts in all discussions about how to implement AI.

First, we host regular small group sessions where employees and small businesses share their issues and see the possibilities of AI. Make these sessions feel low stakes and exploratory, or instead use an anonymous research and feedback platform to ensure comprehensive and honest feedback. The key is visibly working in feedback, and workers feel they are being asked and invested in the organization’s journey of change.

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

Development of trust leads to technology development

To achieve true buy-in and engagement from employees, companies need to not only declare, but clearly demonstrate that the goal of AI is not a replacement, but an enhancement of human capabilities. The success of the frame in terms of resonating with frontline workers rather than just cost savings – face late at night, reduce manual input, and more time for creative or high value work.

Identify respected engineers acting as liaisons between leadership and ground teams to help bridge the gap. These “AI Ambassadors” will help you translate your technical needs into strategic priorities. Meanwhile, organizations investing in people’s training and development often boost loyalty and engagement, preparing for key technological changes in the future.

AI-focused roles are emerging rapidly. New skill sets such as data labeling (tagging data so that AI systems can understand and learn) and rapid engineering (creating effective questions or instructions for AI tools such as language models) must recognize and support organizations with fresh opportunities for growth.

Ultimately, employee trust is the foundation of sustainable business success. It is a way to reinforce and explain the role of AI in enhancing our world. It is a disparity in enthusiasm, improving economic outlook, and promoting responsible use of natural resources. In the long run, the challenge of AI is to strike a proper balance between economic growth and social responsibility. Building on full transparency, incremental transformation is the only way AI can benefit business, society and the environment.