The World Economic Forum (WEF) has predicted that 85 million jobs will be lost by 2025 due to the introduction and automation of AI. At the same time, it has also presented a forecast that 97 million new jobs will be created.

This means that AI is not ‘replacing’ jobs, but ‘reshaping’ them. The introduction of AI currently underway in manufacturing sites is at the forefront of this change.

Many current employees are feeling anxious about the introduction of AI. ‘Is my job secure?’, ‘Will my decades of experience be rendered meaningless?’, ‘How much should I trust AI's decisions?’ These questions are circulating in the field.

This is a challenge that goes beyond the simple issue of introducing technology and involves a fundamental change in organizational culture and human resources.

However, the introduction of AI is an inevitable trend of the times, which can be both a crisis and an opportunity.

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In fact, in the case of manufacturing companies that have successfully introduced AI, the roles of those in charge of the field have not been reduced, but rather expanded and deepened.

The scope of work has expanded into more creative and strategic areas, such as data-based decision-making, transition to high-value-added work, and discovery of new business opportunities.

This article aims to alleviate the anxiety and concerns of those in charge of the field and provide solutions to practical problems that may arise during the process of introducing AI.

In particular, we will focus on cases actually experienced at manufacturing sites and proven approaches, and provide a practical guide on how AI adoption can provide opportunities for those in charge of the field and how to prepare and respond to this period of change.

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The real reason why those in charge of the field refuse to adopt AI

Anxiety about job loss and the structural problems behind it

When promoting the introduction of AI in manufacturing, the first thing that people face is the anxiety of job loss. This is a structural problem that goes beyond the mere anxiety of individual employment.

In particular, veteran employees with more than 20 years of experience in the field express concerns that the tacit knowledge and know-how they have accumulated over the years could be replaced by AI systems. This is also based on a lack of trust in the direction of the company's workforce management.

However, the purpose of introducing AI is not to reduce the number of employees by automating repetitive and mechanical tasks. Rather, it is to help those in charge of the business focus their expertise on more valuable areas.

An analysis of the introduction of AI by global manufacturing companies shows that the role of those in charge of the business tends to expand to data-based decision-making and process optimization after the introduction of AI. This shows that AI is not replacing human judgement and experience, but is being used as a tool for more advanced decision-making.

The role of those in charge of the manufacturing site will become more important in the process of digital transformation. Their experience and insight will be essential for optimising the performance of AI systems and adapting them to the site.

AI finds patterns and provides predictions based on data, but it is still up to the people in charge to interpret and apply them in the context of the field.

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The burden and new opportunities of changing the existing way of working

Changing the work processes that have been accumulated over decades in the manufacturing field means more than just replacing the system. People in charge have developed their own ways of handling work and decision-making patterns over a long period of time.

In this situation, the introduction of an AI system requires a complete review of the existing work method.

The burden of learning a new system is not limited to the technical aspect. Data-based decision-making processes require a completely different approach from the existing experience-based decision-making.

This means a fundamental change in the way work is done, and this change inevitably acts as a psychological burden on the members of the organisation.

However, this change also presents new opportunities. AI systems can help to digitise and systematise the tacit knowledge held by frontline employees.

This is an opportunity to transform individual experience and know-how into organisational assets. In addition, when repetitive tasks such as data analysis and report creation are automated through AI systems, frontline employees can focus on higher-level analysis and strategy formulation.

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Fundamental concerns about the reliability and field applicability of AI

The reliability of AI in manufacturing sites goes beyond the simple issue of technical performance. There are many exceptions and unexpected variables that occur on the site, and there are doubts about whether AI can make the right decisions in such situations.

This concern is especially deep in areas related to direct productivity and safety, such as quality control and facility maintenance.

Field personnel have acquired the skills to deal with various variables and exceptional situations through their long experience. They can detect and respond to subtle changes or signs that are difficult to capture with simple data.

In this context, it is natural to question whether AI can truly understand and deal with all the complexities of the field.

However, the purpose of AI is not to completely replace human judgment. AI is a tool that analyzes vast amounts of data to discover patterns that humans may miss, and thereby supports the decision-making of those in charge of the business.

In fact, when looking at successful cases of AI adoption in manufacturing sites, AI plays a role in supplementing the experience and intuition of the on-site personnel. When data-based objective analysis is combined with the experience of on-site experts, the most effective decision-making can be achieved.

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The ambiguity of decision-making responsibility and solutions

One of the major concerns of those in charge of the field is the responsibility for the problems that occur when the AI system's judgment is followed. In the past, the responsibility was clear because decisions were made based on the experience and judgment of the on-site personnel, but there are concerns that this may become ambiguous with the intervention of AI.

This is because the decision-making process tends to be viewed as a black box due to the nature of AI systems. While those in charge of the field can clearly explain the basis for their decisions, it can be difficult to fully understand the decision-making process in the case of AI.

In such a situation, there is anxiety about who will be held responsible if a problem occurs after following an AI suggestion.

To solve these problems, it is important to clearly define the role and limitations of AI systems and systematize the decision-making process. It should be made clear that AI is a tool that supports decision-making, not the main decision-maker.

In addition, the basis for the judgments presented by AI should be disclosed as transparently as possible, and a process should be established to enable the on-site personnel to verify them.

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Step-by-step action plan to reduce the reluctance to adopt AI

Pre-implementation stage: The importance of a hands-on preparation process

The success of AI implementation is largely determined in the preparation stage before the actual system is built.

The participation of those in charge of the business is not a mere formal procedure, but a key factor that determines the effectiveness of the system. In this stage, it is essential to identify the practical needs and concerns of those in charge of the business and reflect them in the system design.

First, we identify the actual problems in the work process and the areas that need improvement through in-depth interviews with the people in charge of the work. The important thing in this process is to make the people in charge of the work feel that their experience and know-how are being respected.

The experience accumulated in the field over decades is an invaluable asset that cannot be replaced in building an AI system.

It is also important to redefine the purpose and expected effects of AI adoption from the perspective of those in charge of the field. It should be made clear that AI is not a tool that replaces the work of those in charge of the field, but a tool that supports them to further demonstrate their expertise.

To this end, it is effective to conduct a small-scale pilot project in which those in charge of the field participate directly so that they can experience the actual effects.

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Establishment of a practical support system during the introduction process

The most important thing in the process of introducing an AI system is for the people in charge of the field to feel that they are receiving practical support. This means a comprehensive support system that goes beyond simple technical training.

A system must be in place to immediately resolve the difficulties that people in charge of the field experience in adapting to the new system.

To do this, it is necessary to first assign dedicated support personnel to each department so that they can provide technical support on a regular basis. They not only solve technical problems, but also act as a bridge to collect and reflect the concerns and improvement requests of the department managers.

In addition, it is important to establish and implement a phased transition plan so that the workload of the department managers does not increase while they are adapting to the new system.

Problems and improvements that arise in the process of using the system should be immediately fed back and reflected. This allows the people in charge to feel that their opinions are being reflected in the improvement of the system.

This responsive support system plays a key role in increasing the acceptance of the system by the people in charge.

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Building trust through gradual transition

The success of introducing an AI system ultimately depends on how much trust the people in charge of the business can earn.

This trust is not built overnight, but through gradual demonstration of performance and continuous communication. A systematic and step-by-step transition process is essential for this.

In the first stage, the AI system will be operated in parallel with the existing work method. In this process, the on-site staff can compare the AI system's judgment with their own empirical judgment.

This is an important opportunity to verify the reliability of the AI system while identifying the system's limitations and areas for improvement.

In fact, in the case of several manufacturing companies, the AI system's prediction accuracy has greatly improved through this parallel operation period, which has led to an increase in the confidence of those in charge of the field.

In the second stage, the utilization of the AI system will be increased step by step, starting with the areas that have been verified. At this point, it is important to clearly measure the performance of each stage and share it with those in charge of the field.

By demonstrating the effectiveness of the AI system through objective performance indicators, you can encourage the voluntary use of the system by those in charge of the business.

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Long-term talent development plan

The introduction of an AI system is more than just the introduction of technology; it is a key task that will determine the future competitiveness of the organization.

Therefore, a long-term plan is needed to develop frontline employees into talent suitable for the AI era. This should be a comprehensive talent development program that goes beyond simple technical training to include data-based decision-making and strengthening digital capabilities.

The training curriculum should be designed based on the work experience and expertise of frontline employees. Training that is practical and focused on effectively combining their on-site knowledge and AI skills is needed.

In addition, education should be operated as a continuous competency development program, not a one-time event. A virtuous cycle should be created in which those in charge of the business discover new insights through the AI system and apply them to their work.

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Conditions for true success in the introduction of AI

The success of AI adoption goes beyond technical perfection and depends on the receptivity of those who use it and the actual value it creates.

The core value of the AI solutions that IMPACTIVE AI pursues is to make people more competent. This means going beyond simply increasing work efficiency to helping those in the field demonstrate their expertise at a higher level.

Many companies are focusing on reducing manpower or cutting costs through the introduction of AI, but this is only a limited use of the true potential of AI.

AI is a tool that allows those in the field to see the experience and insights they have through a new lens of data.

The experience and know-how that field veterans have accumulated over decades is a valuable asset that no AI system can replace. Our AI solutions act as a catalyst to bring out this expertise.

When the data-based insights provided by AI meet the experience of those in the field, we can create new value that we could not have imagined before.

In the era of digital transformation, true competitiveness does not depend on the technology itself, but on how much human potential can be unleashed through technology.

We aim to help those in the field make better decisions, create greater value, and ultimately grow through AI. This is what IMPACTIVE AI believes is the true definition of successful AI adoption.