Key Takeaways
Hyundai Motor Group has expanded access to generative AI to more than 30,000 employees. Its transformation offers three lessons for HR: empower employees to solve problems, turn individual expertise into organizational knowledge, and measure changes in work—not just training participation. Real examples from engineering, manufacturing, vehicle service, and customer support show how AI can help employees work differently.
1. AI Transformation Starts with Employees, Not Technology
Peemo — Research and Evidence Gathering:
Dash, Hyundai Motor Group offers an interesting example of company-wide AI transformation.
According to its August 2026 announcement, more than 30,000 employees were using its internal generative AI platform, H Chat Pro, as of July 2026.
That represented approximately 80% of general and research employees at Hyundai Motor and Kia—not 80% of the group’s entire workforce.
The platform gives employees access to AI models in a protected business environment. Some employees without programming backgrounds have also begun creating AI agents tailored to their work.
But the company did not start with AI alone.
It had been advancing digital transformation since 2019, introducing collaboration and knowledge-sharing tools and connecting data across research, production, quality, and sales.
Dash — Leadership and Talent Development Analysis:
That sequence matters.
Many Japanese companies are investing in AI training. Yet HR leaders often face a familiar challenge: employees complete the training, but their everyday behavior barely changes.
Hyundai’s experience suggests that AI adoption depends on more than technical literacy.
Employees need access to information, opportunities to experiment, and an environment where improvement ideas can become action.
For HR, the central question is not simply:
“How many employees have learned to use AI?”
It is:
“How many employees are changing the way they work because of AI?”
2. What Employees Are Actually Doing Differently
Engineering: Spending Less Time Searching, More Time Solving Problems
Peemo:
In crash-safety engineering, employees previously spent considerable time searching historical test results, images, and analysis reports.
The company introduced Crash Safety AI Assistant to help engineers retrieve and compare relevant information.
According to Hyundai Motor Group, the time required for related information searches and analysis was reduced by approximately 90%.
Engineers can use the retrieved information to examine past cases and support technical decisions.
Dash:
This is an important distinction.
The objective is not merely to search faster. It is to free engineers to spend more time interpreting evidence, identifying causes, and improving vehicle designs.
From The Extraordinary Leader™ perspective, effective leadership includes the ability to analyze problems, take initiative, and deliver results.
These behaviors are not limited to employees with management titles.
An engineer who identifies a recurring problem, uses AI to investigate it, and helps colleagues improve a process is demonstrating leadership through action.
Manufacturing: Giving Frontline Employees Better Information
Peemo:
In manufacturing, Hyundai Motor Group uses AI-based image recognition to read vehicle identification numbers and compare them with production records.
This helps frontline employees identify discrepancies and respond to potential problems.
The company reports annual cost savings of approximately US$3.9 million from this application.
Another application uses AI to calculate appropriate movement routes when disruptions affect the flow of parts and transport equipment.
The company reports an approximately 86% reduction in unnecessary production downtime in the relevant process.
Dash:
For Japanese manufacturers, this connects directly to kaizen, frontline problem-solving, and operational excellence.
AI can perform repetitive recognition and complex calculations. Employees can then focus more attention on exceptions, quality, and corrective action.
However, technology alone does not create continuous improvement.
Employees still need the confidence to raise concerns, question existing procedures, and collaborate across functions.
That is where DialogueWORKS becomes relevant.
Its emphasis on recognizing assumptions and creating constructive dialogue helps us understand why employees must be able to discuss problems rather than simply follow established routines.
Manufacturing Engineering: Turning Experience into Shared Knowledge
Peemo:
Hyundai Motor Group has also introduced E-FOREST: POLARIS, an AI platform for manufacturing operations.
It enables engineers working in areas such as quality, production, equipment, and logistics to create, test, and operate AI agents suited to their work.
This creates opportunities to make specialized operational knowledge more accessible.
Dash:
This may be one of the most important examples for Japanese HR leaders.
Many organizations depend heavily on experienced employees whose expertise is difficult to document or transfer.
The challenge is not a lack of information. It is that valuable knowledge often remains with individuals.
When engineers translate their experience into reusable AI tools, they can potentially make that knowledge available to colleagues.
But first, they must articulate what they know.
Why do they make a particular judgment?
What signals indicate a potential failure?
Under what conditions should a different action be taken?
These are questions that connect naturally with PQA—Precision Questioning and Answering.
Better questions help employees identify the knowledge that matters and communicate it more clearly.
AI becomes more valuable when employees can explain their own reasoning.
Vehicle Service: Supporting Frontline Judgment
Peemo:
At overseas service locations, technicians can describe vehicle symptoms or enter error codes in natural language.
AI helps them locate relevant repair information and diagnostic guidance.
Hyundai Motor Group reports an approximately 42% reduction in service response time.
Technicians still need to inspect the vehicle and make appropriate service decisions.
Dash:
This illustrates an important principle for workforce development.
AI does not eliminate the need for professional judgment. It changes how employees access and apply knowledge.
For HR, the opportunity is to develop employees who can combine technical expertise, critical thinking, and AI-supported information.
That combination becomes especially important as products and services grow more complex.
Customer Support: Moving from Processing to Understanding
Peemo:
Customer-support employees also use AI to classify app reviews, identify customer sentiment, and prepare draft responses.
Employees review the AI-generated output and handle cases requiring human attention.
The company reports that average processing time per review decreased from 35 minutes to approximately five minutes.
Dash:
The productivity gain is substantial, but the deeper opportunity lies in how employees use the time saved.
They may have more capacity to identify recurring customer concerns, share insights with other departments, and recommend service improvements.
That requires more than operating an AI tool.
It requires listening, interpreting information, and influencing others.
These are core leadership behaviors.
3. Three Lessons for Japanese HR Leaders
Peemo:
Across these examples, employees are using AI in different ways.
Engineers search and compare technical information.
Manufacturing employees identify exceptions and respond to disruptions.
Technicians access diagnostic knowledge.
Customer-support teams process feedback more efficiently.
What connects these activities from a talent-development perspective?
Dash:
I see three lessons.
Lesson 1: Enable Employee-Led Improvement
AI transformation should not be treated exclusively as an IT project.
Employees closest to the work are often best positioned to recognize unnecessary steps, recurring problems, and opportunities for improvement.
HR can support them by developing problem-solving skills, encouraging experimentation, and recognizing initiative.
This is especially relevant to Japanese companies seeking to strengthen employee engagement and autonomous career development.
The goal is not simply to create more AI users.
It is to develop employees who take ownership of improving their work.
Lesson 2: Turn Tacit Knowledge into Organizational Capability
Japan’s aging workforce and the retirement of experienced specialists make knowledge transfer an urgent issue for many organizations.
Hyundai Motor Group’s manufacturing and engineering examples suggest how AI can support the sharing of specialized knowledge.
But AI tools cannot automatically capture the reasoning behind an expert’s decisions.
Employees must ask questions, explain their assumptions, and communicate what they have learned.
This is why communication capability remains essential even as technology advances.
DialogueWORKS and PQA provide useful perspectives for examining these behaviors.
The objective is not simply to store more information.
It is to make valuable knowledge usable by other people.
Lesson 3: Measure Behavioral Change, Not Training Attendance
Traditional training metrics often focus on participation, completion, and satisfaction.
Those measures are useful, but they do not establish whether employees have changed their behavior.
Hyundai Motor Group reports outcomes such as reduced search time, shorter service response times, and lower production downtime.
These are operational measures, not direct measures of leadership development. Nevertheless, they provide evidence that work processes are changing.
Japanese HR leaders could complement training metrics with questions such as:
- What problems did employees identify?
- What did they change after using AI?
- How did they share their learning with colleagues?
- What improvements resulted in quality, productivity, or customer experience?
The Extraordinary Leader™ emphasizes the importance of observable leadership behaviors.
For talent development, the same principle is valuable:
Development matters when employees behave differently and contribute more effectively.
4. The Real Question for HR: Who Is Driving the Change?
Peemo:
Dash, Hyundai Motor Group’s experience seems to suggest that successful AI transformation involves both technology and organizational practices.
Dash:
Exactly.
Providing AI tools is an important step. But the greater challenge is creating an organization where employees recognize problems, test solutions, share knowledge, and act on what they learn.
For Japanese companies, this connects AI transformation with several strategic HR priorities:
Human capital management.
Reskilling and upskilling.
Knowledge transfer.
Employee engagement.
Psychological safety.
Leadership development.
These should not be treated as separate initiatives.
They are connected through the everyday behavior of employees.
The question for HR is shifting from:
“How do we get employees to use AI?”
to:
“How do we develop employees who use AI to improve their work, collaborate with others, and create measurable value?”
That is where AI transformation becomes a talent-development opportunity.
In our Friday HLS Insight, we will examine the next question:
“3 Employee Behaviors That Turn AI into Better Work—Without Being an AI Expert.”
We will explore these behaviors through the perspectives of The Extraordinary Leader™, DialogueWORKS, and PQA.
Source
Hyundai Motor Group, official announcement, August 12, 2026:
https://www.hyundaimotorgroup.com/en/news/hyundai-motor-group-accelerates-ai-transformation-across-its-business-advancing-toward-the-physical-ai-era
The leadership and talent-development interpretations in this article are those of SmartWorks. They do not imply that Hyundai Motor Group uses or endorses The Extraordinary Leader™, DialogueWORKS, or PQA.
Akira Chida (Dash)
SmartWorks
HLS Global Case Research