Hello, I am Kakeya, the representative of Scuti.

Our company offers offshore development, lab-based development, and consulting services specializing in generative AI. Recently, we have been fortunate to receive many requests for system development integrated with generative AI.

Does your company face challenges in improving operational efficiency?
In the construction industry, daily operations are becoming more complex, and new technologies are needed to enhance productivity. One such technology gaining attention is “generative AI.”

Generative AI is an innovative tool that processes tasks, previously handled manually over long periods, in a much shorter time, supporting efficiency in design and operations. By incorporating generative AI into architectural design and optimizing business processes, the potential for significantly improving productivity across the industry is vast.

This article explains how generative AI is used in the construction industry and how it contributes to operational efficiency through specific examples and benefits.

 

What is Generative AI?

Definition of Generative AI

Generative AI contributes to operational efficiency in the construction industry in the following ways:

• Automated Design Generation:
  Generative AI can automatically create architectural design proposals. This streamlines tasks previously done manually by designers, allowing for the consideration of more design options.
• Automated 3D Modeling:
  Generative AI enables the automatic creation of 3D models of buildings, reducing the time and effort spent on manual modeling.
• Rendering Generation:
  It can also generate architectural renderings automatically, making it easier for designers to convey the visual concept of a building.
• Construction Simulation:
  Generative AI can simulate the construction process, enhancing site safety and helping to shorten project timelines.

How Generative AI Contributes to the Construction Industry

Generative AI can enhance operational efficiency in the construction industry in the following ways:

• Automated Design Generation:
  Generative AI automatically creates architectural design proposals. This streamlines tasks that were previously done manually, allowing designers to explore more design options efficiently.
• Automated 3D Modeling:
  Generative AI generates 3D models of buildings automatically, reducing the time and effort required for manual modeling.
• Automated Rendering Creation:
  It can also create architectural renderings automatically, helping designers easily convey the visual concept of a building.
• Construction Simulation:
  Generative AI can simulate the construction process, improving site safety and helping to shorten project timelines.

 

Considerations for Implementing Generative AI

Ethical Issues

Generative AI raises ethical concerns related to copyright and privacy. There is a risk that the content generated by AI could infringe on others’ copyrights or violate privacy. It is essential to be fully aware of these issues when using generative AI.

Accuracy Issues

As an evolving technology, generative AI does not always produce flawless content. The output may contain incorrect or biased information, so it is crucial for humans to review and correct AI-generated content as needed.

Importance of Data Quality

The performance of generative AI heavily relies on the quality of the training data. Training the AI with appropriate data ensures the generation of high-precision content. Ensuring the quality of training data is vital when implementing generative AI.

 

Practical Applications of Generative AI Tools

• Design Support Tools

These tools automatically generate design proposals to assist architects. Traditionally, architectural design has been a time-consuming and labor-intensive process. However, with generative AI, multiple design options can be created quickly.
AI learns from past design data and style patterns, automatically presenting proposals that reflect the required criteria and conditions. Additionally, the automation of blueprint creation minimizes manual errors and significantly enhances the overall design process efficiency.
This allows designers to focus more time on creative tasks.

• 3D Modeling Tools

This tool leverages generative AI to automatically create 3D models of buildings and efficiently modify them as needed. Traditional 3D modeling requires specialized skills and significant time investment. However, generative AI simplifies this process, allowing it to be completed quickly. For example, by inputting the basic design data of a building, AI can generate detailed 3D models. If design changes are necessary, AI can automatically make adjustments, updating the model in real-time. This streamlines communication between the design team and the client, ensuring that modifications and fine-tuning are promptly reflected.

• Rendering Tools

This tool automatically creates renderings, or visualizations, of completed buildings and offers features to enhance their quality. Renderings are crucial for communicating the visual concept of a building to clients and stakeholders, but their creation typically requires both technical skills and time. With generative AI, designers can input information to generate high-quality renderings automatically, improving the accuracy of these visualizations. AI can also replicate details such as lighting, shadows, and textures realistically, providing a more lifelike representation. This makes it easier to effectively convey the building’s vision to clients.

• Construction Simulation Tools

This tool uses generative AI to simulate the entire construction process. Construction involves many interconnected factors, but AI analyzes these elements to propose optimal plans that enhance both safety and efficiency. For instance, AI can assess weather conditions, terrain, and material delivery schedules to recommend the most effective workflow. Through simulations, AI can also predict potential risks in advance, helping to ensure safety during construction. Additionally, AI contributes to shortening project timelines and reducing costs, significantly improving the overall efficiency of projects.

 

Case Studies of Our Generative AI Solutions

Structuring Data from Construction Material Catalogs:

Traditionally, extracting information about materials needed for home construction from catalogs was done manually, incurring high labor costs. To address this, we developed a system that uses OCR to read catalog content, allowing generative AI to interpret the text and extract only relevant construction material data. This information is then stored in a database, reducing labor costs by over 70%.

Document Search with AI Chat:

A major challenge we addressed was the difficulty in locating specific documents within the vast collection of shared materials in the company. This made information retention and sharing highly dependent on individual employees. To solve this, we implemented a secure RAG (Retrieval-Augmented Generation) system, which allows for efficient document sharing by searching documents and summarizing the retrieved information using generative AI.
Note: This system processes only text-based documents; images and graphs within design and technical documents are not included in the search.

 

Conclusion

Generative AI is a powerful tool that contributes to design support and operational efficiency in the construction industry. It accelerates the generation of design proposals and 3D models, enabling designers to focus more on creative tasks.

Additionally, the automatic generation of renderings and construction simulations facilitates smooth project progression, improving safety and reducing timelines. With the adoption of generative AI, the construction industry can expect further development and enhanced efficiency.

 

Hello, I am Kakeya, the representative of Scuti.

Our company specializes in offshore development and lab-based development in Vietnam, leveraging generative AI as our core strength. We also provide consulting services for generative AI. Recently, we have been fortunate to receive many requests for system development integrated with generative AI.

Are you facing challenges in streamlining your HR operations? With advancements in generative AI, HR departments can now experience significant improvements in various tasks, including recruitment, training, and performance management.

For example, leveraging talent intelligence enables the efficient analysis of candidates’ skills, allowing them to be placed in the most suitable positions.

Additionally, we can enhance employee experience and automate training processes, creating an environment where employees can perform at their best.

 

The Present and Potential of AI Utilization in the HR Industry

How Does AI Transform HR Operations?

AI brings significant changes to HR operations.

Firstly, it automates previously manual tasks such as recruitment processes, schedule management, and candidate screening, leading to improved efficiency. This enables HR professionals to focus more on strategic tasks.

Moreover, AI analyzes large datasets to support the formulation of optimal strategies for recruitment and talent placement. Predictive analytics based on historical data allow companies to identify the right talent and make informed decisions effectively.

Thus, AI not only enhances efficiency but also empowers companies to evolve their HR strategies through data-driven decision-making.

Benefits of AI Implementation

Operational Efficiency: Routine tasks such as screening applications, scheduling interviews, and managing HR information can be automated.

Improved Recruitment Success Rate: AI-powered candidate analysis efficiently identifies candidates with high potential to match the company’s desired profile.

Enhanced Employee Engagement: Providing personalized training programs and ensuring timely communication enhances employee engagement.

Utilization of HR Data: Analyzing accumulated HR data allows for strategic decisions, such as predicting employee turnover and optimizing talent placement.

 

Streamlining HR Operations with Generative AI: Practical Examples and Benefits

Generative AI in Recruitment

By leveraging generative AI, recruitment activities such as creating job postings, screening applications, and scheduling interviews can be automated. This allows recruiters to focus on more strategic tasks, leading to greater efficiency.

Automated Job Post Creation:
With generative AI, job descriptions including roles, required skills, and experience can be generated automatically, resulting in compelling job postings.

Automated Application Screening:
AI can efficiently screen large volumes of applications, ensuring that candidates matching the company’s criteria are shortlisted. This reduces the workload of recruiters significantly.

Automated Interview Scheduling:
AI manages the schedules of applicants and interviewers, setting up interviews efficiently.

Generative AI for Employee Development

Generative AI enables the creation of personalized training programs tailored to each employee’s skills, experience, and learning progress.

Additionally, AI chatbots can handle inquiries related to training content, providing prompt responses to employees’ questions.

Automated Training Program Creation
Generative AI allows for the creation of personalized training programs tailored to each employee’s skills, experience, and learning progress.

Automated Inquiry Handling for Training
AI chatbots can automatically respond to inquiries about training content, ensuring prompt responses to employee questions.

Generative AI for Performance Management

Traditionally, employee evaluations often lacked clear standards due to subjectivity among evaluators. However, with generative AI, evaluations can be conducted based on objective performance metrics.

Automated Evaluations Based on Metrics:
Generative AI enables evaluations to be based on objective performance indicators.

Automated Feedback Generation:
AI automatically generates feedback for employees based on evaluation results.

Case Studies of Generative AI Implementation at Our Company

Structuring Resumes and Job Histories:
By uploading resumes and job histories, the data—such as names, contact details, and career information—are converted into structured formats, significantly reducing the time spent on data entry. Additionally, the system can automatically generate summaries of candidates using generative AI, allowing recruiters to quickly grasp the profiles of applicants.

Matching Evaluation Between Candidates and Companies:
Generative AI analyzes the information of both candidates and companies, evaluates the degree of compatibility, and creates lists of companies that match each candidate, as well as lists of candidates that match each company.

 

Key Considerations for Generative AI Implementation

Data Accuracy Management

The accuracy of generative AI heavily depends on the quality of the training data. Therefore, providing precise and unbiased data to the AI is crucial.
Using low-quality or biased data may lead to inaccurate or skewed results.
Additionally, AI trained on incorrect data carries the risk of spreading misinformation. For ethical reasons, ensuring data accuracy is essential. To maintain the performance and reliability of AI, a rigorous process must be established for collecting, selecting, and managing data accuracy, creating an environment where AI can learn from accurate information.

Ethical Considerations

The use of generative AI requires careful ethical consideration.

First, it is essential to prevent generative AI from producing discriminatory content or biased outputs. If the training data contains bias, the resulting outputs may also reflect unfairness.

Additionally, the handling of personal and confidential information demands strict attention. When AI processes personal data, it is crucial to establish robust management frameworks to ensure that the data is not misused. Furthermore, incorporating mechanisms for reviewing and filtering generated content is necessary to prevent the spread of misinformation, which is another ethical challenge that must be addressed.

Security Measures

When using generative AI, implementing effective security measures is essential.

In particular, handling highly sensitive HR data or personal information requires adequate precautions to mitigate the risks of data leaks or unauthorized access. Encrypting data and applying access restrictions are necessary steps to ensure information security.

Additionally, it is important to regularly check the AI system for vulnerabilities and keep security updates current. Ensuring robust security measures enables the reliable operation of AI systems.

 

Conclusion: Evolving HR Strategies with AI

AI offers new possibilities in various aspects of HR operations, revolutionizing corporate HR strategies.

Automation of recruitment processes, optimal talent placement through employee data analysis, and performance evaluation are all significantly enhanced by AI implementation.

This enables companies to pursue more strategic talent management and make quick, accurate decisions. Moreover, by utilizing AI’s analytical capabilities, businesses can support optimal placements and career development, which contributes to improving employee motivation and retention rates.

By effectively leveraging AI, companies can enhance their competitiveness and achieve sustainable growth. Evolving HR strategies with AI will be a key component in the future of corporate management.