Hello, I am Kakeya, the representative of Scuti.
Our company offers services such as Vietnamese offshore development and lab-type development with a focus on generative AI, and recently, we have been fortunate to receive numerous requests for system development that integrates generative AI.
Implementing generative AI presents a significant opportunity for companies to enhance productivity and streamline operations.
However, if one does not correctly understand the methods of implementing and utilizing generative AI, it will not be possible to fully leverage its potential value. Generative AI refers to the technology that generates new content and data using AI generation techniques, capable of outputting various formats such as text, images, audio, and even video.
Many companies are utilizing this technology to automate business processes and achieve innovations in creative fields, but proper implementation methods and responses to risks are essential.
What is Generative AI?
Definition and Mechanism of Generative AI
Generative AI is an AI technology that generates new content and data just like a human creator. It learns patterns and rules from large amounts of data and, based on that knowledge, can produce natural text, images, music, and more that seem to have been created by humans.
For example, a generative AI trained on a vast number of paintings can create new works of art, and an AI trained on many compositions can compose original music. Additionally, a generative AI trained on large amounts of text data can produce natural text that appears to have been written by a human, generating novels, articles, poems, and more.
In this way, generative AI has the potential to extend human creativity and generate new content that has never existed before.
What Generative AI Can Do
Generative AI is a powerful tool that shapes our imagination. It can automatically generate various outputs, including text, images, audio, and program code.
For example, it is increasingly used in business contexts, such as chatbots that automatically respond to customer inquiries and marketing tools that generate attractive advertising copy and catchphrases. Furthermore, it opens up new possibilities in the arts, enabling the creation of innovative product designs and compositions or paintings.
Generative AI stimulates our creativity and has the power to accelerate innovation across various fields.
Algorithms of Generative AI
Representative Algorithms and Their Characteristics
There are various types of algorithms that realize generative AI. The following are some of the representative ones:
The first is GAN (Generative Adversarial Networks). GAN is an algorithm that uses two neural networks (the generative network and the discriminative network) for training. The generative network learns to produce data that resembles real data, while the discriminative network learns to distinguish between real and generated data. By having these two networks compete with each other, it becomes possible to generate data with higher accuracy. GAN has achieved high performance, particularly in the field of image generation.
The second is VAE (Variational Autoencoder). VAE is an algorithm that extracts the features of data and uses those features to generate new data. Because VAE can learn the latent characteristics of data, it can generate a more diverse set of data compared to GAN.
Points for Selecting Algorithms
When selecting the most suitable algorithm for a generative AI project, it is necessary to consider the project goals, data characteristics, and required accuracy. For example, if high-precision image generation is needed, GAN is suitable, whereas VAE may be more appropriate if diverse data generation is desired.
Steps for Implementing Generative AI
Clarifying Business Goals
To successfully implement generative AI, it is crucial to first clarify business goals.
By specifically determining “what you want to achieve” and “which operations you want to streamline” through generative AI, you can select appropriate use cases and create an effective implementation plan. Additionally, the introduction of AI should not be merely a technical choice; it needs to align with the company’s strategy.
Before implementation, establishing a common understanding among stakeholders and setting goals from a comprehensive perspective that includes resource allocation and risk management can be expected to generate sustainable value post-implementation.
Selecting and Evaluating Use Cases
Based on business goals, consider how to apply generative AI to different operations.
Various use cases can be considered, such as automating customer support, creating marketing content, and developing new products. Each use case should be evaluated for feasibility and cost-effectiveness to select the optimal one.
Furthermore, it is necessary to examine how the selected use cases will impact on-site business processes, including not only labor reduction and speed improvement but also aspects such as quality enhancement and increased customer satisfaction. Finding use cases that contribute to the long-term growth of the company is the first step to success.
Project Planning and Organizational Structure
Based on the selected use cases, develop a specific project plan.
Clarify specific tasks and schedules for necessary data collection, AI model development, system integration, and operational structure building. Additionally, organize a team to drive the project forward, clearly defining roles and responsibilities. Moreover, incorporate strategies in advance for addressing potential risks and challenges that may arise during the course of the project, creating a flexible response system.
It is also important to establish methods of communication with stakeholders and a reporting structure to enhance the transparency of the project and accurately track progress. Furthermore, strengthening collaboration among stakeholders and considering external partners or expert cooperation as needed for the success of the project are crucial.
Additionally, it is essential to prioritize feedback and periodically review the plan to maintain the optimal direction toward achieving the project’s goals. This will lead to the successful implementation of the generative AI project.
Implementation of PoC (Proof of Concept)
Before full-scale implementation, it is recommended to conduct a small-scale PoC to verify feasibility and effectiveness. In the PoC, actual data is used to develop the AI model, and it is evaluated whether the expected results are obtained and whether the implementation effects are promising. Based on the results of the PoC, it may be necessary to revise the implementation plan or reevaluate the use cases.
Gradual Implementation and Effect Verification
If the results of the PoC are favorable, you can proceed to full-scale implementation. However, rather than implementing it company-wide all at once, it is advisable to initially introduce it in a specific department or for certain operations on a trial basis, verifying effectiveness while gradually expanding its use. After implementation, it is crucial to regularly measure effectiveness and, if necessary, to retrain the AI model and improve the system to maximize the effectiveness of generative AI.
Points to Consider in Implementing Generative AI
Ethical Considerations
When utilizing generative AI, it is essential to consider ethical aspects. It is necessary to examine in advance whether the content generated by generative AI is ethically sound and whether there are issues related to copyright and privacy.
Moreover, there is a risk that the use of generative AI could promote bias or discrimination, so it is also important to ensure fairness in the selection of datasets and the design of models. Additionally, clarifying the source of the generated content and its creation process for users is required to enhance transparency and increase trustworthiness. Developing ethical guidelines and raising staff awareness through ongoing education is also part of fulfilling corporate responsibility.
By doing so, it is possible to consider the impact of AI utilization on society and contribute to sustainable development. Furthermore, it is important to accept feedback from users and continue improvements. This will promote the proper use of AI and build trust across society.
Security Measures
The learning data and generated data from generative AI are valuable information assets for companies, so appropriate security measures must be taken.
To prevent data breaches and unauthorized access, it is necessary to implement security software, set access permissions, and establish security policies. Additionally, conducting regular security audits and vulnerability assessments is important to identify system weaknesses and take prompt action. Employees should also receive training to enhance security awareness and deepen their knowledge regarding data handling and password management.
Moreover, reviewing data management processes and implementing encryption or backup as necessary can further strengthen data protection. This will enhance the company’s reliability and build trust from customers and partners.
Continuous Learning and Improvement
The implementation of generative AI is not a one-time event. AI models continuously learn from new data to improve their accuracy. Additionally, it is important to reassess the use cases and implementation methods of generative AI in response to changes in the business environment and customer needs.
Generative AI is a technology that has the potential to bring significant transformation to businesses. Companies can maximize the benefits by understanding the steps for implementing generative AI and the key considerations, while advancing appropriate planning and preparation.
Conclusion
Generative AI holds great potential to significantly contribute to improving corporate productivity.
To ensure successful implementation, it is crucial to set clear objectives, select appropriate use cases, verify through PoC (Proof of Concept), and build a sustainable operational framework. By referencing successful cases, innovation creation can be achieved.
Generative AI is not just a tool; it can be a powerful partner that has the potential to greatly change the future of businesses.