There are lots of questions from many shoppers who want AI conversion (AX). It’s, “What is similar industry An organization?” Whether AX or whatever, the purpose that firms are inquisitive about is that “What did someone in an analogous environment proceed?”
This time, we’ll examine the characteristics of AI application points and projects through evaluation of domestic cases by industry corresponding to finance, retail, healthcare, insurance company, and government. Comparing and analyzing the unique challenge of every industry and the actual strategy that overcomes them, and suggests suggestions for firms to right away use them.
■ Finance: AX centered on security and accuracy
The AI project of the financial industry is the important thing to accuracy and data security. For that reason, it’s needed to thoroughly learn financial data and internal regulations in order that they’ll respond quickly to policy changes. Woori Bank devised a system that explains complex financial products and loan conditions to customers through its own AI Banker. Initially, AI chatbots weren’t satisfied because they might not respond accurately to complex customer queries, but continued to learn internal regulations and financial data to enhance the success rate of counseling to greater than 80%.
Shinhan Bank selected to concentrate on security. With a view to minimize the danger of external outflow of economic data, the corporate has developed its own large language model (LLM) as an alternative of external cloud services and adopts only the inner system. As such, the event of open source models has been a crucial variable within the financial sector’s security -oriented approach. With the appearance of low -cost LLM, which has improved performance, the bank has gained the chance to expand the AI function while maintaining higher levels of security.
▲ Financial Industry AX Core Point
-Important area: Creating AI -based consultation and product suggestion
-Industrial characteristics: Strict data security and regulatory compliance, accuracy
-Strate:
1. Select a model that takes under consideration data security and regulatory compliance as the highest priority
2. Thorough learning and continuous update on internal financial products and regulations
■ Retail: Enhancement of personalization and brand experience
The appliance of the retail industry is evolving through the creation of personalized experience beyond work efficiency. Nike built Naver and AI -based brand chat and provided an internet customized service that appeared to be consulted with a store worker. AI grasped the particular demands of shoppers and detailed characteristics of the product, and the promoting click rate (CTR) increased by about 20%. Within the early days, AI had an issue that used vocabulary that didn’t fit the brand identity, but overcame it with answer rules and internal tests that reflect Nike’s tone and manner.
AMOREPACIFIC also developed ‘AI Beauty Counselor (AIBC)’ in cooperation with Microsoft. Online, now we have established a system that gives customer skin condition diagnosis to personalized products. Within the early days, there was an incorrect suggestion problem because of the dearth of natural language understanding and the dearth of name knowledge, however the dialogue management module was devised by conducting additional learning with its own collected data.
▲ Retail industry AX key point
-Important area: Personalization of shoppers apart from business efficiency
-Industrial characteristics: Personalization demand, brand identity
-Strate:
1. The technique of accurately learning brand identity and tone and manners to AI
2. Phase of personalization using customer behavior data
■ Healthcare: Balance of accuracy and private information protection
Within the healthcare industry, the balance of security and accuracy considering the personalization of customer in addition to the high sensitivity of medical data is essential. The AI -based health care application ‘KGC Ginseng Corporation’, KGC Ginseng Corp., has led to August 2024, however it continues to be a big insight as a pioneering example of medical data utilization.
The gene and health check -up data were analyzed as AI to supply personalized health care plans and risk prediction services for disease development. In response to the strict regulations and data sensitivity of the medical field, Jeong Kwan -jang introduced an intensive encryption system by establishing a database proven with clinical experts. Within the early days, there was an evaluation accuracy issue because of data integration and quality management, but the standard was increased by standardizing data and noise data filtering.
Meanwhile, Kakao Healthcare’s blood sugar management service ‘Pasta’, which is rapidly growing within the healthcare area, has caught each medical accuracy and private information protection in the applying of AI by securing about 20%of the market share within the domestic CGM market. Kakao Healthcare cooperated with greater than 180 medical institutions nationwide to link the electronic medical record system (EMR) to supply customized blood glucose management pattern care services and guide high accuracy medical information.
▲ Healthcare Industry AX Core Point
-Important area: personalized health care and disease prediction, chronic disease management
-Industrial characteristics: data sensitivity, medical accuracy and scientific reliability, regulation compliance
-Strate:
1. Create a collaboration system with medical professionals and medical institutions
2. Thorough encryption to balance personal information protection and use
3. Data standardization and verification, feedback process
■ Insurance: Precise risk evaluation and customization
The insurance industry is considered one of the fastest areas of AI technology, mainly specializing in skilled screening and customised product design. KB Insurance has implemented the designer’s actual face and voice as AI, and DB Insurance has a suggestion system for the optimal insurance product based on customer data as an AI insurance assistant.
Within the early days of the introduction of the AI, the corporate faced problems corresponding to lack of knowledge matching and backlash from insurance agent, however it was established a dedicated AI data organization and constantly communicated with the designer to prove its performance through AI and reduce internal resistance. Within the case of the insurance industry, AI effectively analyzes the vast customer data and sophisticated product structure, greatly increasing the design expertise and consistency.
▲ Insurance Industry AX Core Point
-Important area: Advisable for contract review and personalized product suggestion
-Industrial characteristics: Use of vast data, skilled decision -making required
-Strate:
1. Initial cooperation relationship with internal designers and agents
2. Composition of dedicated organizations to secure data consistency
■ Government and public: Strengthening reliability and accessibility
In the federal government and public sectors, the introduction of AI is comparatively initial than other countries corresponding to the US. But additionally it is true that it’s developing rapidly recently.
Above all, system reliability and public access are a key element. The IRS’s AI counseling service is an example of this characteristic. The IRS introduced AI counseling solutions to resolve the repeated inquiries of the repeated tax return season, which greatly increased the success rate of counseling from 24%to 98%by automating easy repetitive tax inquiries.
Specifically, because of the character of presidency agencies, data security is completely vital, so the IRS operated the AI system only in an internal cloud environment that blocked external connections. Within the early days, there was an absence of response to exceptional questions, however it worked with a hybrid model that mixes actual counselors and AI. Basically, government agencies’ AI projects are aimed toward upgrading the standard of the general public service, efficiency of labor, and securing access to service that every one residents can use without discrimination.
▲ AX core points in government and public sectors
-Important areas: automation of grievance processing and providing information
-Industrial characteristics: Regulatory compliance, service transparency and user accessibility
-Strate:
1. Design that may be operated effectively within the closed network environment
2. Construct a hybrid model of AI and human counselors
3. Designed user interface considering the accessibility of varied classes
Within the AX course, each industry faces amazingly similar challenges. Firms at the moment are not simply combining AI technology, but should closely analyze how AI can effectively apply in a novel business environment. And as vital as the small print of every industry, the combination, quality control, and consolidation of knowledge will likely be shown. That is the fundamentals for AX success, and it is necessary to know that any industry is a necessary element.
Ahn Chan -bong, CEO of Talent
