생성AI의 2024 기업 활용: 3가지 핵심 포인트

생성AI의 2024 기업 활용: 3가지 핵심 포인트

 

 

 

Point #1: Multimodal Applications

In 2024, the utilization of multimodal AI in various industries is anticipated to significantly enhance productivity and efficiency. This technology, which integrates text, images, audio, and video, offers several advantages:

1. Manufacturing Industry: Multimodal AI can revolutionize the manufacturing sector by providing more efficient ways to interact with complex machinery and processes. For example, workers could use voice commands to control machines while simultaneously receiving visual feedback. This can lead to quicker problem-solving, faster adaptation to new production lines, and more personalized training methods.
2. Engineering Sector: In engineering, multimodal AI can facilitate the interaction with detailed schematics and blueprints. Engineers could use voice or gesture controls to manipulate 3D models, allowing for a more intuitive design process. It can also aid in virtual prototyping, where engineers can test and modify designs in a virtual environment before actual construction, saving time and resources.
3. Healthcare Field: The healthcare industry could see significant improvements through multimodal AI, especially in genomics and medical imaging. Doctors and researchers could interact with complex genomic data using natural language processing, making it easier to identify patterns and anomalies. In medical imaging, combining visual data with AI-driven analysis could lead to quicker and more accurate diagnoses.
4. Training and Education: Multimodal AI can also transform training and educational methodologies. Interactive AI tutors that respond to voice, text, and visual input can provide a more engaging and personalized learning experience. This is particularly relevant in fields requiring hands-on training, where AI can simulate real-world scenarios.
5. Customer Service and Support: In customer service, multimodal AI can provide more engaging and efficient support. For instance, AI could analyze a customer's tone of voice, facial expressions, and verbal inquiries to offer more tailored assistance, enhancing customer satisfaction and reducing resolution time.
6. Accessibility: This technology can make digital content more accessible, especially for people with disabilities. For example, an AI that converts text to speech or interprets sign language can break down communication barriers.

In summary, the adoption of multimodal AI in 2024 is expected to revolutionize how we interact with technology, making it more intuitive and efficient. This will not only boost productivity in various industries but also pave the way for innovative approaches to problem-solving and learning.

 

 

Point #2: Adoption Lagging

 

In 2024, the landscape of generative AI utilization in enterprises is expected to exhibit a notable lag, especially when compared to its adoption in other sectors or by smaller businesses. This slower adoption rate in larger enterprises can be attributed to several key challenges:

1. Data Privacy and Security Concerns: Large enterprises often deal with sensitive data, and the use of generative AI raises significant privacy concerns. The risk of data breaches, misuse of data, and compliance with regulations like GDPR and HIPAA are major deterrents. Enterprises are cautious about integrating AI technologies that require access to large datasets, fearing the potential for data leaks or misuse.
2. Lack of Interpretability and Transparency: Many generative AI models, particularly those based on deep learning, are often referred to as "black boxes" due to their lack of interpretability. Enterprises, especially those in regulated industries, require a clear understanding of how decisions are made by AI systems. The inability to fully explain AI decisions can be a barrier to adoption, as it poses risks in terms of accountability and compliance.
3. Scalability Issues: While generative AI shows great promise in smaller-scale applications, scaling these solutions to meet the demands of large enterprises can be challenging. This includes not only the technical scalability of AI models but also the integration with existing enterprise systems and workflows. Enterprises often have complex, established systems, and integrating AI solutions that can operate at this scale and complexity is a significant challenge.
4. Application Focus: In 2024, generative AI is expected to be primarily used for content creation applications, such as marketing material generation, personalized customer communications, or creative design. These applications, while valuable, represent a narrower scope of AI's potential. The utilization of generative AI for more complex operations like manufacturing or supply chain management is lagging, possibly due to the aforementioned challenges and the need for more specialized, industry-specific AI solutions. ["Appendix: 관련된 다른 시각" 참고]
5. Cost and ROI Considerations: Implementing AI solutions in large enterprises requires significant investment not only in technology but also in training and change management. Enterprises need to justify this investment with a clear return on investment (ROI), which can be difficult to ascertain for AI projects, especially when long-term benefits are not immediately evident.
6. Regulatory and Ethical Considerations: Large enterprises must navigate a complex web of regulations and ethical considerations, particularly when adopting new technologies like AI. Concerns around bias, fairness, and the ethical use of AI are particularly acute in sectors like finance, healthcare, and public services.

In conclusion, while generative AI offers significant potential for enhancing productivity and innovation in enterprises, its adoption in 2024 is expected to be slower, particularly for complex operations. This is due to challenges related to data privacy, interpretability, scalability, focus on content creation, cost considerations, and regulatory and ethical issues. Addressing these challenges will be key to accelerating enterprise adoption of generative AI technologies.

 

 

Point #3: Steps To Take

 

To ensure generative AI is ready for enterprise use in 2024, several crucial steps must be taken. These steps address current limitations and lay the groundwork for more extensive and effective utilization within enterprises:

1. Anonymizing Sensitive Business Data: Protecting sensitive information is paramount in enterprise settings. Generative AI systems must be developed with robust mechanisms to anonymize and secure data, ensuring that all AI interactions and training processes maintain the highest standards of confidentiality and compliance. This involves developing advanced encryption methods, secure data storage and access protocols, and strict adherence to data protection regulations.
2. Customization for Diverse Enterprise Needs: Enterprises have varied and specific needs depending on their industry, size, and operational focus. Generative AI must be highly customizable to cater to these diverse requirements. This customization may involve industry-specific model training, adaptable interfaces, and the ability to integrate seamlessly with existing enterprise systems and workflows.
3. Partnerships with Vendors and Market Data Providers: Collaborations with external vendors and data providers are crucial for enriching the AI's understanding and capabilities. These partnerships can provide access to a wider range of data, insights, and expertise, which can enhance the AI's performance, accuracy, and relevance to specific enterprise contexts.
4. Development and Testing: Generative AI requires extensive development and testing, particularly in complex and variable enterprise environments. This involves rigorous testing for accuracy, reliability, and performance under various conditions, as well as ensuring the AI's outputs meet enterprise standards and objectives.
5. Refinement and Iterative Improvement: Given the rapid evolution of AI technology, continuous refinement and updates are necessary. Enterprises will need AI solutions that can adapt and evolve with changing market conditions, technological advancements, and enterprise needs. This requires a commitment to ongoing research, development, and investment in AI capabilities.
6. Compliance and Ethical Considerations: Ensuring compliance with regulatory requirements and addressing ethical considerations is critical. AI systems must be developed with a focus on ethical AI practices, including considerations around bias, fairness, and transparency, and must adhere to industry-specific regulations.
7. Scalability and Integration: The AI solutions must be scalable to handle large volumes of data and transactions typical of large enterprises. They also need to be able to integrate smoothly with existing enterprise infrastructure, including databases, CRM systems, and other operational tools.
8. User Training and Support: Enterprises will require comprehensive training programs for their staff to effectively use and manage AI systems. Additionally, ongoing support and troubleshooting will be essential to ensure smooth operation and quick resolution of any issues.

In summary, making generative AI enterprise-ready by 2024 involves significant advancements in data security, customization, partnerships, and continuous development. Given these requirements and the current state of AI technology, it may indeed take several years for generative AI to be fully integrated and optimized for enterprise use.

 

 

 

 

[Appendix: 관련된 다른 시각]

 

The 2024 outlook for generative AI (GAI) in enterprise settings presents a complex picture, especially regarding its application focus. While GAI is expected to be predominantly used for content creation applications such as marketing material generation, personalized customer communications, and creative design, its utilization for more intricate operations like manufacturing or supply chain management appears to be developing at a slower pace.

According to G2's analysis, GAI adoption in enterprises will continue to lag behind other segments in 2024, with its main application being in content creation rather than in complex operations like manufacturing or supply chain management. This trend is observed despite GAI's broad potential, which encompasses a range of technologies including AI chatbots and large language models (LLMs) capable of creating text, images, videos, and even code. The primary use cases for enterprise GAI are content and writing, coding and programming, business communication, and reporting. These findings underscore the limited scope of GAI applications within larger organizations at present, focusing mainly on content generation rather than on more complex operational tasks​​.

However, there are indications that GAI is beginning to make inroads into more complex domains like manufacturing and supply chain management. AWS, for instance, highlights the transformative potential of generative AI in manufacturing, with engineers able to analyze large datasets to improve safety, create simulation datasets, and expedite bringing products to market. Moreover, AWS's introduction of Amazon Bedrock, a service that makes foundational models (FMs) accessible via an API, demonstrates efforts to customize and scale GAI for specific tasks, including in manufacturing contexts. This approach not only keeps data private and secure but also enables effective use of GAI in production environments. In these scenarios, generative AI shows promise for enhancing overall equipment effectiveness (OEE) in factory settings and aiding in capturing and digitizing historical data to compensate for the loss of experienced workers​​.

In summary, while GAI in 2024 is expected to be primarily utilized for content creation applications in enterprise settings, there are emerging trends and efforts underway to extend its application to more complex operations like manufacturing and supply chain management. The current focus on content creation reflects both the immediate applicability and the relative ease of integrating GAI into these areas. However, as technological advancements continue and challenges around data privacy, interpretability, and scalability are addressed, we can anticipate a broader application of GAI in various industries, including more complex and data-intensive domains.

 

 

Ending Note ::

2023년 이미 기업들이 생성AI의 업무 활용 가치에 대해서는 눈을 뜬 상태로 마감되었다고 본다면 2024년에는 어떻게 업무에 본격적으로 적용할 것인가를 다양한 업무를 대상으로 다양한 방식으로 실험하는 시기가 될 것으로 전망할 수 있다. 2023년 중 ChatGPT가 보여준 빠르고 혁신적인 완성도 제고와 다양한 기능의 통합을 참고한다면 2024년 이러한 발전 속도가 지속될 것이라 예상할 수 있으나, 이 과정에서 OpenAI, Microsoft, Google, Amazon 등 주요 공급자들간의 경쟁이 어떤 양상으로 전개되는가에 따라 기업이 활용할 수 있는 현실적인 영역의 범위가 정해질 수 있을 것이다. (AWS는 이 과정에서 매우 중요한 역할을 할 가능성이 높아 보인다.)

보안이나 안정성, 정확성 등 이미 드러난 여러가지 이슈들을 공급자들이 얼마나 잘, 그리고 빠르게 해결해줄 수 있는가가 기업들의 행보에 결정적인 요소가 될 것이며, 기업의 ROI 관점에서는 총소유비용(Total Cost of Ownership, TOC)에 대한 논의가 본격화될 것으로 전망할 수 있을 것이다.