Enterprise Generative AI Strategy for 2024: Outlook and Considerations

Enterprise Generative AI Strategy for 2024: Outlook and Considerations

 

 

 

Image in a minimalistic American pop art style, showing a group of AI robots collaboratively working in a large, open office space. Each robot is busy with a specific corporate task, mirroring different departments: marketing, strategic planning, sales management, manufacturing, and logistics. The marketing robot is analyzing data, the strategy robot is outlining plans, the sales robot is engaging in client interactions, the manufacturing robot is handling machinery, and the logistics robot is organizing deliveries. The image captures their individual activities within a shared workspace, emphasizing a sense of collaboration. The style is characterized by bright, bold colors and simple, clear lines, typical of American pop art, without any text.

 

Looking Back 2023

 

In 2023, generative AI technologies, particularly ChatGPT, have significantly impacted businesses from a strategic perspective in multiple ways:

1. Enhanced Business Operations and Services: The adoption of generative AI in business functions has been notably high among organizations that achieve significant value from AI. These companies, often termed as AI high performers, use generative AI extensively in areas like product and service development, risk, and supply chain management. These organizations focus less on cost reduction and more on creating new businesses or revenue sources using AI. This shift indicates a strategic pivot towards leveraging AI for expanding business offerings and enhancing existing ones​​.
2. Efficiency and Effectiveness Gains: Generative AI tools have shown the potential to deliver substantial gains in efficiency and effectiveness, particularly in customer service, marketing content, software development, field services, and engineering. For instance, in the banking sector, a call center achieved a near 50% reduction in customer service consultation duration using generative AI tools. The integration of generative AI with predictive AI and proprietary data has also shown promising results in enhancing business operations and customer experiences​​.
3. Impact on the Economy and Workforce: ChatGPT and similar technologies have been identified as potential drivers for economic growth, with predictions of adding trillions of dollars in economic value. The technology is not just seen as a tool for automating tasks but also as a means to enhance the capabilities of the workforce and improve productivity. This perspective aligns with the view of businesses seeking to use AI for expanding offerings and improving workforce productivity, rather than just replacing human labor​​.
4. Widespread Adoption and Cost Savings: ChatGPT, in particular, has seen wide adoption in businesses, with significant percentages of companies using it for tasks like coding, content creation, customer support, and business documentation. The use of ChatGPT has led to considerable cost savings for companies, with a substantial number reporting savings of over $50,000. This widespread adoption and the resultant financial impact underscore the strategic importance of generative AI in the business world​​.
5. Strategic Use in Various Industries: Different industries have been leveraging generative AI and ChatGPT to overcome specific challenges and innovate. In the manufacturing industry, for instance, these technologies are used to optimize production processes, improve product design, and even create 3D models, reducing the need for physical prototypes. This demonstrates the versatility of generative AI in providing strategic solutions across various sectors​​.
6. Potential Risks and Challenges: While the benefits of generative AI are significant, they come with risks such as inaccuracy, cybersecurity threats, and intellectual-property infringement. These risks highlight the importance of cautious and strategic adoption of AI technologies to ensure sustainable and secure integration into business operations​​.

In conclusion, generative AI and ChatGPT have made a profound impact on businesses in 2023, offering strategic advantages in terms of operational efficiency, cost savings, and innovation across various industries. However, the successful and sustainable adoption of these technologies requires a balanced approach that considers potential risks and challenges.

 

 

이 데이터를 분석하여 파악할 수 있는 핵심적인 사실 다섯 가지는 다음과 같습니다:

1. "ChatGPT" 관심도의 급증: "chatgpt_us" 관련 검색 추세가 시간이 지남에 따라 급격히 증가한 것으로 보입니다. 이는 ChatGPT에 대한 대중적인 관심과 인지도가 상당히 높아졌음을 나타냅니다. 특히 특정 시점에서의 급격한 증가는 특정 이벤트나 발표가 있었음을 시사할 수 있습니다.
2. "Enterprise AI"의 동향: "enterprise_ai_us" 항목도 일정한 관심을 받고 있으나, "chatgpt_us"에 비해 상대적으로 변동성이 적은 것으로 보입니다. 이는 엔터프라이즈 AI 분야가 일정한 수준의 지속적인 관심을 받고 있지만, ChatGPT와 같은 특정 기술이나 제품에 대한 관심 폭발에는 미치지 못한다는 것을 의미할 수 있습니다.
3. 지역별 관심도 차이: "chatgpt_us"와 "enterprise_ai_us"의 추세를 비교해보면, 미국 내에서 ChatGPT에 대한 관심이 특히 높은 것으로 보입니다. 이는 지역별 기술 수용도나 관심사의 차이를 나타내며, 미국 시장에서 ChatGPT와 같은 기술이 특히 주목받고 있음을 시사합니다.
4. 세계적인 관심의 확산: "chatgpt_world"와 "AI_world"의 추세를 살펴보면, 이들 기술에 대한 전 세계적인 관심도 역시 증가하고 있음을 알 수 있습니다. 이는 ChatGPT 및 AI 기술이 전 세계적으로 영향력을 끼치고 있으며, 다양한 지역에서 이 기술에 대한 관심이 높아지고 있음을 나타냅니다.
5. AI 기술의 대중화: 전반적으로, 이 데이터는 AI와 관련된 기술, 특히 ChatGPT의 대중화와 관심 증가를 나타냅니다. 이러한 추세는 AI 기술이 사회와 산업 전반에 걸쳐 점점 더 중요한 역할을 하고 있음을 보여줍니다. 이는 기업, 개발자, 연구자들에게 새로운 기회와 도전을 제시할 수 있습니다.

 

구글트렌즈를 통한 관심도 변화 추이에서는 2023년 2월~5월 미국 및 전세계에서의 급격한 관심 폭등은 일반 대중 전체가 주도한 것이라면 기업들의 관심은 이와는 별개로 꾸준히 2023년 전체에 걸쳐 증가해 왔음이 드러난다. 2023년 후반에는 미국의 경우 이미 기업들의 관심이 정점(plateau)에 근접해 있는 것이 아닌가를 생각해 보게 한다. 이는 기업의 AI 본격 활용은 당연한 사실로 받아들이는 단계에 진입했다는 의미가 될 수 도 있을 것이다.

 

 

 

엔터프라이즈 AI에 대한 관심 추이는 미국에서와 전세계에서 크게 차이가 나지는 않는다. 다만, OpenAI가 ChatGPT Enterprise를 런칭한 8월말 시점에 미국에서 더 큰 관심을 끈 것이 드러나며, 전세계의 관심이 연말까지 지속 증가하는 것과 미국에서의 관심이 2023년 4Q에 다소 정체되는 패턴을 보이는 것은 차이점이다.

 

 

As of 2023, more than 92% of Fortune 500 companies are using ChatGPT and similar generative AI platforms. This represents a significant increase in adoption, with the figure standing at 80% in August of the same year. These companies span various industries, including financial services, legal applications, and education, highlighting the broad applicability and growing reliance on AI technologies in the corporate world​ ( https://aiwithphil.com/2023/11/fortune-500-companies-embrace-chatgpt/ ).

 

 

In 2023, generative AI and ChatGPT have been applied across various industries with significant impacts and results:

1. Microsoft: Search and Software Integration Microsoft has integrated advanced language models, like GPT-3 and GPT-4, into its Bing search engine, offering users a more conversational search experience. Additionally, Microsoft plans to incorporate ChatGPT into cornerstone software like Word and Excel, aiming to enhance productivity and user experience​​.
2. Duolingo: Language Learning Duolingo uses GPT-4 in its language learning app, providing feedback akin to interacting with a seasoned tutor. This integration offers an immersive learning experience with AI personas, enhancing the language learning process for users​​.
3. Coca Cola: Marketing and Personalization Coca Cola collaborates with Bain & Company to use ChatGPT and Dall-E for creating personalized ads and marketing content. This approach aims to establish more profound connections with customers through tailored experiences​​.
4. Snap Inc: Social Messaging Snap Inc has introduced "My AI" in Snapchat, an AI-powered feature to enhance user interactions. This tool provides various types of assistance and adds a richer dimension to social messaging​​.
5. Slack: Workplace Productivity Slack integrates ChatGPT to streamline team communications and workflows, effectively acting as an AI assistant within the digital workspace. This enhances team collaboration and productivity​​.
6. Octopus Energy: Customer Service Octopus Energy uses ChatGPT in its customer service operations, resulting in more efficient handling of inquiries and improved customer satisfaction​​.
7. Cheggmate: Student Support Cheggmate leverages ChatGPT for providing 24/7 assistance to students, helping with academic queries and assignments, thereby enhancing the educational experience​​.
8. Freshworks: Software Development Freshworks utilizes ChatGPT in software development, significantly reducing project timelines and maintaining a competitive edge in technology solutions​​.
9. American Express: Customer Marketing American Express employs AI-powered chatbots in SMS campaigns, leading to increased customer engagement and more effective marketing strategies​​.
10. Healthcare Applications In healthcare, generative AI assists in patient data analysis, conducting satisfaction surveys, managing appointments and medications, and providing remote monitoring and counseling through chatbots. This integration aids healthcare professionals in making more informed decisions and improves patient care services​​.
11. Finance and Banking Sector AI chatbots in finance offer personalized financial planning, tax preparation, real-time reporting, and investment advice. In banking, these chatbots handle customer queries, manage transactions, automate KYC and AML processes, and enhance wealth management services​​.
12. Ecommerce In eCommerce, AI chatbots track orders, provide shipping information, assist in the return process, and improve the overall customer experience​​.
13. SEO and Online Content Generative AI aids in discovering long-tail keywords, creating SEO-friendly content, improving website ranking potential, and optimizing the website's UI/UX​​.

Overall, the impact of generative AI and ChatGPT in 2023 has been widespread, revolutionizing operations, enhancing customer experiences, and fostering innovation across different industries. These technologies have not only streamlined processes but also opened up new avenues for creative and personalized customer engagement.

 

 

In 2023, various companies have innovatively applied generative AI and ChatGPT across different sectors, demonstrating a range of practical applications and benefits. Here are some notable examples:

1. Expedia: Expedia has integrated conversational AI into its travel planning services, allowing customers to plan vacations through a chat interface that simulates interaction with a knowledgeable travel agent. This has enhanced the customer experience by providing personalized travel planning assistance​​.
2. Microsoft: Microsoft has incorporated ChatGPT into its Bing search engine, transforming the search experience into a more dynamic and conversational one. Beyond search, Microsoft plans to integrate ChatGPT into core software products like Word and Excel to enhance productivity and user experience​​​​.
3. Duolingo: Duolingo utilizes GPT-4 for its language learning app, Duolingo Max, offering feedback akin to conversing with a tutor. This makes language learning more interactive and immersive​​​​.
4. Coca Cola: In collaboration with Bain & Company, Coca Cola is leveraging ChatGPT and Dall-E to create personalized marketing content. This approach aims to form deeper connections with consumers through tailor-made advertising experiences​​​​.
5. Snap Inc: Snap Inc has enhanced its Snapchat app with "My AI," an AI-powered feature for social messaging, offering users assistance, advice, and entertainment in their conversations​​​​.
6. Slack: Slack has introduced a ChatGPT-powered AI assistant to streamline team communications and workflows, effectively improving productivity and collaboration within teams​​​​.
7. Octopus Energy: This UK energy company has integrated ChatGPT into its customer service operations, leading to faster and more efficient responses to customer inquiries, thereby enhancing customer satisfaction​​​​.
8. Cheggmate: ChatGPT has been implemented to provide AI assistance for student support at Cheggmate, offering 24/7 help with academic questions and assignments, thus enriching the educational experience for students​​​​.
9. Freshworks: Freshworks has utilized ChatGPT in software development, significantly accelerating the pace of creating complex software applications​​​​.
10. American Express: American Express has adopted AI-powered chatbots in their customer marketing strategies, notably in their SMS campaigns, to enhance customer engagement and offer tailored recommendations and reminders​​​​.

These examples illustrate the diverse and impactful ways in which generative AI and ChatGPT are being utilized in various industries. From enhancing customer experience and streamlining operations to boosting marketing efforts and aiding in education, the applications are broad and transformative. These advancements underscore the potential of AI to augment human capabilities and create innovative solutions in business and beyond.

 

 

here are 10 more examples of how companies are innovatively using generative AI and ChatGPT:

1. Photoshop: Adobe has integrated generative AI in Photoshop through "Generative Fill", enabling users to create, add, remove, or replace images using simple text prompts.
2. Duolingo Max: Duolingo Max offers new exercises and features powered by the latest generative AI technology, enhancing language learning efficiency.
3. ChatGPT Plus: ChatGPT Plus, a subscription plan, provides users with faster response times and access to new features, including ChatGPT plugins and web browsing.
4. Zobot: Zoho's Zobot, a chatbot platform, uses generative AI for creating codeless chatbots, streamlining customer engagement and support.
5. Einstein GPT: Salesforce's Einstein GPT, integrated within Salesforce Data Cloud, leverages AI to generate email responses, content, and customer insights.
6. Midjourney: Midjourney is a generative AI tool for creating astonishing images based on text prompts, available in various subscription plans.
7. SubPage: SubPage utilizes AI for building website pages quickly, enabling non-tech staff to easily manage website layouts and content.
8. BacklinkGPT: BacklinkGPT aids in backlink outreach by generating personalized emails and managing outreach campaigns more effectively.
9. WAGPT: This tool integrates ChatGPT’s intelligence within WhatsApp, enhancing conversations with rapid insights and personalized experiences.
10. Monica: Monica, a GPT-4 AI Copilot, assists users in writing articles, summarizing content, and enhancing internet search capabilities.

These examples illustrate the diverse applications of generative AI across different industries, from enhancing language learning and digital art creation to streamlining marketing and customer service operations​​.

 

In 2023, several retail companies are leveraging generative AI and ChatGPT to enhance various aspects of their business. Here are ten specific examples:

1. Carrefour: The French grocery chain Carrefour has utilized ChatGPT to create AI-driven FAQ videos. In February 2023, they generated a video featuring a human avatar, powered by ChatGPT, to answer common customer questions about eating better for less cost.
2. JD.com: One of China's largest online retailers, JD.com, announced plans to launch a tool similar to ChatGPT, specifically tailored for the retail and finance sector. This tool, ChatJD, focuses on content generation, man-machine dialogue, understanding user intent, information extraction, and sentiment analysis.
3. Generative AI in Retail: Retailers are using generative AI for personalized customer experiences, inventory management, and marketing strategies. These applications not only enhance efficiency but also foster a deeper connection with consumers.
4. Superior Customer Support: Generative AI, like ChatGPT, is being used to improve customer support in retail. AI-powered chatbots help customers with tracking orders, changing shipping details, reordering, and finding physical store locations.
5. Personalized Recommendations and Promotions: ChatGPT can analyze customer data to provide personalized product recommendations and promotions, helping to improve the shopping experience and increase sales.
6. Content Generation for Social Media: Retailers are utilizing ChatGPT to generate engaging content for social media channels. This includes writing captions, posts, and composing responses to follower messages and tags.
7. Proofreading and Translating Customer Communications: ChatGPT aids in proofreading text, correcting grammatical errors, and translating communications into multiple languages, thus enhancing the quality of customer interactions.
8. Fraud Detection: ChatGPT analyzes customer behavior and transactions to identify potential fraud, helping eCommerce businesses protect their customers' personal and financial information.
9. Writing Product Descriptions: eCommerce retailers use generative AI to create search engine-optimized and customer-appealing product descriptions, improving SEO and driving more traffic to their online shops.
10. Voice Assistance Integration: Retailers are integrating ChatGPT with voice assistants like Amazon Alexa and Google Assistant to provide voice-enabled shopping experiences, making shopping more accessible and convenient.

These examples demonstrate the diverse and innovative ways in which retail companies are adopting generative AI and ChatGPT to improve customer experience, enhance operational efficiencies, and drive business growth

 

 

In 2023, several manufacturing companies have been leveraging generative AI and ChatGPT technologies to innovate and enhance their operations. Here are ten specific examples illustrating this trend:

1. Automotive Supplier (Unnamed): An automotive supplier utilized AI to boost productivity by 21%. They implemented an AI-powered scrap adviser, reducing scrap rates by 25%, and a pump health monitor, which almost eliminated breakdowns of a critical production pump. Additionally, an AI-driven visual quality inspection system was introduced, which reduced the need for quality control staff by 65% while increasing inspection accuracy​​.
2. Capgemini Engineering: Capgemini Engineering is incorporating AI into real products. They developed tools like a code assistant to aid developers and an internal communication system similar to WeChat. Their collaboration with Microsoft provided them early access to OpenAI’s API, integrating generative AI into various roles like engineering and design​​.
3. Siemens: Siemens introduced the "Industrial Copilot" at the SPS trade show, an AI-based digital assistant akin to ChatGPT for factory workers. This tool aids in generating PLC code through natural language input and supports factory workers in tasks like machine operation and fault diagnosis​​.
4. Rockwell Automation: Collaborating with Microsoft, Rockwell Automation is developing a ChatGPT-inspired Co-Pilot for Automation. Integrated with Azure OpenAI Service, it assists in generating code using natural language prompts, aiming to automate routine tasks and improve design efficiency in industrial automation systems​​.
5. Thales Digital Factory: Thales Digital Factory created technological tools, including a code assistant and an internal communication platform. They also work with OpenAI’s API through a partnership with Microsoft, focusing on enhancing the creativity of individuals working for Thales​​.
6. Texas Instruments: Texas Instruments significantly shifted programming approaches with their C-based digital signal processor compiler, which could consistently create more efficient code than humans. This advancement in generative AI has led to a major shift in creative processes, from directing computers on "how" to do something to telling them "what" you want done, such as code generation and design tasks​​.
7. CNET: CNET's use of generative AI for writing articles highlighted the importance of human oversight in the AI creative process. While AI can produce content, it requires human review to ensure accuracy and relevance, underscoring the complementary role of AI and human expertise​​.
8. Microsoft: Microsoft, with its focus on AI and emerging technologies, discusses the coevolution of AI and human abilities. Their approach includes the use of natural language processing, allowing non-experts to interact with digital twins and other AI systems more intuitively​​.
9. Capgemini Americas: Capgemini's research indicates that 48% of manufacturing executives see generative AI significantly disrupting their business models. The company is piloting innovative design and predictive maintenance applications that can reduce overhead costs and expensive downtime​​.
10. High-Tech and Industrial-Manufacturing Sectors: These sectors are particularly well-positioned to capitalize on generative AI. They have a history of AI adoption and are enthusiastic about the potential of generative AI, particularly in optimizing part design, advanced 3D modeling, and predictive maintenance​​.

These examples showcase the diverse applications of generative AI in the manufacturing sector, from enhancing productivity and operational efficiency to innovative design and predictive maintenance. The integration of AI in manufacturing is not only optimizing current processes but also opening up new possibilities for innovation and creative problem-solving.

 

 

While many companies have successfully implemented AI and ChatGPT in various domains, some have faced significant challenges and setbacks. Here are 10 examples illustrating these challenges:

1. Creativity Hindrance: Some marketers fear that generative AI like ChatGPT might stifle creativity. 64% believe AI could alter or hamper creativity, although 36% see potential for boosting creativity​​.
2. Over-Reliance: There's a concern about becoming too dependent on AI tools, with 67% of marketers suggesting caution against over-reliance​​.
3. Time Commitment: Properly prompting AI requires time and skill. Learning to use AI effectively adds another task for already busy professionals​​.
4. Establishing Working Processes: Implementing AI necessitates the creation of new processes, which can be time-consuming and challenging, especially in ensuring consistency and eliminating bias​​.
5. Inaccurate Information: Nearly half of the marketers surveyed have received incorrect information from generative AI, raising concerns about brand reputation and consumer relations​​.
6. Poor Quality Content: Despite AI's ability to assist in content creation, there are concerns about the quality of the output. This challenge underlines the importance of human oversight in AI-generated content​​.
7. Privacy and Data Analysis: AI's role in analyzing customer data brings challenges related to privacy and handling sensitive information​​.
8. Job Replacement Fears: Although AI is unlikely to replace marketing teams completely, there is an ongoing fear about job security related to AI implementation​​.
9. AI Bias: AI models can inherit biases from their training data, posing a challenge in ensuring fairness and accuracy in their outputs​​.
10. Keeping Up with Trends: The fast-paced evolution of AI technology can be overwhelming for marketers trying to stay current with the latest developments​​.

These challenges highlight the complexity of implementing AI and ChatGPT in business contexts. While AI offers substantial benefits, its successful integration requires careful consideration of its potential drawbacks, particularly in terms of creativity, reliance, accuracy, and ethical use.

 

 

dall-e3 prompt :  Image of a middle-aged Korean man, who is a corporate strategist, deeply engrossed in reviewing plans for next year's business. He has a slim build and is wearing glasses, reflecting a very cautious and thoughtful demeanor. He is dressed in a blue shirt and casual attire, symbolizing a blend of professionalism and approachability. The setting is an office, with visible elements like a computer, documents, and possibly a whiteboard in the background, emphasizing the strategic planning aspect of his work. The overall composition is wide, capturing the intense focus and dedication of the strategist in his professional environment.

 

 

Foreseeing 2024

 

The outlook for enterprise application of generative AI in 2024 is shaped by several key trends and challenges, as identified by industry experts and analysts.

1. Multimodal AI and Increased Productivity: In 2024, multimodal AI, which enables interactions beyond text to include images, audio, and video, is expected to bring a new level of productivity to various industries. This evolution will be particularly impactful in sectors like manufacturing, engineering, and healthcare, where interaction with schematics, blueprints, or genomics can become more conversational and intuitive​​.
2. Open Source AI and Regulatory Challenges: Open source AI is driving broader adoption of generative AI. Organizations are looking to leverage open source large language models for control, transparency, and customization. However, regulatory challenges, particularly in terms of data privacy and security, could slow down adoption. The focus on responsible AI, with standardization of protocols and best practices, is expected to gain momentum in 2024, addressing governance, safety, security, and trust​​.
3. National and Global AI Regulation: There is an anticipation of clear laws governing AI at both national and global levels. Enterprises will need to show agility and adaptability as they balance productivity and efficiency gains with compliance and security concerns. Violations of new regulations, security breaches, and other issues related to generative AI may expose organizations, particularly those lacking proper policies or overstepping regulatory boundaries​​.
4. Enterprise Adoption Lagging: While businesses of all sizes will use generative AI, enterprise adoption is expected to lag behind other segments. In 2024, generative AI is predicted to be mainly used for content creation rather than complex operations like manufacturing or supply chain. This lag is attributed to challenges such as data privacy concerns, lack of interpretability and transparency in AI models, and scalability issues​​.
5. Requirements for Generative AI to be Enterprise-Ready: To make generative AI ready for enterprise use, efforts are needed in terms of anonymizing sensitive business data, customization to meet diverse enterprise requirements, and partnerships with other vendors and market data providers. Although generative AI holds immense promise, its current limitations and the need for extensive development, testing, and refinement indicate it may take years to be fully enterprise-ready​​.

Overall, while generative AI continues to evolve rapidly and holds significant potential for transforming various industries, enterprises face a complex landscape of regulatory challenges, adoption hurdles, and the need for responsible AI practices. The focus in 2024 will likely be on addressing these challenges to harness the full potential of generative AI in a safe, ethical, and efficient manner.

 

 

 

 

As we look towards 2024, the enterprise application of generative AI (GAI) is set to evolve, but certain challenges and key considerations will need careful attention. Here's an overview based on the latest insights and predictions:

Enterprise Adoption of Generative AI in 2024

1. Limited Scope in Enterprise Use: In 2024, businesses of all sizes are expected to utilize GAI, primarily for content creation rather than for more complex operations like manufacturing or supply chain management. This is partly due to the current state of enterprise generative AI, which shows a low adoption rate among large companies and is mainly used in professional services industries​​.
2. Challenges in Enterprise Adoption: Significant hurdles for enterprise adoption of generative AI include data privacy and security concerns, lack of interpretability and transparency, and scalability issues. These challenges imply that achieving enterprise readiness for GAI will require substantial financial and operational efforts over time​​.

Key Considerations and Policy Development

1. AI Acceptable Usage Policy (AUP): With rapid advancements in generative AI, organizations need clear guidance on usage that balances benefits against risks. Only 10% of organizations have comprehensive policies for generative AI, highlighting the need for structured policy development. Effective policies should distinguish between general guidelines and specific standards, focusing on privacy, ethics, and data management​​.
2. Functional, Operational, and Legal Risks: Generative AI entails various risks, including functional risks like model drift and data poisoning, operational risks like resource wastage and confidential information leaks, and legal risks such as copyright infringement and biases leading to discrimination. Addressing these requires a comprehensive machine learning operations lifecycle embedded within a broader governance framework​​.

Generative AI in IT and Cloud Architecture

1. IT Integration and Productivity: Generative AI can significantly enhance developer productivity. For example, ServiceNow reported a 20% productivity improvement among its software engineers using text-to-code technology. This raises questions about budgeting and resource allocation for IT departments​​.
2. Cloud Infrastructure and Architecture: The integration of generative AI with cloud computing is crucial. Decisions on architecture patterns will arise from the certification process, determining how generative AI fits into different organizational aspects like employee enablement, infrastructure automation, or help desk operations. Cloud providers are enhancing their AI infrastructures to support advanced compute needs for customers​​.

Regulatory and Policy Landscape

1. Regulatory Developments: Policymakers are rapidly responding to the challenges posed by generative AI. This includes potential regulations on AI chat tools, restrictions on AI-generated content, and the need for developers to train their models compliant with local laws. The European Union's proposed AI Act revisions and China's proposed rules on AI chat tools are examples of emerging regulatory frameworks​​.

In conclusion, while generative AI offers immense potential for business growth and innovation, enterprises must navigate a landscape of challenges including technological limitations, policy development, risk management, and regulatory compliance. As generative AI continues to evolve, a balanced approach that considers both its capabilities and limitations will be crucial for successful and responsible enterprise adoption in 2024 and beyond.

 

 

 

 

 

 

2024년 기업에서의 생성 인공지능(GAI) 적용 전망은 산업 전문가와 분석가들이 확인한 몇 가지 주요 동향과 도전 과제에 의해 형성됩니다.

1. 멀티모달 AI와 생산성 향상: 2024년에는 멀티모달 AI가 예상되며, 이는 텍스트 이외에도 이미지, 오디오 및 비디오와 상호 작용을 가능하게 합니다. 이러한 진화는 특히 제조, 공학 및 헬스케어와 같은 분야에서 스키매틱, 블루프린트 또는 유전체와 대화식 및 직관적으로 상호 작용할 수 있게 할 것으로 예상됩니다.
2. 오픈 소스 AI와 규제적 도전: 오픈 소스 AI는 생성 인공지능의 보다 넓은 채택을 촉진하고 있습니다. 조직은 제어, 투명성 및 사용자 정의를 위해 오픈 소스 대형 언어 모델을 활용하려고 합니다. 그러나 데이터 개인 정보 보호 및 보안과 관련된 규제적 도전 과제가 채택을 늦출 수 있습니다. 2024년에는 책임 있는 AI에 중점을 두며 프로토콜 및 최상의 실천 방법의 표준화가 더욱 중요해질 것으로 예상됩니다.
3. 국가 및 국제 AI 규제: 국가 및 국제 수준에서 AI를 규제하기 위한 명확한 법률이 예상됩니다. 기업은 생산성 및 효율성 향상과 규정 준수 및 보안 우려 사이에서 민첩성과 적응력을 보여야 할 것입니다. 새로운 규정 위반, 보안 위협 및 생성 인공지능과 관련된 기타 문제는 특히 적절한 정책이나 규제적 경계를 초과하는 조직을 노출시킬 수 있습니다.
4. 기업 채택의 미비: 모든 규모의 비즈니스가 생성 인공지능을 사용할 것으로 예상되지만, 기업 채택은 다른 세그먼트에 비해 뒤처질 것으로 예상됩니다. 2024년에는 생성 인공지능이 주로 제조 또는 공급망과 같은 복잡한 운영 대신 콘텐츠 생성에 사용될 것으로 예측됩니다. 이 미비는 데이터 개인 정보 보호 우려, AI 모델의 해석 및 투명성 부족 및 확장 가능성과 같은 도전 과제와 관련됩니다.
5. 기업용 생성 인공지능의 요구 사항: 생성 인공지능을 기업용으로 사용할 수 있도록 하려면 비즈니스 데이터의 익명화, 다양한 기업 요구 사항을 충족하기 위한 맞춤화 및 다른 공급 업체 및 시장 데이터 제공 업체와의 협력이 필요합니다. 생성 인공지능은 엄청난 잠재력을 가지고 있지만 현재의 제한 사항과 포괄적인 개발, 테스트 및 개선이 필요하다는 점에서 완전히 기업용으로 사용 가능한데는 몇 년이 걸릴 것으로 예상됩니다.

요약적으로, 생성 인공지능은 여전히 빠르게 발전하고 다양한 산업을 변혁할 중요한 잠재력을 가지고 있지만, 기업은 규제적 도전 과제, 채택 장벽 및 책임 있는 AI 실천 방법의 필요와 같은 복잡한 환경에서 진행해야 합니다. 2024년에는 이러한 도전 과제를 해결하여 생성 인공지능의 전체 잠재력을 안전하고 윤리적이며 효율적으로 활용하는 데 중점이 둘 것으로 예상됩니다.

 

 

2024년을 향해 바라볼 때, 생성 인공지능(GAI)의 기업 응용분야는 진화할 것으로 예상되지만, 특정 도전 과제와 주요 고려 사항에 주의를 기울여야 합니다. 최신 통찰과 예측을 기반으로 한 개요는 다음과 같습니다:

1. 2024년 기업에서의 생성 인공지능(GAI) 채택
   • 기업 사용 범위 제한: 2024년에는 모든 크기의 기업이 GAI를 활용할 것으로 예상되지만, 이는 주로 제조 또는 공급망 관리와 같은 복잡한 운영보다는 주로 콘텐츠 생성에 중점을 둘 것으로 예상됩니다. 이것은 대기업 사이에서 낮은 채택률을 보이며 주로 전문 서비스 업종에서 사용되기 때문입니다.
   • 기업 채택의 도전 과제: 기업이 생성 인공지능을 도입하는 데 있어서 주요 도전 과제는 데이터 개인 정보 보호 및 보안 우려, 해석 가능성 및 투명성 부족, 확장성 문제 등입니다. 이러한 도전 과제를 극복하기 위해서는 시간과 자원이 상당한 노력이 필요할 것으로 예상됩니다.
2. 주요 고려 사항과 정책 개발
   • AI 적절한 사용 정책 (AUP): 생성 인공지능의 급속한 발전으로 조직들은 혜택과 위험을 균형있게 고려하는 사용에 대한 명확한 지침이 필요합니다. 조직 중 10%만이 생성 인공지능에 대한 포괄적인 정책을 가지고 있으며, 체계적인 정책 개발의 필요성을 강조합니다. 효과적인 정책은 개인 정보 보호, 윤리 및 데이터 관리에 중점을 두는 일반 가이드라인과 구체적인 표준을 구별해야 합니다.
   • 기능, 운영 및 법적 위험: 생성 인공지능은 모델 드리프트 및 데이터 오염과 같은 기능적 위험, 리소스 낭비 및 기밀 정보 유출과 같은 운영적 위험, 저작권 위배 및 편견으로 인한 차별과 같은 법적 위험을 포함합니다. 이러한 위험을 해결하기 위해서는 넓은 범위의 지배 구조에 포함된 포괄적인 머신 러닝 작업 수명주기가 필요합니다.
3. IT 및 클라우드 아키텍처에서의 생성 인공지능
   • IT 통합과 생산성: 생성 인공지능은 개발자 생산성을 크게 향상시킬 수 있습니다. 예를 들어, ServiceNow는 텍스트를 코드로 변환하는 기술을 사용하여 소프트웨어 엔지니어의 생산성이 20% 향상되었다고 보고했습니다. 이로 인해 IT 부서의 예산 및 자원 할당에 관한 질문이 제기될 것입니다.
   • 클라우드 인프라 및 아키텍처: 생성 인공지능을 클라우드 컴퓨팅과 통합하는 것이 중요합니다. 아키텍처 패턴에 대한 결정은 인증 프로세스로부터 나올 것이며, 생성 인공지능이 직원 활성화, 인프라 자동화 또는 도움말 데스크 운영과 같은 다양한 조직 측면에 어떻게 맞아 떨어지는지를 결정할 것입니다. 클라우드 제공 업체들은 고객의 고급 컴퓨팅 요구를 지원하기 위해 AI 인프라를 개선하고 있습니다.
4. 규제 및 정책 환경
   • 규제 개발: 정책 제정 기관은 생성 인공지능이 제기하는 도전 과제에 신속하게 대응하고 있습니다. 이에는 AI 채팅 도구에 대한 잠재적인 규제, AI 생성 콘텐츠에 대한 제한, 개발자가 로컬 법률을 준수하도록 모델을 훈련해야 하는 필요 등이 포함됩니다. 유럽 연합의 제안된 AI 법안 개정과 중국의 AI 채팅 도구에 대한 제안된 규정은 신흥 규제 프레임워크의 예입니다.

총론적으로, 생성 인공지능은 비즈니스 성장과 혁신의 엄청난 잠재력을 제공하지만, 기업은 기술적 한계, 정책 개발, 리스크 관리 및 규제 준수와 같은 도전적인 환경을 탐색해야 합니다. 생성 인공지능이 계속해서 진화함에 따라 능력과 한계를 모두 고려하는 균형 잡힌 접근 방식이 2024년과 그 이후에 성공적이고 책임 있는 기업 채택에 중요할 것입니다.