Snowflake：2024实践中的生成式人工智能：探索利用企业数据的用例报告（英文版）

By Fern Halper, Ph.D.Generative AI in Practice: Exploring Use Cases to Harness Enterprise Data CHECKLIST 2024 tdwi.org1 TDWI RESEARCHGenerative AI in Practice: Exploring UseCases to Harness Enterprise DataBy Fern Halper, Ph.D.Five considerations for generative AI:Understand when generative AI is the right choiceConsider content creation use cases firstEvaluate chatbotsUse generative AI to derive insightsDon’t forget about back-end deployment issues12345It is an exciting time for artificial intelligence. Generative AI—a subset of artificial intelligence that involves systems designed to generate outputs such as images, music, text, or other forms of media based on its training data—is top of mind for many organizations. Generative AI promises to revolutionize businesses by potentially enabling unprecedented levels of creativity, efficiency, and personalization in content creation, product development, and operational processes. In November 2023, OpenAI announced that its generative AI system, ChatGPT, had 100 million weekly active users.1 It is not surprising, then, that in TDWI surveys the majority of respondents state that they are either experimenting with or exploring generative AI.1 “OpenAI unveils personalized AI apps as it seeks to expand its ChatGPT consumer business,”https://www.reuters.com/technology/openai-enables-customized-gpt-bots-offers-cheaper-more-powerful-models-2023-11-06/ tdwi.org2 TDWI RESEARCHTDWI CHECKLIST REPORT: GENERATIVE AI IN PRACTICE: EXPLORING USE CASES TO HARNESS ENTERPRISE DATAAt TDWI, we see organizations exploring generative AI to unlock new levels of productivity using large language models (LLMs) alongside related tools. In a recent TDWI survey, for instance, the top use cases for generative AI included creating chatbots for customer support (39%), generating marketing content (29%), onboarding new employees (26%), and acting as a front end for analyzing company data (22%).2 All of these use cases may involve using company data, including structured and unstructured data. For instance, content creation or chatbots may utilize unstructured, text-heavy assets such as corporate product information, troubleshooting manuals, website content, and other documentation that holds valuable company information; analytics assistants may use structured and other kinds of tabular data typically stored in data warehouses, lakes, and other analytics platforms. For organizations, it’s important to consider use cases that target all kinds of data because with all enterprises having access to the same models, company data is what provides their competitive advantage. Of course, generative AI isn’t necessarily the best option for all AI use cases. In this ever-evolving environment, it is important to have well-defined business objectives and then evaluate the technology that will help the organization successfully achieve them. As part of implementation, it’s important to have a clear path to production, one focused on maintainin