Published: 12 April 2023
生成式人工智能（AI）可以制作原创媒体内容、合成数据和物理对象模型，为用户提供突破性的创新机会。企业架构和技术创新领导者须对各类用例进行评估，为部署生成式AI做好准备。
生成式人工智能可通过AI模型增强核心流程，对许多行业产生直接影响，包括制药、制造、媒体、建筑、室内设计、工程、汽车、航空航天、国防、医疗、电子以及能源行业。此外，还能增强覆盖多个部门的支持流程，从而影响营销、设计、企业沟通、培训和软件工程。
深度伪造等恶意用法，会使个人、企业机构和政府部门面临重大的声誉、假冒、欺诈和政治风险。版权问题依然存在，许多模型使用的训练数据并未获得授权，并且可以通过模仿特定样式来生成原创内容。
生成式AI已经过多年发展；过去12个月内，基础模型对创意工作的增强能力受到热炒，吸引了大量风投资金。
企业架构和技术创新领导者如需了解最重要的新兴技术和战略趋势，应：
了解本文介绍的用例，确定生成式AI对其行业和企业机构的业务影响（效益/风险）。与各利益相关方合作，评估生成式AI用例蕴含的业务机会和威胁，同时就部署此类用例或防范相关威胁的技术可行性、组织就绪度和外部因素展开评估。
与安全和风险管理领导者合作，积极行动，降低深度伪造、版权问题和其他针对企业机构的生成式AI技术恶意使用风险。选择供应商时，确保其提供了用于减少偏见的工具，并且优先选择提供详细模型卡以及训练数据集相关信息的工具。
提高数据科学家运用生成式AI的技能，并且与学术机构和初创公司合作，为内外部产品和服务创建定制化的AI算法和模型。为终端用户提供培训，帮助其通过可用的生成器来评估潜在风险或市场机会，并且创建自己的产品。
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Analysts:
Brian Burke, Arun Chandrasekaran, Svetlana Sicular
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