Published: 14 March 2024
分析和人工智能持续碰撞，对多个领域产生冲击。数据和分析领导者必须应对人工智能分析和数据科学生态系统、用户行为、角色和决策的影响，才能充分把握新兴机遇，控制未知风险。
人工智能（AI）正快速改变业务线用户利用分析平台制定决策的方式，更多用户通过使用增强分析能力来使用此类平台。
分析内容创建者和消费者的界限变得模糊。这是因为，在AI的加持下，这两类人群都能够获取与自身工作流完美匹配和集成的洞察。
AI正在重塑数据科学人员的职责。针对复杂决策的增强和自动化功能，让技术水平较低的用户也能够承担更复杂任务，进而发挥更大的作用。
AI赋能型分析体验的可组装性增强，智能应用与无头架构和行动框架的集成，打破了分析和商业智能（ABI）平台和业务应用的界限。
负责支持分析和AI解决方案的数据和分析（D&A）领导者，应：
评估需要由AI解答的业务问题，并确定主要利益相关方对于此类解决方案的青睐程度，以确保这项技术能够得到采用，并对业务目标产生积极影响。
在数据素养计划中引入关于新技能的内容，使员工具备成为分析内容创建者所必备的技能。这些技能可能包括提示工程、公平性与偏见缓解、知识治理、统计，以及与数据和AI道德使用相关的政策及标准、隐私、风险和合规。
引入AI，使技术水平较低的用户能够承担更复杂的任务，从而优化相对稀缺的D&A技术人才的价值。以有效和安全的方式实现可衡量的价值交付，支持用户主导型AI技术使用。
组建平台和智能应用生态系统，借助开放式无头架构和新兴决策智能实践，将洞察与行动联系起来
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Analysts:
Edgar Macari, Peter Krensky, Afraz Jaffri, David Pidsley, Aura Popa, Mike Fang, Rita Sallam, Julian Sun, Radu Miclaus
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