包阅导读总结
1. 关键词：Analytics、ML、Business Objectives、CIOs、Data Science
2. 总结：报告指出尽管多数 CIO 关注 AI 应用且面临数据相关投资交付业务价值的需求，但预算增加有限。重要成功指标明确，然而机器学习模型部署成功率低，很多项目未衡量绩效，组织若表现不佳可能削减投入等。
3. 主要内容：
– 80%的 CIO 负责研究评估可能的 AI 技术添加，74%与业务领导更紧密合作 AI 应用
– 仅 54%的 CIO 报告 IT 预算增加，AI 投资是第三驱动因素，安全改进和技术成本上升排名更高
– 分析项目重要成功指标包括投资回报、收入增长和效率提高，但仅 32%成功部署超 60%的机器学习模型
– 超 50%不常衡量分析项目绩效，更多项目可能未交付业务价值
– 模型层面高部署率难，组织交付业务价值不佳可能削减投入、寻求替代方法或落后于竞争对手
思维导图：
文章来源：infoworld.com
作者：InfoWorld
发布时间：2024/7/15 9:00
语言：英文
总字数：2349字
预计阅读时间：10分钟
评分：87分
标签：机器学习,商业目标,分析失败,数据科学,人工智能部署
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Foundry’s State of the CIO 2024 reports that 80% of CIOs are tasked with researching and evaluating possible AI additions to their tech stack, and 74% are working more closely with their business leaders on AI applications. Despite facing the demand for delivering business value from data, machine learning, and AI investments, only 54% of CIOs report IT budget increases. AI investments were only the third driver, while security improvements and the rising costs of technology ranked higher.
CIOs, IT, and data science teams must be careful that AI’s excitement doesn’t drive irrational exuberance. One recent study shows that the most important success metrics for analytics projects include return on investment, revenue growth, and improved efficiencies, yet only 32% of respondents successfully deploy more than 60% of their machine learning models. The report also stated that over 50% do not regularly measure the performance of analytics projects, suggesting that even more analytics projects may fail to deliver business value.
Organizations shouldn’t expect high deployment rates at the model level, as it requires experimentation and iteration to translate business objectives into accurate models, useful dashboards, and productivity-improving AI-driven workflows. However, organizations that underperform in delivering business value from their portfolio of data science investments may reduce spending, seek alternative implementation methods, or fall behind their competitors.
