人工智能技术正以前所未有的速度发展，特别是生成式AI的突破更是加速了这一进程。AI在多模态处理、推理和自主行动能力方面都有显著提升。然而，文章指出，AI模型的效果高度依赖于输入的数据质量，尽管数据管理技术也在进步，但多数组织未能跟上AI发展的步伐。这导致只有极少数组织能从AI战略中实现预期的业务成果，仅有2%的受访高管认为其组织在AI成效方面表现出色。调查显示，数据团队在跟上AI步伐方面表现不佳，与生成式AI出现前相比，在数据战略执行上并无显著改善。人才短缺、数据获取、血缘追踪和安全复杂性是主要制约因素。因此，AI的潜力尚未完全释放，仅有2%的受访者认为其组织的AI表现出色，大多数组织仍在努力扩展生成式AI的应用。

🚀 AI技术飞速发展，多模态处理与自主行动能力增强。自2021年以来，人工智能能力显著提升，尤其在生成式AI突破后，AI在处理文本、音频、视频等多种信息格式（多模态）以及自主推理和行动方面展现出强大潜力，AI代理的应用也逐渐开始。

📊 数据管理滞后，制约AI成效。尽管AI能力不断增强，但其输出质量仍高度依赖于输入数据。文章指出，虽然数据管理技术也在进步，但多数组织未能充分利用这些技术跟上AI发展的速度，导致AI战略的业务成果不尽如人意，仅有2%的受访高管认为组织在AI成效方面表现出色。

📉 数据团队表现未达预期，AI应用仍面临挑战。调查显示，数据团队在跟上AI步伐方面表现不佳，12%的受访者自评为数据“高成就者”，与2021年的13%相比并无提升。人才短缺、数据获取困难、血缘追踪复杂以及安全问题是阻碍AI成功的关键因素。因此，AI尚未完全发挥潜力，仅有2%的受访者认为其组织的AI表现出色，多数组织在扩展生成式AI应用方面仍面临挑战。

Four years is a lifetime when it comes to artificial intelligence. Since the first edition of this study was published in 2021, AI’s capabilities have been advancing at speed, and the advances have not slowed since generative AI’s breakthrough. For example, multimodality— the ability to process information not only as text but also as audio, video, and other unstructured formats—is becoming a common feature of AI models. AI’s capacity to reason and act autonomously has also grown, and organizations are now starting to work with AI agents that can do just that.

Amid all the change, there remains a constant: the quality of an AI model’s outputs is only ever as good as the data
that feeds it. Data management technologies and practices have also been advancing, but the second edition of this study suggests that most organizations are not leveraging those fast enough to keep up with AI’s development. As a result of that and other hindrances, relatively few organizations are delivering the desired business results from their AI strategy. No more than 2% of senior executives we surveyed rate their organizations highly in terms of delivering results from AI.

To determine the extent to which organizational data performance has improved as generative AI and other AI advances have taken hold, MIT Technology Review Insights surveyed 800 senior data and technology executives. We also conducted in-depth interviews with 15 technology and business leaders.

Key findings from the report include the following:

• Few data teams are keeping pace with AI. Organizations are doing no better today at delivering on data strategy than in pre-generative AI days. Among those surveyed in 2025, 12% are self-assessed data “high achievers” compared with 13% in 2021. Shortages of skilled talent remain a constraint, but teams also struggle with accessing fresh data, tracing lineage, and dealing with security complexity—important requirements for AI success.

• Partly as a result, AI is not fully firing yet. There are even fewer “high achievers” when it comes to AI. Just 2% of respondents rate their organizations’ AI performance highly today in terms of delivering measurable business results. In fact, most are still struggling to scale generative AI. While two thirds have deployed it, only 7% have done so widely.

Download the report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. AI tools that may have been used were limited to secondary production processes that passed thorough human review.