Rebuilding the data stack for AI

重构人工智能的数据栈

Artificial intelligence may be dominating boardroom agendas, but many enterprises are discovering that the biggest obstacle to meaningful adoption is the state of their data. While consumer-facing AI tools have dazzled users with speed and ease, enterprise leaders are discovering that deploying AI at scale requires something far less glamorous but far more consequential: data infrastructure that is unified, governed, and fit for purpose. 人工智能或许正主导着董事会的议程，但许多企业发现，实现有效应用的最大障碍在于其数据状况。尽管面向消费者的 AI 工具以其速度和便捷性令用户惊叹，但企业领导者们正逐渐意识到，大规模部署 AI 需要一些远没那么光鲜、却至关重要的东西：即统一、受控且适用的数据基础设施。

That gap between AI ambition and enterprise readiness is becoming one of the defining challenges of this next phase of digital transformation. As Bavesh Patel, senior vice president of Databricks, puts it, “the quality of that AI and how effective that AI is, is really dependent on information in your organization.” Yet in many companies, that information remains fragmented across legacy systems, siloed applications, and disconnected formats, making it nearly impossible for AI systems to generate trustworthy, context-rich outputs. AI 雄心与企业就绪程度之间的差距，正成为数字化转型下一阶段的关键挑战之一。正如 Databricks 高级副总裁 Bavesh Patel 所言：“AI 的质量及其有效性，实际上取决于你组织内部的信息。”然而，在许多公司中，这些信息仍然分散在遗留系统、孤立的应用程序和互不兼容的格式中，使得 AI 系统几乎无法生成可信且内容丰富的结果。

“Really, the big competitive differentiator for most organizations is their own data and then their third-party data that they can add to it,” says Patel. For enterprise AI to deliver value, data must be consolidated into open formats, governed with precision, and made accessible across functions. Without that foundation, businesses risk “terrible AI,” as Patel bluntly describes it. “实际上，大多数组织真正的核心竞争差异化因素在于他们自己的数据，以及他们可以整合的第三方数据，”Patel 说道。为了让企业级 AI 发挥价值，数据必须整合为开放格式，进行精确治理，并实现跨职能访问。如果没有这个基础，企业将面临 Patel 直言不讳所描述的“糟糕的 AI”风险。

That means moving beyond siloed SaaS platforms and disconnected dashboards toward a unified, open data architecture capable of combining structured and unstructured data, preserving real-time context, and enforcing rigorous access controls. When the groundwork is laid correctly, organizations can move toward measurable outcomes, unlocking efficiencies, automating complex workflows, and even launching entirely new lines of business. 这意味着要超越孤立的 SaaS 平台和断开连接的仪表板，转向一种统一的、开放的数据架构，这种架构能够结合结构化和非结构化数据，保留实时上下文，并执行严格的访问控制。当基础工作打好后，组织便能迈向可衡量的成果，释放效率、自动化复杂工作流，甚至开辟全新的业务线。

That value focus is critical, says Rajan Padmanabhan, unit technology officer at Infosys, especially as enterprises seek precision in the outputs driving business decisions. Rather than treating AI initiatives as isolated innovation projects, leading companies are tying AI deployment directly to business metrics, using governance frameworks to determine what delivers results and what should be abandoned quickly. Infosys 部门技术官 Rajan Padmanabhan 表示，这种对价值的关注至关重要，尤其是当企业寻求驱动业务决策的精确输出时。领先的公司不再将 AI 计划视为孤立的创新项目，而是将 AI 部署直接与业务指标挂钩，利用治理框架来确定哪些举措能带来成果，哪些应该迅速放弃。

“We see this big opportunity just with AI literacy with business users, where they’re very eager to understand how they should be thinking about AI,” adds Patel. “What does AI mean when you peel the covers? What are the pieces and the building blocks that you need to put in place, both from a technology and a training and an enablement standpoint?” “我们看到了商业用户在 AI 素养方面的巨大机遇，他们非常渴望了解应该如何思考 AI，”Patel 补充道。“当你揭开 AI 的面纱，它到底意味着什么？从技术、培训和赋能的角度来看，你需要部署哪些部分和构建模块？”

The possibilities ahead are substantial. As AI agents evolve from copilots into autonomous operators capable of managing workflows and transactions, the organizations that win will be those that build the right foundation now. “What we are seeing as a new way of thinking is moving from a system of execution or a system of engagement to a system of action,” notes Padmanabhan. “That is the new way we see the road ahead.” 未来的可能性是巨大的。随着 AI 智能体从副驾驶演变为能够管理工作流和交易的自主操作员，最终胜出的组织将是那些现在就打好正确基础的企业。“我们所看到的一种新思维方式，是从执行系统或交互系统转向行动系统，”Padmanabhan 指出。“这就是我们所看到的未来之路。”

The future of AI in the enterprise will be determined by whether businesses can turn fragmented information into a strategic asset capable of powering both smarter decisions and entirely new ways of operating. 企业 AI 的未来将取决于企业能否将碎片化的信息转化为战略资产，从而驱动更明智的决策和全新的运营方式。