Scaling agentic AI pilots across the enterprise

在企业中扩展代理式人工智能试点项目

As agentic AI moves from experimentation toward enterprise deployment, the challenge is figuring out how agents can work together, connect to the systems and data they need, and operate safely across the workflows that run a business. Although agentic AI has been adopted by some 80% of Fortune 500 companies, progress toward meaningful scale remains uneven, with many organizations still working through isolated pilots.

随着代理式人工智能（Agentic AI）从实验阶段迈向企业部署，目前的挑战在于如何让这些智能体协同工作、连接所需的系统与数据，并在支撑业务的各类工作流中安全运行。尽管约 80% 的财富 500 强企业已经采用了代理式人工智能，但实现大规模应用的进展仍不均衡，许多组织仍处于孤立的试点阶段。

For Arun Chandra, chief operating officer at NiCE, the first step is moving beyond experimentation for its own sake. “Everybody’s trying to figure out what can we do with this technology?” he says. But scaling requires a clearer connection to business strategy: Organizations need to define whether they are trying to increase revenue, reduce costs, or pursue another strategic or financial objective.

对于 NiCE 首席运营官 Arun Chandra 而言，第一步是超越“为了实验而实验”的阶段。他说：“每个人都在试图弄清楚我们能用这项技术做什么？”但要实现规模化，就需要与业务战略建立更清晰的联系：组织必须明确他们是为了增加收入、降低成本，还是追求其他战略或财务目标。

From there, they need to rethink the workflows where agents will operate instead of just layering AI onto existing processes. “The last thing you want to do is to apply AI on an outdated or an inefficient workflow,” Chandra says.

在此基础上，他们需要重新思考智能体将要运行的工作流，而不是仅仅将人工智能叠加在现有流程之上。Chandra 表示：“你最不希望看到的就是将人工智能应用在过时或低效的工作流中。”

That shift requires organizations to treat agentic AI as a cohesive system. Agents need access to the data, knowledge, and context required to make effective decisions, as well as connections to back-end systems if they are expected to take action. Fragmented information can undermine those capabilities: “The efficacy of these AI agents is purely a function of the context, the knowledge, and the data they can ingest and use,” Chandra says.

这种转变要求组织将代理式人工智能视为一个统一的系统。智能体需要获取做出有效决策所需的数据、知识和背景信息；如果需要它们采取行动，还必须与后端系统建立连接。信息碎片化会削弱这些能力：Chandra 指出：“这些人工智能体的效能完全取决于它们所能摄取和使用的背景信息、知识和数据。”

The organizational implications are equally noteworthy. Scaling agents can create a new form of fragmentation if teams build isolated systems that don’t connect with one another, while governance, privacy, security, and change management become more important as agents take on more consequential work. Chandra argues that AI agents should ultimately be held to the same standards as human workers, with organizations thinking of their workforce as a combination of humans and AI agents.

组织层面的影响同样值得关注。如果各团队构建了互不连接的孤立系统，扩展智能体可能会产生一种新型的碎片化；而随着智能体承担更多关键性工作，治理、隐私、安全和变革管理也变得愈发重要。Chandra 认为，最终应以对待人类员工的标准来要求人工智能体，组织应将劳动力视为人类与人工智能体的结合体。

Looking ahead, that connected approach could enable agents to work proactively and even communicate with other agents to resolve customer needs. For organizations making the transition from pilots to scale, the priority is not to “boil the ocean,” Chandra says, but to instead build a connected strategy around high-value use cases, workflows, workforce changes, and measurable outcomes.

展望未来，这种互联的方法可以使智能体主动工作，甚至与其他智能体沟通以解决客户需求。对于正在从试点转向规模化的组织，Chandra 表示，当务之急不是“试图一口气吃成胖子”（盲目追求大而全），而是围绕高价值用例、工作流、劳动力变革和可衡量的成果，构建一套互联的战略。