Gartner 问卷调查结果：仅少部分组织具备成熟的人工智能流程

The use of artificial intelligence (AI) is spreading rapidly in organisations, the latest survey from analyst Gartner has reported. However, the majority of organisations polled admit that their AI processes are not m分析机构 Gartner 的最新问卷调查报告显示，人工智能的使用正在业界企业迅速普及。然而大多数受访企业承认自己企业的人工智能流程并不成熟。

The survey, based on a poll of 644 organisations, found that the percentage of respondents whose organisations are applying AI in multiple business processes has increased from 13% in 2021 to 28% this year.

该调查基于对 644 家组织的问卷访问，结果发现受访者的组织在多个业务流程中应用人工智能的比例从 2021 年的 13% 增至今年的 28%。

Leinar Ramos, senior director analyst at Gartner, said: “This means that AI is evolving from something just performed in certain islands in the organisation to a much more widespread activity.”

Gartner 高级主任分析师  Leinar Ramos 表示，“这意味着人工智能正在从只是组织中某些孤岛的东西演变为更为广泛的活动。”

Almost half (49%) of the organisations polled said that their biggest challenge was estimating and delivering value with AI-based initiatives. Gartner identifies five business outcomes that AI initiatives can support, namely: revenue growth, cost optimisation, risk reduction, customer experience and employee productivity improvement.

近一半（49%）的受访企业表示，他们面临的最大挑战是估算和实现人工智能项目的价值。Gartner 发现，人工智能项目可以支持的五项业务成果分别为收入增长、成本优化、风险降低、客户体验和员工生产力的提高。

According to Ramos, the survey results show that organisations need to develop foundational AI capabilities to tackle the challenges that come from deploying the technology at scale. This involves balancing AI projects and initiatives across the broader business.

Ramos 认为，调查结果显示，企业有必要发展基础人工智能能力，以应对大规模部署人工智能技术所带来的挑战。这涉及在更广泛的业务范围内平衡人工智能项目和计划。

However, Gartner’s survey found that less than a tenth (9%) of the organisations polled said they had mature processes in place for AI. A focus on AI is among the attributes of those organisations that identify as having mature AI processes, said Ramos, which means they have a systematic way of building and deploying AI projects into production, including monitoring AI models and a change management programme.

然而 Gartner 的调查发现，只有不到十分之一（9%）的受访企业表示他们已经为人工智能制定了成熟的流程。Ramos 表示，那些觉得自己拥有成熟人工智能流程的组织的一个特点是重视人工智能，这意味着这些企业拥有一套系统化的方法，可以构建和部署人工智能项目，包括监控人工智能模型和变更管理计划。

“We found that organisations that performed changed management activities more frequently tend to have AI initiatives that have a greater impact on business outcomes,” he said.

他表示，“我们发现，那些经常改变管理活动的组织的人工智能项目往往可以对业务结果产生更大的影响。”

Ramos said that there is a clear difference between those organisations that claim their data is ready for AI versus the ones that said their data is not AI-ready, adding: “You need to prioritise data when you’re working on AI.”

Ramos 表示，在声称其数据已为人工智能做好准备的组织与声称其数据尚未为人工智能做好准备的组织之间存在明显差异。他接着表示，“搞人工智能需要优先考虑数据。”

He noted that there is a misconception, especially with generative AI (GenAI), that organisations do not need to worry about clean data since they start with pre-trained models that have already been trained using a lot of data.

他指出，业界尤其是在生成式人工智能（GenAI）领域存在一个误解，有些人认为企业不需要担心干净数据的问题，因为他们使用的预训练模型已经经过大量数据的训练。

“For the most valuable use cases, you need some sort of data source going into these models. Having your data AI-ready enables organisations to generate good business outcomes with AI,” added Ramos.

Ramos 补充表示，“对于那些最有价值的使用案例，数据源需要进入这些模型。为人工智能准备好了数据就可以让企业利用人工智能产生良好的业务成果。”

The survey found that utilising GenAI embedded in existing applications (such as Microsoft’s Copilot for 365 or Adobe Firefly) is the top way to fulfil GenAI use cases, with 34% of respondents saying this is their primary method of using GenAI.

Gartner 的调查发现，利用嵌入到现有应用程序的生成式人工智能（例如微软的 Copilot for 365 或 Adobe Firefly）是实现生成式人工智能用例的首选方式，34% 的受访者表示这是他们使用生成式人工智能的主要方法。

This was found to be more common than other options such as customising GenAI models with prompt engineering (25%), training or fine-tuning bespoke GenAI models (21%), or using standalone GenAI tools such as ChatGPT or Gemini (19%).

而认为其他方法是使用生成式人工智能主要方法的受访者则更少一些，例如以下各种方法，提示工程定制生成式人工智能模型（25%）、训练或微调定制生成式人工智能模型（21%）或使用 ChatGPT 或 Gemini 等独立的生成式人工智能工具（19%）。

“GenAI is acting as a catalyst for the expansion of AI in the enterprise,” said Ramos. “This creates a window of opportunity for AI leaders, but also a test on whether they will be able to capitalise on this moment and deliver value at scale.”

Ramos表示，“生成式人工智能正在成为企业人工智能扩展的催化剂。这为人工智能领导者创造了一个机遇窗口，但同时在考验他们能否利用机遇并实现规模化的价值。”