内容来源：https://aibusiness.com/intelligent-automation/ai-tools-from-surveymonkey-aim-to-speed-up-survey-insights

内容总结：

谷歌云赞助发布的一项研究指出，企业首次选择生成式AI应用场景时应优先关注提升信息交互体验的领域。在线调查平台SurveyMonkey本周推出多项AI新功能，旨在帮助人力资源和客户体验团队更高效获取调查洞察。

该平台于周二正式推出AI分析套件，整合了全新开发的智能对话式数据分析工具"AI智能分析"、可自动归类文本反馈的"主题分析"（测试版），以及现有的情感分析和回答质量检测等机器学习功能。同时发布的还有AI问卷生成工具包，支持将文档内容自动转换为定制化问卷，并能智能生成调查主题。

"这些功能采用人机协同模式，既能确保专业性，又能大幅减少人工操作时间，"SurveyMonkey产品营销总监迈克·格林伯格表示，"我们正在合理部署AI技术，帮助用户更快速地获得可靠决策依据。"

星座研究机构分析师莉兹·米勒指出，此类工具降低了企业使用门槛："让那些无力购置高端调研工具的中小企业也能通过AI快速获得客户与员工洞察。"但她同时提醒，AI分析工具仍需谨慎使用，使用者需充分理解数据样本特征和历史背景，否则可能影响分析准确性。

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中文翻译：

由谷歌云赞助
如何选择首个生成式AI应用场景
要开始使用生成式AI，首先应关注能够提升人类信息交互体验的领域。

新功能包括"AI智能分析"——一款基于聊天的分析工具，可快速提供数据洞察和细分功能。
SurveyMonkey本周推出了多款AI新工具，旨在帮助人力资源和客户体验团队从调查中获取更深入的洞察。
这家调查平台提供商于周二发布了AI分析套件，将新功能与现有AI能力相结合。新增功能包括聊天式数据分析工具"AI智能分析"，以及可自动识别归类开放式文本答复的主题分析功能（目前处于测试阶段）。
该套件现有功能包含对开放式回答进行情感分析，以及通过机器学习识别低质量答复的响应质量检测。
SurveyMonkey同日还推出了创建调查问卷的新工具包。人力资源经理和客户体验团队可使用AI辅助粘贴创建工具，将文档或邮件中的调查问题自动转换为问卷。用户还能通过AI问卷主题生成器创建个性化调查主题。
"这是人机协作的AI辅助模式，用户无需整天手动输入调查问题或担心设置是否准确，"SurveyMonkey产品营销总监迈克·格林伯格表示，"我们将AI智能融入现有平台，帮助用户以正确方式提问，从而获得可靠洞察并加速决策，这比其他技术方案更高效。"
但星座研究公司分析师莉兹·米勒指出，从调查中获取洞察历来并非易事。
"升级后的功能实现了真正的随问随答，"米勒说。她补充说，诸如AI智能分析等功能让分析师、营销人员或人力资源主管能够直接向数据提问，发现更精准的细分维度或从不同角度解读答复。
此类工具对某些企业而言风险较低。
"这让那些尚未投资重型调研工具、但需要通过调查表单从客户或员工处获取洞察的企业也能应用AI技术，"米勒表示。她补充说，快速上手的便捷性和低风险特性意味着各种规模的组织都能使用这些AI工具。
但此类工具并非万无一失。米勒指出，挑战之一在于理解数据集与分析方法的匹配度。
"要理解数据和结果，往往需要了解调研对象群体，"她说，"必须掌握历史数据和人群细微特征，而这些很难通过训练获得。"
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英文来源：

Sponsored by Google Cloud
Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
New features include Analyze with AI, a chat-based analysis tool that offers quick insights and data segmentation.
SurveyMonkey introduced new AI features and tools this week, designed to help HR and customer experience teams gain better insight from surveys.
On Tuesday, the survey platform provider introduced AI Analysis Suite, which combines new functionality with existing AI capabilities. New features include Analyze with AI, a chat-based data analysis feature, and Thematic Analysis, which is currently in beta and can identify and categorize open-ended text responses.
The suite's existing features include sentiment analysis to sort open-ended responses, and response quality, which uses machine learning to identify poor-quality results.
SurveyMonkey also on Tuesday introduced a new toolkit for creating surveys and forms. HR managers and customer experience teams can use the AI-powered paste and create tool to transform survey questions from a document or email into a questionnaire. Users can also create and customize themes for their surveys with the AI survey and form theme generator tool.
"This is AI that's helpful with a human-in-the-loop, so they don't have to spend their day entering survey questions one by one [or] wondering if they've got it right," said Mike Greenberg, director of product marketing at SurveyMonkey. "We are infusing our existing platform with AI, where it makes sense to help ... ask questions the right way so that you can get reliable insights on the other side and make a decision faster than you would be able to do with other technology."
Trying to gather insights from a survey, though, has never been easy, according to Constellation Research analyst Liz Miller.
"What these updated capabilities represent is the ability to ask," Miller said. Capabilities such as Analyze with AI enable an analyst, marketer or HR leader to ask a question of the data, identify better segments or look at the responses from a different angle, she added.
Tools like these are also low risk for some businesses.
"This brings AI to those businesses that might not be investing in heavy firepower survey and research tools but are deploying surveys and forms to gather insights from customers or employees better," Miller said. That accessibility to get started quickly and low-risk profile means that organizations of all sizes can use these AI tools, she added.
However, tools like these aren't foolproof. One challenge is understanding how the datasets align with how the analysis is done, Miller said.
"To understand data and outcomes, you often need to understand your panel," she said. "You need to understand some of that history and the nuance of people. That could be hard to train."
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