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抢先实测:当AI支付宝只剩下对话框 它能做什么?

关注前沿科技的 2026-06-16 18:47
关注前沿科技的 2026/06/16 18:47

邦小白快读

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本文是AI支付宝内测版本的抢先实测,给普通用户整理了新版核心变化、功能体验和优缺点,核心干货如下:

1.核心改版:AI支付宝把原有的臃肿入口重构,只保留“阿宝”“资产”两个页面,阿宝页以对话框为核心,仅保留扫一扫、收付款等四个核心高频功能,其余小程序入口隐藏,大幅解决了原版本找服务麻烦的痛点。

2.阿宝功能体验:阿宝可根据用户需求直接跳转对应服务入口,操作流畅,支持覆盖多平台商家服务,还新增了Agent定时日程提醒功能;但目前仅负责找入口,具体操作仍需用户手动完成,任务精准度和步骤简化还有优化空间。

3.资产页功能体验:整合用户分散在各处的资产,AI自动分析收支、给出理财建议,支持个性化细分支出管控,体验远超很多普通独立记账软件。

AI支付宝的改版,给品牌商的渠道运营、用户运营带来新变化和新机会,核心干货整理如下:

1.渠道逻辑变化:原来用户找品牌服务需要多层跳转,中小品牌的服务很容易被淹没,现在改为AI对话框直接按需求匹配,品牌触达用户的逻辑改变,品牌需要尽快适配AI入口的检索匹配逻辑。

2.生态优势利好多平台布局品牌:AI支付宝依托支付宝原有数百万商家生态,可以支持多平台品牌服务被唤起,比仅支持单一平台的AI服务更有优势,多渠道布局的品牌可以获得更多曝光机会。

3.用户运营新机会:AI可以精准识别用户的消费分类、消费频次、偏好等标签,品牌可以依托这些数据做更精准的用户分层运营,还可以适配AI唤起的场景优化服务流程,提升用户转化。

AI支付宝的改版给卖家带来了新的流量增长机会,也明确了需要提前布局的方向,核心干货如下:

1.流量机会:原来支付宝入口层级深,中小卖家的服务很难被用户找到,改版后AI按用户需求直接匹配服务,中小卖家获得了更平等的曝光机会,获客成本有望降低。

2.接入成本低:只要卖家已经接入支付宝小程序生态,就可以被AI阿宝匹配唤起,不需要额外做重复接入,当前就可以享受改版带来的流量红利。

3.风险提示和机会预判:目前AI匹配精准度还有不足,卖家需要优化自身服务的关键词标签,避免AI错配;后续AI升级后大概率会实现全流程代操作,卖家可以提前适配AI场景的服务流程,提前抢占先机。

AI支付宝的改版探索,给面向C端的生产工厂带来了数字化转型、用户需求洞察的新启示,核心干货如下:

1.需求洞察新机会:AI支付宝可以依托支付数据,沉淀出更精准的用户消费行为数据,包括不同品类的消费频次、客单价、消费偏好,工厂做产品研发和设计,可以依托这些数据更精准匹配用户需求,避免生产不符合市场的产品。

2.数字化转型启示:支付宝做AI升级,是从用户最痛的“找服务难”痛点切入,没有盲目追求大而全的改造,工厂推进数字化转型也可以参考这个路径,从自身核心痛点切入,逐步落地,降低转型风险。

3.直达C端的新机会:如果工厂做自有品牌,想要直接触达C端用户,可以接入支付宝生态,借助AI支付宝的新入口获得更多精准曝光,降低获客成本,拓展新的销售渠道。

AI支付宝的内测,展现了ToC AI服务的行业发展趋势、现存痛点和可参考的方向,核心干货如下:

1.行业发展新趋势:超级App的AI化已经从概念进入落地阶段,核心方向是从原来的多入口分散布局,转向统一对话式入口,由AI代替用户手动找服务,这是未来ToC互联网产品的重要发展方向。

2.现存核心痛点:当前AI管家类产品普遍停留在“帮用户找入口”的初级阶段,无法完成全流程复杂操作,意图识别精准度不足,还有很大的优化空间,这也是服务商接下来可以突破的方向。

3.可参考的产品方案:垂直领域结合自有数据做AI整合,已经展现出明显优势,支付宝在资产管理领域结合自身支付数据,打造的AI记账能力远超普通独立记账产品,这个模式值得垂直服务商参考;另外涉及隐私资金的场景,保持权限克制,核心操作交给用户确认,可以有效降低用户的安全顾虑。

AI支付宝的改版探索,给超级平台做AI转型提供了很多可参考的经验,核心干货如下:

1.明确平台AI转型的核心方向:多数超级平台都会面临生态臃肿、入口层级深、用户找服务难的问题,AI支付宝的改版证明,做统一AI对话框入口、简化界面,是解决这个痛点的可行方向。

2.运营管理的参考经验:改版只保留四个核心高频功能放在首页,其余入口隐藏,这种克制的设计既满足了用户的核心需求,又保持了界面简洁,适合各类平台做AI改版参考。

3.风险规避经验:涉及用户资金、隐私的功能,支付宝坚持AI只提供服务和方案,核心变动操作必须由用户本人确认,既发挥了AI的便捷性,又控制了安全风险,避免权限过大带来的安全问题。

4.核心竞争力构建:超级平台自身积累的商家生态和用户数据,是AI竞争的核心壁垒,多服务覆盖比单一平台AI更有优势,平台可以依托自身原有生态构建AI竞争力。

AI支付宝的内测是国民级支付产品AI转型的重要样本,对研究AI落地产业有很高的参考价值,核心干货如下:

1.产业新动向:国内互联网巨头的AI布局已经从发布大模型技术,进入到核心产品AI重构的落地阶段,支付宝作为成立22年的国民级支付产品,做出最大幅度的改版押注AI,代表移动互联网产品开始全面向AI原生产品转型。

2.新的产品商业模式:本次探索出了“统一对话入口+垂直场景数据整合”的AI超级App模式,在入口层做简化提升体验,在自身擅长的资产管理等垂直领域,依托原有数据叠加AI能力打造差异化壁垒。

3.待研究的新问题:当前AI原生产品还存在明显能力短板,AI仅能承担入口检索功能,无法完成全流程复杂操作,产品体验距离用户期待还有差距,这是接下来产业界需要研究解决的核心问题。

4.转型路径启示:蚂蚁采取内测试水、从核心痛点切入逐步迭代的转型路径,也为大产品AI转型提供了可研究的典型样本。

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Quick Summary

This article is an early hands-on test of the internal beta version of AI-powered Alipay. It summarizes the core updates, functional experience, pros and cons of the new version for general users, with key takeaways as follows:

1. Core redesign: AI Alipay has restructured its original bloated navigation, keeping only two tabs: "Abao" and "Assets". The Abao tab centers on a chat dialog, retaining only four high-frequency core functions including Scan and Pay/Receive, while hiding all other mini-program entries, which largely solves the original pain point of difficulty finding services.

2. Abao user experience: Abao can directly jump to the corresponding service entry based on user requests, with smooth operation. It supports accessing merchant services across multiple platforms and adds a new AI Agent-powered scheduled reminder function. However, it currently only locates service entries for users, and all specific operations still need to be completed manually by users. There is room for improvement in task accuracy and process simplification.

3. Assets tab experience: The page aggregates users' assets scattered across different platforms, automatically analyzes income and expenditure with AI, provides personalized financial advice, and supports granular personalized expense management. Its experience outperforms many standalone ordinary bookkeeping apps.

The redesign of AI-powered Alipay brings new changes and opportunities to brands' channel operation and user management. Key takeaways are summarized below:

1. Change in channel logic: Originally, users needed multiple layers of navigation to find brand services, making it easy for small and medium-sized brands' services to get buried. The new version matches services directly via AI chat dialog based on user needs, which completely changes how brands reach users. Brands need to adapt to the retrieval and matching logic of the AI entry as soon as possible.

2. Ecological advantages benefit brands with multi-platform layouts: Backed by Alipay's original ecosystem of millions of merchants, AI Alipay supports activating brand services across multiple platforms, giving it an edge over AI services that only support single-platform access. Brands with multi-channel layouts will gain more exposure opportunities.

3. New opportunities for user operation: AI can accurately identify user tags including consumption category, frequency, and preferences. Brands can leverage this data for more precise user hierarchical operation, and optimize service processes for AI-activated scenarios to improve conversion rates.

The redesign of AI-powered Alipay brings new traffic growth opportunities for sellers and clarifies directions for early layout. Key takeaways are as follows:

1. Traffic opportunity: Originally, service entries on Alipay were deeply nested, making it hard for small and medium-sized sellers to get discovered by users. After the redesign, AI matches services directly based on user needs, giving small and medium-sized sellers more equal exposure opportunities and potentially lowering customer acquisition costs.

2. Low access cost: As long as sellers have already integrated into the Alipay mini-program ecosystem, they can be matched and activated by Abao AI without additional redundant access work, and can start benefiting from the traffic dividend brought by the redesign right now.

3. Risk warning and opportunity outlook: AI matching accuracy is still insufficient at this stage, so sellers need to optimize the keyword tags of their services to avoid AI mismatching. After subsequent AI upgrades, full-process automated operation is very likely to be realized, so sellers can adapt their service processes for AI scenarios in advance to get a first-mover advantage.

The exploratory redesign of AI-powered Alipay brings new insights for C-end oriented manufacturing factories on digital transformation and user demand insight. Key takeaways are as follows:

1. New opportunities for demand insight: Backed by payment data, AI Alipay can accumulate more accurate user consumption behavior data, including consumption frequency, average order value and preferences across different categories. Factories can leverage this data to align product R&D and design more accurately with user demand, avoiding producing products that do not fit the market.

2. Insights for digital transformation: Alipay's AI upgrade started from the most pressing user pain point of "difficulty finding services", instead of blindly pursuing an all-encompassing overhaul. Factories can follow this path for digital transformation: start from core pain points, roll out solutions step by step, and reduce transformation risks.

3. New opportunities for direct C-end access: If factories operate their own brands and want to reach C-end users directly, they can integrate into the Alipay ecosystem, gain more precise exposure through the new AI entry, lower customer acquisition costs, and expand new sales channels.

The internal beta of AI-powered Alipay reveals industry trends, existing pain points and reference directions for ToC AI services. Key takeaways are as follows:

1. New industry trend: The AI transformation of super apps has moved from concept to implementation. The core direction is shifting from the original distributed multi-entry layout to a unified conversational entry, where AI replaces manual service searching for users. This is a key development direction for future ToC internet products.

2. Current core pain points: Most existing AI assistant products still stay at the primary stage of "helping users find entries", unable to complete full-process complex operations, with insufficient intent recognition accuracy. There remains large room for improvement, which is also the breakthrough direction for service providers going forward.

3. Reference product frameworks: AI integration in vertical fields combined with proprietary data has already shown clear advantages. Alipay built its AI bookkeeping capability in the asset management field by combining its own payment data, and its performance outperforms ordinary standalone bookkeeping products. This model is worth referencing for vertical service providers. Additionally, in scenarios involving private information and funds, maintaining restrained permissions and leaving core operations for user confirmation can effectively reduce users' security concerns.

The exploratory redesign of AI-powered Alipay provides lots of reference experience for super platforms' AI transformation. Key takeaways are as follows:

1. Clarifying the core direction of platform AI transformation: Most super platforms face common problems including ecosystem bloat, deeply nested entries, and difficulty for users to find services. The redesign of AI Alipay proves that building a unified AI dialog entry and simplifying the interface is a viable solution to this pain point.

2. Reference experience for operation and design: The redesign only retains four core high-frequency functions on the homepage and hides all other entries. This restrained design meets users' core needs while keeping the interface clean, making it a good reference for AI redesign across all types of platforms.

3. Experience for risk mitigation: For functions involving user funds and privacy, Alipay adheres to the rule that AI only provides services and recommendations, and all core changes and operations must be confirmed by the user personally. This approach leverages the convenience of AI while controlling security risks, avoiding safety issues caused by excessive AI permissions.

4. Building core competitiveness: The merchant ecosystem and user data accumulated by super platforms are the core barriers for AI competition. Multi-service coverage offers greater advantages than single-service AI platforms. Platforms can build their AI competitiveness based on their existing original ecosystems.

The internal beta of AI-powered Alipay is an important sample of AI transformation for a national-scale payment product, offering high reference value for research on AI industrial implementation. Key takeaways are as follows:

1. New industrial trend: Chinese internet giants' AI layout has moved beyond releasing large model technologies and entered the implementation stage of AI-driven reconstruction of core products. As a 22-year-old national-scale payment product, Alipay has launched its most extensive overhaul to bet on AI, representing that mobile internet products have started an all-round transition to AI-native products.

2. A new product business model: This exploration has developed an AI super app model of "unified conversational entry + vertical scenario data integration". It simplifies the entry layer to improve user experience, and builds differentiated barriers in vertical fields it excels at (such as asset management) by overlaying AI capabilities on top of original data.

3. New open research questions: Current AI-native products still have obvious capability shortcomings: AI can only undertake entry retrieval work, and cannot complete full-process complex operations. Product experience still lags behind user expectations, which is the core problem that the industry needs to research and solve next.

4. Insights on transformation paths: Ant Group's approach of testing via internal beta, starting from core pain points and iterating step by step, also provides a typical research sample for AI transformation of large established products.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

I am a Brand Seller Factory Service Provider Marketplace Seller Researcher Read it again.

前沿科技,数智经济

把一万个入口塞进一个对话框。

文|魏琳华

编|刘俊宏

从曝光到正式上线,AI支付宝的内测版本终于浮出水面。

6月16日,蚂蚁集团官宣新版AI支付宝内测开启,这可以说是蚂蚁对自己“开刀”最大的一次,它在原版界面之外,给了用户一个更AI味的全新界面:新版只有“阿宝”、“资产”两个功能页,“阿宝”中,一个AI对话框加上克制的四个功能,做到清爽的同时还要好用。

可以说,这是支付宝成立22年来最大胆的一次改版,也是蚂蚁押在AI上的一次有分量的尝试——它能不能像互联网时代创新出“二维码”那样,继续跟上AI时代的浪潮。

今天,光锥智能也拿到了AI支付宝的邀请码,率先体验一把清爽版的支付宝到底好不好用。

实测“阿宝”

入口更好找,任务完成待提升

打开新版支付宝,第一反应是,前所未有地干净。

AI支付宝的界面只有“阿宝”、“资产”两个功能页。没有种树、没有养鸡、没有双排的短视频推流,主要界面都被一个干干净净的对话框占据。

对话框下面放的功能也比较克制,只有“扫一扫”、“收付款”、“出行”、“理财”四项功能,都是用户基本必用的核心功能。其他的一些小程序入口则被隐藏到了下方,用户主动划开才能看到。

从名字听起来,阿宝延续了蚂蚁健康产品阿福的起名模式,它俩的定位也都是管家,阿宝就是一个直接抵达各种支付服务的AI支付管家。

为什么这么改?支付宝的生态太庞大了,之前几百万个小程序,比如公积金、社保、医保、打车、外卖,什么都有。但App也因此变得臃肿。用户想查个公积金,得从首页→市民中心→公积金做三步,还得保证能找到位置。

阿宝的定位,就是帮用户免去在几百万个小程序里“淘”自己需要的那一个,它让用户只说需求,剩下的让AI办。

光锥智能测试了开花呗、水电缴费、公积金、医保码等界面,阿宝都帮我顺利找到了对应的场景,且大多数打开的过程都很丝滑,基本不用等就能打开。

通过“讲需求”的方式,阿宝能直接帮用户打开对应的界面,但它做的能力还非常初级——只负责帮你找页面,细节操作和决策还是交给用户自己做。

单以点外卖为例,和阿里的千问AI对比,后者的操作显然更精准、能帮用户省去更多的操作步骤。

简单来说,它帮你把购买页打开,但操作还得自己来。比如当我要求AI帮我“点一杯无糖的清新手打柠檬茶”,阿宝能推送对应的点单页让我自己下单,但我还需要在推送出来的界面里自己找清新手打柠檬茶在哪,然后自己在界面上选无糖,再自己支付。

但如果交给千问AI来做,同样的需求,它会直接帮我在淘宝闪购里勾选好“清新手打柠檬茶”和“无糖”的要求,我只需要确认后点支付这一步操作,这才是我想要的AI下单。

打车任务上,阿宝倒是根据我的地点直接选好了地址和车型,但可惜我要求拼车的情况下,它选择了一个“其它品牌”的服务,没有满足我的具体要求。

不过,支付宝这么多年沉淀下来的商家生态,让它能够比千问AI提供更多平台的购买服务。比如我想在唯品会上下单,就能直接叫阿宝打开对应界面,但千问AI现在就只能提供淘宝一家的购买服务。

值得一提的是,蚂蚁还在左边设置栏的界面中加了一个“日程”服务,这个设计就是Agent的定时任务,可以设置一个定时的每日/每周/每月或是不重复任务。我会更倾向于设置一些简单的任务,比如让它每天11点提醒我点外卖、每周看下财务支出情况之类的工作。

后续,蚂蚁其实可以设计一些应用场景比较高的任务给到用户,让用户直接从一些样本服务里做选择。

总结下来,阿宝确实让用户在“找入口”这件事的成本降低了,但要想做得更丝滑、更准确,在具体任务上,AI支付宝还有进一步优化的空间。

资产

从管钱到赚钱

如果说阿宝是一个帮忙“跑腿”的角色,资产界面则是更贴近支付宝本身能力的“账本管家”。

资产页面的做法,是把用户原来散落在余额宝、小荷包、银行卡、各类理财里头的钱,整合成一份“个人账本”。流动资产、理财资产、保障资产、信用资产,一张一张卡片摊在你面前,每一分钱去了哪儿,一目了然。

考虑到资金安全的问题,蚂蚁的做法很克制,任何涉及资金变动的操作,它都得弹出来让你本人确认。相当于还是只让AI跑腿,管钱的权限永远在用户手里。

在资产管理上,支付宝延续了很多在AI时代尝试的新功能,并把它们融合到了新版AI支付宝中。

比如,针对理财资产的分析,里面也有支付宝两年前发布的理财智能体“蚂小财”的影子。定位为AI理财助手,它能够为用户提供行情分析、持仓建议、财报解读等功能。

在资产界面的理财分析中,新版支付宝同样可以拆解出每一项理财配置,给出可视化图表的同时,它也能进一步针对各项配置给出理财建议。金融垂类服务一直是蚂蚁的优势,叠加上生活服务的积累,可以说蚂蚁在支付方面积累的功能,已经足够给它自己树立壁垒了。

上半部分的四张卡片,让用户能够一目了然地看到自己持有资金的情况。下半部分的“盯收支”和“盯收益”,则是两项可以查看更详细、定制化管理的功能,也是我测评下来觉得设计最有新意的部分。

以“盯收支”界面为例,对比支付宝原版的账单功能,加入AI后,它对账单的分析更细致了,能够针对一个月的支出情况拆解出来,加上它在理财方面的积累,给出详细意见。

比如,阿宝针对我本月的支出情况,点出我在服饰购买上的一笔高额收入是这个月支出高的原因,表示这笔是非日常性的高额支出,不作为参考。它也点出了我在餐饮消费最频繁的是“奶茶咖啡”,半个月就点了九笔订单——打工人日常就是靠茶续命的,扎心了。

除了更细致地分析,借助Agent能力,支付宝的管账能力也能个性化定制了。

对比原来的“账单”功能,如果我想自己管账,我只能按照界面给出的功能,选择管控每月支出的总额。

但在新版AI支付宝界面中,根据AI的分析,它给了我两个建议,一是管控服饰类支出,二是管控餐饮类支出。这相当于我可以用更细分的维度,自由“盯”每一项支出,并且个性设定限额。比如服饰,阿宝自己根据分析给出了一个“占可支配收入30%”的建议,用户可以按照建议设置,也可以自行调整。

可以说,支付宝用自己积累的支付数据叠加AI能力后的记账能力,已经可以乱杀一批当前市面上的记账软件了。对于支付宝来说,这更是它自己对于各项AI能力整合后的成品。

体验完上述功能,我想起了OpenAI、Anthropic每更新一项产品或功能,就有可能“杀死”杀死一个行业。

在AI记账上,蚂蚁现在给出的只是内测阶段的产物,但后续如果继续细化更新,像我这样懒得手动一笔笔记账的人,支付宝显然是一个更好的选择。

结语

新版AI支付宝的内测,是蚂蚁一次声势浩大的转型强调:支付也要跟上AI时代,让用户体验上AI的便捷。

但在内测版“阿宝”的办事能力上,我们看到的现版本,在具体任务的优化空间还有待改进:在意图和入口检索够丝滑,但在具体任务的准确和节省步骤等维度,阿宝做得还比较初级,距离代替用户完成复杂决策的深度操作仍有一段距离。

另一方面,蚂蚁在自己的舒适区——“资产”管理上展现出的AI能力,让人看到了超级App结合垂直场景数据的优势。借助AI,支付宝已经展现了可以对标记账软件的灵活:当AI自动拆解支付宝记录下的消费流水、给出定制化的财务管控建议时,AI+支付的能力又得到了扩展。

在“如何让支付跟上AI时代”的命题上,支付宝的存在感一直很强。

从“支小宝”到AI收、AI付等新功能的推出,可以看出,蚂蚁在AI时代的思考,依旧是以需求为先,通过一系列产品的试水,押注AI时代支付的爆款产品应该长什么样。

注:文/关注前沿科技的,文章来源:光锥智能(公众号ID:guangzhui-tech),本文为作者独立观点,不代表亿邦动力立场。

文章来源:光锥智能

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