写文案时让 AI 起个初稿，开会后用 AI 整理纪要，做汇报前请 AI 帮忙梳理思路……这些用法，正在进入越来越多人的日常工作。
但对业务负责人和企业培训负责人来说，还有几个值得进一步了解的问题：
团队里有哪些人在用 AI？用在什么任务上？产出的结果是否达到交付要求？
有人用得很顺手，经验却很难分享给同事；有人生成内容很快，到了审核环节仍要反复修改；也有人搭出了智能体，但演示结束后，很少有人继续使用。
从个人尝试到团队持续使用，中间还有一些环节需要打通。下一步怎么走，可以先从观察团队当前的应用状态开始。
从四个层面，看看团队走到了哪里
懂 AI、用 AI、驾驭 AI、落地 AI，可以作为观察团队 AI 应用的四个层面。
同一个团队也可能在不同任务上处于不同状态：内容制作已经形成固定方法，经营分析还在探索，客户服务则需要先明确使用边界。可以结合具体任务，看看哪些能力已经具备，哪些还需要补齐。
01 懂 AI：知道能用在哪里，也知道边界在哪里
这一层需要建立基本判断：哪些任务适合 AI 参与？需要准备什么材料？哪些结果必须核查？涉及内部资料时，可以使用哪些工具？
例如，让 AI 总结一份公开报告，与让 AI 处理客户合同，需要考虑的数据范围、使用权限和审核要求就不同。
在开始使用前，把工具、数据和审核要求说清楚，员工才更容易判断什么可以做、怎样做。
自查一下：团队能否说清楚，哪些任务适合 AI 参与，哪些资料不能输入，哪些结果必须人工核查？
02 用 AI：在具体任务中，形成可复用的方法
员工开始用 AI 写文案、做分析、制作视觉素材，是很好的起点。接下来值得关注的是：这些方法能否重复使用，能否帮助其他同事完成类似任务。
以周报为例，如果每次都要重新解释背景、调整格式、补充要求，经验还停留在个人手里。把数据口径、分析要求、输出模板和检查清单整理下来，才更方便团队复用。
自由探索之后，还需要留出整理和验证的环节。否则，有效的方法容易留在个人手里，其他同事仍要从头摸索。
自查一下：团队是否有一项高频任务，已经整理出可复用的操作方法、输出模板和检查清单？
03 驾驭 AI：让输出进入下一环节
当单项任务的方法逐渐稳定，就可以进一步考虑流程衔接。
例如，经营周报涉及数据整理、异常识别、分析初稿、人工核验和行动建议。AI 可以参与其中多个环节，但每一步都需要明确输入、输出以及出错后的处理方式。
这时，Agent、知识库和工作流才有了具体的使用目标。
容易出现的偏差是先搭工具，再找使用场景。如果输入资料、输出标准和责任人尚未明确，工具即使能运行，也可能难以融入日常工作。
自查一下：AI 的输出能否按约定标准进入下一环节？遇到错误或信息缺失时，是否有明确的处理方式？
04 落地 AI：持续观察业务效果
一套方法能运行之后，还需要判断它是否值得持续使用。
处理时间有没有变化？输出是否达到交付要求？审核和返工是否增加？使用者是否愿意继续采用？
调用量可以帮助了解使用情况，但还需要结合交付质量、处理时间和返工情况，判断 AI 是否真正改善了工作。
对业务团队来说，内部知识查询更准确、客户资料准备更充分、经营报告更及时，都可以成为值得验证的目标。
自查一下：与原来的做法相比，这项任务的交付质量、完整处理时间和使用体验发生了什么变化？是否值得持续使用？
从个人会用到团队用好，需要补上什么？
个人用 AI，往往可以随时调整要求、判断结果。进入团队协作后，这些隐含在个人经验里的判断，就需要被说清楚。
首先是共同的交付标准
“帮我写一份客户分析”和“根据已有客户资料，整理业务背景、潜在需求及待确认问题，并标注信息来源”，得到的结果可能差别很大。
团队需要对齐的，包括任务目的、必备信息、输出格式，以及什么情况需要退回修改。标准越清楚，越方便复用和检查。
其次是上下游的衔接
一份分析初稿生成得很快，如果审核者仍需重新寻找依据、核对数据，整个任务的完成时间未必缩短。
因此，评估效果时，要把资料准备、生成、核验、修改和交付一起看，找到真正拖慢任务的环节。
单个环节节省的时间，能否转化为整个任务的改善，需要放到完整流程中验证。
还有持续使用的责任安排
知识资料由谁更新？业务规则变化后谁调整？输出不符合要求时向谁反馈？
这些安排会影响一个试点能否继续运行。人员能力需要与数据、权限、流程和责任安排同步完善，有效的方法才更容易留下来。
下一步，先选一项真实任务
如果准备继续推进团队 AI 应用，可以从一项范围适中的工作开始。选择时，优先考虑四个条件：
- 高频发生：有机会反复实践，便于比较效果。
- 输入清楚：所需文档、数据和背景资料容易准备。
- 结果可检查：能说清楚什么样的输出算合格。
- 风险可控：可以保留人工审核，出现问题时方便纠正。
周报初稿、会议行动项整理、标准资料问答、营销素材初稿，都可以根据团队实际情况进一步筛选。
选定之后，先写一张“任务实践卡”。下面以经营周报为例：
这是一个任务设计示例。真正实践时，还需要结合企业允许使用的工具、数据范围和权限来调整。
先记录原来的工作方式，再在相同交付要求下尝试 AI 辅助。经过几个工作周期，团队就更容易判断：哪些环节有效，哪些需要改进，哪些暂时仍由人工完成更合适。
一次实践最有价值的产出，除了完成任务，还包括留下可复用的模板、检查清单和处理经验。
这张卡可以作为团队启动实践的讨论底稿。先把任务范围、分工和验收方式写清楚，再用实际结果决定是否调整或扩大使用。
按任务安排学习，让能力跟着实践生长
任务明确之后，学习内容也会更容易选择。
需要建立使用边界的团队，可以先补齐 AI 认知、场景识别与安全意识；正在改善日常办公的团队，可以围绕文档、数据分析和视觉内容学习；准备串联多个环节的团队，可以进一步学习 Agent、知识库和工作流；承担业务项目的成员，则需要补充产品设计、需求判断和落地实践方法。
不同岗位，也可以从各自熟悉的任务进入：
市场营销：从内容初稿开始。根据产品资料生成不同渠道的内容，重点检查产品事实、品牌表达和渠道要求。
销售与客户管理：从拜访准备开始。整理客户背景、沟通记录和待确认问题，注意信息来源与客户资料的使用权限。
人力资源：从制度资料查询开始。基于已审核制度探索常见问题解答，明确制度版本、访问权限，以及无法回答时的转交方式。
财务与经营分析：从分析初稿开始。在明确数据口径下整理指标、识别异常，重点核验计算结果与结论依据。
产品与业务策划：从用户反馈整理开始。梳理原始反馈，形成需求讨论稿，再由团队进一步验证需求、判断优先级。
培训负责人可以和业务负责人共同确定任务，再匹配课程，约定一次成果复盘。复盘时，既看员工掌握了什么，也看方法能否在下一次工作中继续使用。
围绕这些需要，极客时间企业版整理了“懂 AI—用 AI—驾驭 AI—落地 AI”的学习路径，并结合营销、销售、人力资源、经营分析等岗位提供场景课程，支持团队按具体任务和能力缺口选学。
员工都会用一点 AI 之后，下一步可以更具体：
选一项工作，说清分工，完成一次实践，再把有效的方法留下来。
如果你正在规划团队 AI 学习，可以从一次真实任务的实践开始。
正好是极客时间 9 周年庆，企业版 SVIP 年卡限时 7 折（原价 ¥2999 / 人 / 年，9 周年价 ¥2099），全年畅学 6000+ AI 与数智化课程，还加赠 ¥199 极客喵礼盒。
想先试试看的话，扫码领取 30 天企业试学福利，结合实际任务体验相关课程，再决定是否长期使用。
你最希望 AI 帮团队改善哪项具体工作？欢迎在评论区分享。
附：候选标题（3 选 1）
方案 A（推荐）：团队 AI 应用的 4 个层次，你们在哪一层？推荐理由：用「4 个层次」+「你们在哪一层」形成天然对标钩子，读者看到会不自觉对号入座；调性依然克制专业，没有用冲击型数字，符合品牌调性；与正文四阶结构直接呼应。
方案 B：员工都会用 AI 了，然后呢？推荐理由：更短、更口语、更有悬念感，「然后呢」三个字留白感强，适合公众号信息流。缺点是专业感稍弱。
方案 C：员工都会用一点 AI，团队的下一步在哪？推荐理由：改动最小，保留了原题的核心表达，把「怎么走」改成「在哪」，从「问路」变成「定位」，多了一点点对
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
When writing, allow AI to start a first draft, then use AI after the meeting to sort out the minutes, and ask AI to help with the ideas before reporting. These usages are entering the daily work of a growing number of people.
There are, however, a number of further questions for managers of business and business training:
Who's on the team with AI? On what mission? Did the output result meet delivery requirements?
There are those who use them very well, but experience is difficult to share with colleagues; those who produce content quickly and are subject to repeated changes in the audit chain; and those who build AI agent, but few continue to use it after the presentation.
From individual attempts to continuous use by teams, there are also links that need to be made accessible. The way next step went could start with the current operational status of the Mission team.
On four levels, see where the team went.
Understanding AI, using AI, managing AI, landing AI can serve as the four levels of mission team AI application.
The same team may also be in different situations: content production has developed a fixed method, business analysis is being explored, and client services need to be clearly bordered. It can be seen in the context of specific mandates what capacity is available and what needs to be filled.
AI: Know where it works, know where the border is.
This level requires a basic judgement: which tasks fit AI participation? What materials do you need? What results must be verified? What tools can be used when internal information is involved?
For example, allowing AI to summarize a public report is not the same as allowing AI to process client contracts, the scope of data to be considered, the use of permission and the requirements for review.
Prior to the start of use, clarity of tools, data and clearance requirements makes it easier for staff to judge what can and how to do it.
Self-check: Can the team clarify which tasks fit AI, which information cannot be entered and which results must be manually verified?
02 Develop reusable methods in specific missions using AI:
It's a good starting point for employees to start writing files, doing analyses and making visual assets. Of concern next is whether these methods can be reused and whether they can help other colleagues in similar tasks.
In the case of weekly reports, experience remains in the hands of individuals if the context, format and additional requirements are to be re-explained each time. It is easier to re-use teams by sorting out data calibres, analytical requirements, output templates and checklists.
After free exploration, there is also a need for a sorting and validation chain. Otherwise, effective methods are easily left in the hands of individuals, and other colleagues still have to search for them.
Self-check: Does the team have a high-frequency task and a reusable operating method, output template and checklist have been compiled?
03 Managing AI: Get the output to the next step.
As the individual task approach stabilizes, further consideration can be given to the process interface.
For example, the weekly reports cover data collation, unusual identification, analysis of first drafts, manual verification and recommendations for action. AI can be involved in several of them, but each step requires clear input, output and post-fault processing.
It was then that Agent, the knowledge base and workflow had specific use targets.
The easy bias is to put the tools in place and then find the scene. Even when tools are operational, they may be difficult to integrate into day-to-day work if data entry, output standards and responsibility are not clear.
Check yourself: Can AI's output go to the next step according to the agreed criteria? Are there clear ways to deal with errors or missing information?
AI: Continuous observation of operational effectiveness
Once a methodology is operational, it needs to be judged whether it is worthy of continued use.
Has the processing time changed? Does the output meet the delivery requirements? Has there been an increase in audits and returns? Are users willing to continue using them?
Calls can help to understand usage, but there is also a need to determine whether AI has actually improved its performance by combining delivery quality, processing time and back-to-work.
For business teams, more accurate in-house knowledge queries, better client information readiness and more timely business reports can be worthy of validation.
Self-check: How has the quality of delivery, the time of complete processing and the experience with its use changed compared to the previous practice? Is it worth continuing?
From personal use to team use. What do you need?
Individual AIs are often readily adaptable to requirements and judgement. These findings, implicit in personal experience, need to be made clear when entering into teamwork.
First, common delivery standards
The results may vary significantly between “writing a client analysis for me” and “collating business background, potential needs and issues to be identified based on available client information, and indicating sources of information”.
The team needs to be coherent, including the purpose of the mission, the information required, the output format and what needs to be returned for revision. The clearer the standards, the easier it is to reuse and check.
Next is the upstream-downstream connection.
A first draft of the analysis was produced quickly, and if the reviewers still had to search for a new basis and reconcile the data, the completion time of the entire task would not necessarily be shortened.
Therefore, in assessing the effects, the preparation, generation, validation, modification and delivery of the information should be seen together, so as to find the link that really slows down the task.
The time saved by a single link needs to be validated in a complete process to determine whether it can be translated into improvements for the entire mission.
There are also liability arrangements for continued use.
Who updates the knowledge materials? Who adjusts the business rules as they change? To whom do you give feedback when the output does not meet the requirements?
These arrangements will affect the continuation of a pilot. Staff capacity needs to be synchronized with data, permission, process and accountability arrangements to make it easier for effective methods to remain.
next step, pick a real task first.
If you're ready to continue with the team AI application, you can start with a modest scope. In selecting, priority is given to four conditions:
- HF occurrence: an opportunity to practice it over and over again to allow comparison of results.
- Enter clear: the required documents, data and background information are easily prepared.
- Results can be checked: it is clear what output is good.
- Risks are manageable: manual audits can be maintained and problems can be easily corrected.
The first draft of the weekly newspaper, the compilation of conference actions, standard information questions and answers, and the first draft of the marketing of asset, can be further screened on the basis of the actual team situation.
After selection, write a mission practice card. The following is an example:
This is an example of a mission design. In real practice, adjustments also need to be made in conjunction with the tools, data range and permission that enterprises are allowed to use.
Record the old working methods and try AI support under the same delivery requirements. After several work cycles, it is easier for the team to judge which links are effective, which need improvement and which are more appropriately performed manually for the time being.
The most valuable output of one exercise, in addition to fulfilling its mandate, includes the retention of reusable templates, checklists and processing experience.
This card can be used as a background for the discussion of team start-up practice. The scope of the mandate, the division of labour and the acceptance check approach were first clarified, and then the actual results were used to determine whether to adjust or expand its use.
Mission-based learning so that abilities grow with practice
Once the mandate is clear, learning content will also be easier to choose.
Border-use teams need to be created to complement AI cognitive, scenario-based and security awareness; teams working to improve day-to-day operations can learn around documentation, data analysis and visual content; teams preparing multiple links can learn further about Agent, the knowledge base and workflow; and members taking on business projects need to complement product design, demand judgement and landing practices.
Different positions can also be entered from their familiar tasks:
Marketing: Start with the first draft of content. The content of different sources of product information is examined primarily for product facts, brand expression and channel requirements.
Sales and customer management: Start with the preparation of a visit. Collating client background, communication records and pending confirmation issues, paying attention to the use of permission for information sources and client information.
Human resources: start with system information. The system is based on a system that explores frequently asked questions, identifies the version of the system, visits to permission, and how it is transmitted if it cannot be answered.
Financial and business analysis: starting with the first draft of the analysis. Collapse indicators with clear data calibres, identify anomalies and focus on the basis of calculations and conclusions.
Product and business planning: Start with user feedback. Combine the original feedback and form the demand discussion draft, which will be further validated and prioritized by the team.
The training manager can work with the head of the operation to define the tasks, re-match the course and agree on a results reset. When doing so, it depends both on what the staff member knows and on whether the methodology will continue to be used in the next job.
In response to these needs, the Optic Time Business Edition organized “Acknow-Ai-Ai-Managing AI-Lost-Ai” learning paths and provided scenario courses in combination with marketing, marketing, human resources, business analysis, etc., to support teams in selecting by specific tasks and capacity gaps.
next step can be more specific:
Select a task to clarify the division of labour, complete one exercise and leave the effective method behind.
If you're planning an AI study, you can start with a real task exercise.
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Which specific job do you want the team to improve? We welcome the sharing in the comment area.
Annex: Candidate Title (3 Option 1)
Option A (recommended): 4 levels of team AI application, which level are you on? Reasons for recommendation: using the "4 Layers" plus the "where are you?" to form a natural pair of hooks, readers see that they are not conscious of each other; modulations remain professional, do not use shock-type numbers and are brand-modified; and respond directly to the four-level structure of the text.
Option B: Employees will use AI, then? Reason for recommendation: Shorter, more spoken, more suspense, and the word "and then" is strong and suitable for WeChat Official Account information streams. The disadvantage is that there is a weak sense of professionalism.
Option C: Employees will use a little bit of AI. Where's the team's next step? Reason for recommendation: Minimal change, retention of the core expression of the original title, replacement of "how to go" with "where", from "the question path" to "location", slightly more yes
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