recommendation-canvas by deanpeters/product-manager-skills
npx skills add https://github.com/deanpeters/product-manager-skills --skill recommendation-canvas使用一个结构化画布来评估和提出 AI 产品解决方案,该画布评估业务成果、客户成果、问题定义、解决方案假设、定位、风险和价值论证。利用此画布为利益相关者和决策者(尤其是在提出具有较高不确定性和风险的 AI 驱动功能或产品时)构建全面、有说服力的建议。
这不是功能规格说明书——这是一份战略提案,旨在阐明 为什么 这个 AI 解决方案值得构建、哪些 假设需要验证,以及 如何 衡量成功。
该画布为 Dean Peters 的 Productside "产品经理的 AI 创新" 课程创建,它将多个产品管理框架综合为一个战略视图:
核心组成部分:
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使用 template.md 获取完整的填写结构。
在填写画布之前,请确保您拥有:
如果缺少背景信息: 先进行探索工作。此画布是综合洞察的工具——它不创造洞察。
对企业有什么好处?使用此格式:
[方向] [指标] [成果] [背景] [验收标准]
示例:
质量检查:
对客户有什么好处?使用此格式:
[方向] [指标] [成果] [从用户角色视角的背景] [验收标准]
示例:
质量检查:
使用 skills/problem-statement/SKILL.md 中的问题定义叙述:
## 问题陈述
### 问题陈述叙述
- [用户角色描述:用 2-3 句话从用户视角讲述其故事]
- [示例:"Sarah 是一名管理着 10 个客户的自由设计师。她每月花费 8 小时手动追踪发票和催收逾期付款。等到她跟进时,一些客户已经转向其他设计师,这使她损失了收入并损害了关系。"]
质量检查:
使用 skills/epic-hypothesis/SKILL.md 中的史诗假设格式:
## 解决方案假设
### 假设陈述
**如果我们** [为目标用户角色采取行动或提供解决方案]
**为** [目标用户角色]
**那么我们将** [达到或实现期望的成果]
示例:
定义轻量级实验来验证假设:
### 微小的探索行动
**我们将通过以下方式测试我们的假设:**
- [实验 1:构建 AI 提醒系统原型并与 5 名自由职业者测试]
- [实验 2:对 20 名用户进行手动提醒与 AI 定时提醒的 A/B 测试]
- [实验 3:在 2 周后调查用户感知价值]
质量检查:
定义验证措施:
### 生命迹象
**如果在** [时间范围] **内我们观察到以下情况,我们就知道我们的假设是有效的:**
- [定量成果:例如,"80% 的用户通过 AI 系统发送提醒"]
- [定性成果:例如,"10 名用户中有 8 名报告每月节省 5 小时以上"]
使用 skills/positioning-statement/SKILL.md 中的定位陈述格式:
## 定位陈述
### 价值主张
**对于** [目标客户/用户角色]
**他们需要** [未满足需求的陈述]
[产品名称]
**是一个** [产品类别]
**能够** [效益陈述,关注成果]
### 差异化陈述
**与** [主要竞争对手或竞争领域] **不同**
[产品名称]
**提供** [独特的差异化,关注成果]
## 假设与未知
- **[假设 1]** - [描述,例如:"我们假设用户会信任 AI 生成的提醒"]
- **[假设 2]** - [描述,例如:"我们假设付款时间优化会提高响应率"]
- **[未知 1]** - [描述,例如:"我们不知道用户更喜欢电子邮件还是短信提醒"]
质量检查:
## 需要调查的问题/风险
- **政治:** [例如:"AI 生成通信的法规变化"]
- **经济:** [例如:"经济衰退降低了对高级功能付费的意愿"]
- **社会:** [例如:"用户可能认为 AI 提醒缺乏人情味或过于主动"]
- **技术:** [例如:"AI 模型准确性可能随时间推移而下降,如果不重新训练"]
- **环境:** [例如:"AI 处理的能源成本"]
- **法律:** [例如:"存储客户电子邮件模式的 GDPR 合规性"]
## 需要监控的问题/风险
- **政治:** [例如:"欧盟市场潜在的 AI 法规"]
- **经济:** [例如:"汇率波动影响国际客户"]
- **社会:** [例如:"围绕自动化通信的规范变化"]
- **技术:** [例如:"拥有更好模型的新兴 AI 竞争对手"]
- **环境:** [例如:"利益相关者对碳足迹的关注"]
- **法律:** [例如:"未来的数据隐私法"]
## 价值论证
### 这有价值吗?
- [绝对有 / 有,但有注意事项 / 没有,建议替代方案 / 绝对没有!]
### 解决方案论证
<!-- 写这些是为了说服 C 级高管 -->
我们认为这是一个有价值的想法。原因如下:
1. **[论证 1]** - [描述,例如:"解决了我们目标细分市场的首要痛点"]
2. **[论证 2]** - [描述,例如:"使我们与只提供手动提醒的竞争对手区分开来"]
3. **[论证 3]** - [描述,例如:"技术风险低——利用现有的 AI 基础设施"]
使用 SMART 指标:
## 成功指标
1. **[指标 1]** - [例如:"80% 的活跃用户在 3 个月内采用 AI 提醒"]
2. **[指标 2]** - [例如:"6 个月内,花在付款跟进上的平均时间减少 50%"]
3. **[指标 3]** - [例如:"6 个月内,发票功能的净推荐值从 6 提高到 8"]
## 后续步骤
1. **[后续步骤 1]** - [例如:"与 10 名 Beta 用户进行为期 2 周的原型测试"]
2. **[后续步骤 2]** - [例如:"构建用于提醒时间优化的轻量级 AI 模型"]
3. **[后续步骤 3]** - [例如:"进行 GDPR 影响的法律审查"]
4. **[后续步骤 4]** - [例如:"向高管团队展示发现以决定是否推进"]
5. **[后续步骤 5]** - [例如:"如果验证通过,则将其加入 Q2 路线图"]
查看 examples/sample.md 获取完整的建议画布示例。
迷你示例摘录:
### 业务成果
- 在 12 个月内,将来自自由职业用户的月度经常性收入提高 20%
### 解决方案假设
**如果我们** 提供 AI 驱动的发票提醒
**为** 自由设计师
**那么我们将** 减少 70% 花在跟进上的时间
症状: "业务成果:增加收入。产品成果:改善用户体验。"
后果: 无法衡量或问责。
解决方法: 使用成果公式:[方向] [指标] [成果] [背景] [验收标准]。要具体。
症状: 问题陈述是"我们需要 AI 驱动的 X"
后果: 在未验证问题的情况下就跳到了解决方案。
解决方法: 从用户角度定义问题。让解决方案假设从经过验证的痛点中产生。
症状: 假设 → 直接进入路线图,没有实验
后果: 构建错误事物的风险很高。
解决方法: 定义 2-3 个轻量级实验。在投入工程资源之前进行测试。
症状: "政治:法规可能会改变"
后果: 风险分析流于形式,不可操作。
解决方法: 要具体:"存储客户电子邮件时间数据的 GDPR 合规性需要法律审查。"
症状: "这很有价值,因为客户会喜欢它"
后果: 对高管没有说服力。
解决方法: 使用数据:"根据用户研究,解决了首要痛点。20% 的流失率降低 = 50 万美元年度经常性收入。技术风险低。"
skills/problem-statement/SKILL.md — 为问题叙述提供信息skills/epic-hypothesis/SKILL.md — 为解决方案假设结构提供信息skills/positioning-statement/SKILL.md — 为定位部分提供信息skills/proto-persona/SKILL.md — 定义目标用户角色skills/jobs-to-be-done/SKILL.md — 为客户成果提供信息https://github.com/deanpeters/product-manager-prompts 仓库中的 prompts/recommendation-canvas-template.md。技能类型: 组件 建议文件名: recommendation-canvas.md 建议放置位置: /skills/components/ 依赖项: 引用 skills/problem-statement/SKILL.md、skills/epic-hypothesis/SKILL.md、skills/positioning-statement/SKILL.md、skills/proto-persona/SKILL.md、skills/jobs-to-be-done/SKILL.md
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Evaluate and propose AI product solutions using a structured canvas that assesses business outcomes, customer outcomes, problem framing, solution hypotheses, positioning, risks, and value justification. Use this to build a comprehensive, defensible recommendation for stakeholders and decision-makers—especially when proposing AI-powered features or products that carry higher uncertainty and risk.
This is not a feature spec—it's a strategic proposal that articulates why this AI solution is worth building, what assumptions need validating, and how you'll measure success.
Created for Dean Peters' Productside "AI Innovation for Product Managers" class, the canvas synthesizes multiple PM frameworks into one strategic view:
Core Components:
Use template.md for the full fill-in structure.
Before filling out the canvas, ensure you have:
skills/problem-statement/SKILL.md)skills/proto-persona/SKILL.md)If missing context: Run discovery work first. This canvas synthesizes insights—it doesn't create them.
What's in it for the business? Use this format:
[Direction] [Metric] [Outcome] [Context] [Acceptance Criteria]
Example:
Quality checks:
What's in it for the customer? Use this format:
[Direction] [Metric] [Outcome] [Context from persona's POV] [Acceptance Criteria]
Example:
Quality checks:
Use the problem framing narrative from skills/problem-statement/SKILL.md:
## The Problem Statement
### Problem Statement Narrative
- [Persona description: 2-3 sentences telling the persona's story from their POV]
- [Example: "Sarah is a freelance designer managing 10 clients. She spends 8 hours/month manually tracking invoices and chasing late payments. By the time she follows up, some clients have already moved to other designers, costing her revenue and damaging relationships."]
Quality checks:
Use the epic hypothesis format from skills/epic-hypothesis/SKILL.md:
## Solution Hypothesis
### Hypothesis Statement
**If we** [action or solution on behalf of target persona]
**for** [target persona]
**Then we will** [attain or achieve desirable outcome]
Example:
Define lightweight experiments to validate the hypothesis:
### Tiny Acts of Discovery
**We will test our assumption by:**
- [Experiment 1: Prototype AI reminder system and test with 5 freelancers]
- [Experiment 2: A/B test manual vs. AI-timed reminders for 20 users]
- [Experiment 3: Survey users on perceived value after 2 weeks]
Quality checks:
Define validation measures:
### Proof-of-Life
**We know our hypothesis is valid if within** [timeframe]
**we observe:**
- [Quantitative outcome: e.g., "80% of users send reminders via the AI system"]
- [Qualitative outcome: e.g., "8 out of 10 users report saving 5+ hours/month"]
Use the positioning statement format from skills/positioning-statement/SKILL.md:
## Positioning Statement
### Value Proposition
**For** [target customer/user persona]
**that need** [statement of underserved need]
[product name]
**is a** [product category]
**that** [statement of benefit, focusing on outcomes]
### Differentiation Statement
**Unlike** [primary competitor or competitive arena]
[product name]
**provides** [unique differentiation, focusing on outcomes]
## Assumptions & Unknowns
- **[Assumption 1]** - [Description, e.g., "We assume users will trust AI-generated reminders"]
- **[Assumption 2]** - [Description, e.g., "We assume payment timing optimization increases response rates"]
- **[Unknown 1]** - [Description, e.g., "We don't know if users prefer email or SMS reminders"]
Quality checks:
## Issues/Risks to Investigate
- **Political:** [e.g., "Regulatory changes to AI-generated communications"]
- **Economic:** [e.g., "Economic downturn reduces willingness to pay for premium features"]
- **Social:** [e.g., "Users may perceive AI reminders as impersonal or pushy"]
- **Technological:** [e.g., "AI model accuracy may degrade over time without retraining"]
- **Environmental:** [e.g., "Energy costs of AI processing"]
- **Legal:** [e.g., "GDPR compliance for storing customer email patterns"]
## Issues/Risks to Monitor
- **Political:** [e.g., "Potential AI regulation in EU markets"]
- **Economic:** [e.g., "Exchange rate fluctuations affecting international customers"]
- **Social:** [e.g., "Changing norms around automated communication"]
- **Technological:** [e.g., "Emerging AI competitors with better models"]
- **Environmental:** [e.g., "Carbon footprint concerns from stakeholders"]
- **Legal:** [e.g., "Future data privacy laws"]
## Value Justification
### Is this Valuable?
- [Absolutely yes / Yes with caveats / No with suggested alternatives / Absolutely NO!]
### Solution Justification
<!-- Write these to convince C-level executives -->
We think this is a valuable idea. Here's why:
1. **[Justification 1]** - [Description, e.g., "Addresses the #1 pain point for our target segment"]
2. **[Justification 2]** - [Description, e.g., "Differentiates us from competitors who only offer manual reminders"]
3. **[Justification 3]** - [Description, e.g., "Low technical risk—leverages existing AI infrastructure"]
Use SMART metrics (Specific, Measurable, Attainable, Relevant, Time-Bound):
## Success Metrics
1. **[Metric 1]** - [e.g., "80% of active users adopt AI reminders within 3 months"]
2. **[Metric 2]** - [e.g., "Average time spent on payment follow-ups decreases by 50% within 6 months"]
3. **[Metric 3]** - [e.g., "Net Promoter Score for invoicing feature increases from 6 to 8 within 6 months"]
## What's Next
1. **[Next step 1]** - [e.g., "Run 2-week prototype test with 10 beta users"]
2. **[Next step 2]** - [e.g., "Build lightweight AI model for reminder timing optimization"]
3. **[Next step 3]** - [e.g., "Conduct legal review of GDPR implications"]
4. **[Next step 4]** - [e.g., "Present findings to exec team for go/no-go decision"]
5. **[Next step 5]** - [e.g., "If validated, add to Q2 roadmap"]
See examples/sample.md for a full recommendation canvas example.
Mini example excerpt:
### Business Outcome
- Increase by 20% MRR from freelance users within 12 months
### Solution Hypothesis
**If we** provide AI-powered invoice reminders
**for** freelance designers
**Then we will** reduce time spent on follow-ups by 70%
Symptom: "Business outcome: increase revenue. Product outcome: improve UX."
Consequence: No measurability or accountability.
Fix: Use the outcome formula: [Direction] [Metric] [Outcome] [Context] [Acceptance Criteria]. Be specific.
Symptom: Problem statement is "We need AI-powered X"
Consequence: You've jumped to solution without validating the problem.
Fix: Frame problem from user perspective. Let the solution hypothesis emerge from validated pain points.
Symptom: Hypothesis → straight to roadmap, no experiments
Consequence: High risk of building the wrong thing.
Fix: Define 2-3 lightweight experiments. Test before committing engineering resources.
Symptom: "Political: regulations might change"
Consequence: Risk analysis is theater, not actionable.
Fix: Be specific: "GDPR compliance for storing client email timing data requires legal review."
Symptom: "This is valuable because customers will like it"
Consequence: Not convincing to execs.
Fix: Use data: "Addresses #1 pain point per user research. 20% churn reduction = $500k ARR. Low tech risk."
skills/problem-statement/SKILL.md — Informs the problem narrativeskills/epic-hypothesis/SKILL.md — Informs the solution hypothesis structureskills/positioning-statement/SKILL.md — Informs positioning sectionskills/proto-persona/SKILL.md — Defines target personaskills/jobs-to-be-done/SKILL.md — Informs customer outcomesprompts/recommendation-canvas-template.md in the https://github.com/deanpeters/product-manager-prompts repo.Skill type: Component Suggested filename: recommendation-canvas.md Suggested placement: /skills/components/ Dependencies: References skills/problem-statement/SKILL.md, skills/epic-hypothesis/SKILL.md, skills/positioning-statement/SKILL.md, skills/proto-persona/SKILL.md, skills/jobs-to-be-done/SKILL.md
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