Tools Define Efficiency, Efficiency Defines Your Ceiling
Product management has an interesting characteristic: you don't write code, you don't create designs, you don't run ad campaigns — but you need to collaborate with everyone who does. Your core output is decisions and documentation, and your efficiency largely depends on the tools you use.
Before 2024, the PM toolkit was fairly stable: Axure for prototyping, Excel for data, Jira for requirements, Confluence for documentation. But the AI tool explosion in 2025 completely changed the landscape. By 2026, PMs who can't use AI tools may be operating at half the efficiency of their peers.
In this article, we'll walk through the best tools for each stage of a product manager's daily workflow, covering both classic tools and new AI-powered ones. This isn't a simple list — we'll explain the core use cases and selection logic for each tool.
1. Requirements Management: Keeping Requirements Organized
Feishu (Lark) Multidimensional Tables
If you're working at a Chinese tech company, Feishu's multidimensional tables are arguably the best solution for requirements management. It's essentially a customizable database where you can use Kanban views to manage requirement status, table views for priority sorting, and Gantt chart views for scheduling.
Key advantages:
- Seamlessly integrated with Feishu Docs and Feishu Meetings — meeting conclusions can be directly linked to requirement cards
- Supports automation rules, such as automatic notifications when requirement status changes
- Powerful filtering and grouping capabilities across multiple dimensions like version, priority, and assignee
Jira
If you're at a multinational or overseas-focused company, Jira remains the standard. Jira's strength lies in its mature workflow engine, which can define complex requirement transition rules. However, it has a steep learning curve and high configuration costs — small teams may find it too heavy.
In 2026, Jira has added AI features that can auto-generate requirement descriptions, estimate effort, and identify duplicate requirements. Honestly though, these AI features are still fairly basic — more nice-to-have than game-changing.
Linear
A project management tool that's been extremely popular in Silicon Valley recently, with a minimalist interface and blazing speed. If your team is engineering-driven, Linear's experience is significantly better than Jira's. Its keyboard shortcuts are exceptionally well-designed, making operations highly efficient once mastered.
Selection advice: Chinese teams should prioritize Feishu multidimensional tables; multinational/overseas teams should use Jira or Linear. Don't use two requirements management tools simultaneously — that's a common pitfall many teams have experienced.
2. Prototyping: From Ideas to Visualization
Figma
By 2026, Figma has become the absolute mainstream for prototyping. Whether it's high-fidelity prototypes or low-fidelity wireframes, Figma handles it all. Its core advantages:
- Runs in the browser — no client installation needed, share a link and everyone sees the latest version
- Mature component system — large companies typically have their own Figma design systems
- Dev Mode lets engineers directly view annotations and code snippets
- Rich plugin ecosystem with numerous productivity plugins
Axure
A veteran prototyping tool that still has a significant user base. Axure's advantage lies in its interaction logic expression capabilities — you can create highly complex interactive prototypes including conditional logic, variables, and dynamic panels. If you need to demonstrate complex B2B form interactions, Axure might be more suitable than Figma.
However, Axure's problems are also obvious: weak collaboration capabilities, cumbersome file management, and high learning costs. New PMs should go straight to Figma.
AI Prototyping Tools: Motiff / Instant AI
A wave of AI prototyping tools emerged in 2025-2026 that generate prototype pages from text descriptions. Currently, the quality of prototypes generated by these tools varies widely — they're suitable for quickly creating demos to validate ideas, but not for formal requirement reviews.
Selection advice: Use Figma as your primary tool, supplement with Axure for complex B2B interactions, and use AI prototyping tools for quick idea validation.
3. Data Analysis: The Foundation of Data-Driven Decisions
SQL
This isn't a tool but a skill — and it's the cornerstone of PM data analysis capabilities. In 2026 big tech interviews, SQL is almost always tested. You don't need to write queries as complex as a DBA, but you should be able to independently handle:
- Basic queries: SELECT, WHERE, ORDER BY, LIMIT
- Multi-table joins: JOIN (LEFT JOIN is most commonly used)
- Aggregate analysis: GROUP BY, HAVING, COUNT, SUM, AVG
- Window functions: ROW_NUMBER, LAG/LEAD (bonus points in interviews)
Recommended learning path: Start with SQL problems on LeetCode, then practice with real business data.
Excel / Google Sheets
Don't underestimate Excel — it's still the most frequently used data analysis tool for PMs. Pivot tables, VLOOKUP, and conditional formatting are used far more frequently in daily work than SQL.
In 2026, both Excel and Google Sheets have added AI assistants that can generate formulas and charts from natural language descriptions. This has significantly lowered the barrier to entry.
BI Tools: Tableau / Metabase / Feishu BI
If you need to create regular data dashboards, BI tools are essential. Large Chinese tech companies typically have proprietary BI platforms, but the principles are the same:
- Tableau: Most powerful features, best visualizations, but expensive with a steep learning curve
- Metabase: Open-source and free, easy to get started, suitable for small to medium teams
- Feishu BI: Integrated with the Feishu ecosystem, ideal for teams already using Feishu
Selection advice: SQL is a fundamental skill you must learn, use Excel daily, and choose BI tools based on your company's tech stack.
4. AI Tools: The 2026 PM Efficiency Multiplier
ChatGPT / Claude
Large language models are the most important new tool for PMs in 2026, bar none. Their application scenarios cover virtually every aspect of PM work:
- Requirements analysis: Input user feedback and let AI categorize and extract core requirements
- Competitive analysis: Have AI help you compile feature comparison tables
- PRD writing: Provide the requirement background and core logic, let AI generate a PRD draft
- Data analysis: Paste data to AI and let it identify anomalies and trends
- Interview preparation: Have AI simulate an interviewer for product design and behavioral interview practice
Key mindset: AI is your assistant, not your replacement. You need sufficient professional judgment to evaluate the quality of AI output.
Cursor
If you're a PM looking to improve your technical understanding, Cursor is worth trying. It's an AI-powered code editor that can help you understand code logic, write simple scripts, and process data.
Typical PM use cases for Cursor:
- Writing SQL queries (AI-assisted generation and optimization)
- Writing Python scripts for data cleaning
- Understanding engineers' technical proposals (paste code and let AI explain it)
Midjourney / DALL-E
AI image generation tools that PMs can use to quickly generate product concept images, marketing materials, and presentation graphics. While they can't replace professional designers, they're extremely useful during early-stage concept exploration.
Gamma / Beautiful.ai
AI-powered presentation tools that generate complete slide decks from outlines. For PMs who frequently need to present, these tools can save enormous amounts of formatting time.
5. Project Management and Collaboration
Feishu Docs
The standard for document collaboration at Chinese tech companies. PRDs, meeting notes, weekly reports, and retrospective documents are all typically done in Feishu Docs. Its real-time collaboration experience is excellent, and the commenting and @mention features are very practical.
Notion
If you're at a multinational company or using tools personally, Notion offers greater flexibility. It can simultaneously serve as a documentation tool, knowledge base, and project management tool. However, Notion's problem is that it's too flexible — it's easy to fall into the trap of "building systems," spending excessive time on the tool itself rather than actual work.
Tencent Docs / Shimo Docs
Lightweight alternatives suitable for teams not using Feishu. Functionally adequate, but ecosystem integration isn't as strong as Feishu's.
6. Competitive Analysis Tools
Qimai / Chandashi
App Store data analysis tools that let you view competitors' download trends, keyword rankings, and version update records. Essential for PMs working on mobile products.
SimilarWeb
A website traffic analysis tool that shows competitors' traffic sources, user profiles, and popular pages. Commonly used by PMs working on web products or overseas businesses.
Enterprise Credit Information Systems / Tianyancha
For understanding competitors' basic information, funding status, and equity structure. When preparing competitive analysis reports, this information helps you assess competitors' strategic direction.
7. The Underlying Logic of Tool Selection
After discussing all these tools, let's talk about the principles of tool selection:
1. Follow your team — don't be a lone wolf
The value of tools lies in collaboration. If your team uses Feishu and you insist on Notion, you're only increasing communication costs.
2. Master one first, then expand
Rather than knowing a little about every tool, master one tool thoroughly. Being an expert in Figma is more valuable than knowing ten prototyping tools superficially.
3. AI tools are a bonus, not a requirement
AI tools are indeed powerful in 2026, but interviewers care more about your product thinking and business understanding. AI tools are efficiency multipliers, but the prerequisite is that your foundational capabilities must be solid.
4. Tools serve goals — don't use tools for the sake of using tools
I've seen too many PMs spend excessive time building Notion knowledge bases and configuring Jira workflows, while their actual product work suffers. Tools are means, not ends.
Summary
The core change in the 2026 PM toolkit is the addition of AI tools. But the underlying logic hasn't changed: requirements management must be clear, prototype expression must be accurate, data analysis must be solid, and collaboration must be efficient.
Tools will continue to evolve, but the thinking patterns and professional capabilities of the people using them are the real competitive advantage.
Rather than agonizing over which tools to use, focus on mastering the tools you have and doing solid product work. Tools are just amplifiers — they amplify the capabilities you already possess.
