By skyforbes 
Most PMs think they’re “using AI” because they have ChatGPT open in a tab.
They use it for writing. Maybe some brainstorming. Occasionally to analyze data.
That’s like saying you’re a carpenter because you own a hammer.
There are 10 categories of AI tools that product managers need. ChatGPT covers one of them.
Here’s the complete toolkit, organized by what you actually need to do.
DISCOVERY
1. Prototyping Tools
Why this matters: Prototypes validate ideas in hours instead of weeks.
Tools like Lovable, Bolt, v0, Magic Patterns, and Base44 let you build functional demos without engineering resources. Not mockups — actual working prototypes you can test with users.
When to use: Early-stage concept validation, stakeholder alignment, user testing before you commit engineering time.
The unlock: You can test 5 ideas in the time it used to take to build one.
2. Customer Intelligence
Why this matters: You have thousands of data points. You can’t read them all manually.
Tools like Dovetail, Enterpret, Unwrap, Monterey, and Sprig aggregate insights from support tickets, app reviews, sales calls, and user interviews. They surface patterns across 10,000+ data points automatically.
When to use: Synthesizing feedback at scale, identifying top feature requests, understanding sentiment trends.
The insight: AI customer intelligence is as important as AI prototyping. Both are fundamental to modern discovery.
DELIVERY
3. Vibe Coding
Why this matters: Some PMs go beyond prototypes to actually shipping small features.
Tools like Cursor, Claude Code, OpenAI Codex, Replit, and Warp let you make simple front-end changes, fix bugs, and ship small features without waiting for eng sprints.
When to use: Copy changes, minor UI updates, small bug fixes, simple feature additions that don’t require backend work.
The boundary: Front-end only, simple and certain, no complex logic. But that’s 20–30% of what clogs your backlog.
4. Vibe Experimentation
Why this matters: You can prompt entire experiments now.
Tools like Optimizely, Amplitude, Kameleoon, Pendo, and LaunchDarkly enable prompt-based experimentation. Describe what you want to test, let AI generate the variations, ship quickly.
When to use: A/B tests, feature flags, product experiments that need to launch fast.
The speed: What used to take 2 weeks (design, eng, QA) now takes 2 days.
PRODUCTIVITY
5. Dictation
Why this matters: Voice-to-text everywhere saves hours every week.
Tools like Wispr, SuperWhisper, Apple’s built-in dictation, TalkType, and Speechify turn speaking into writing. Notes, emails, PRDs, Slack messages — all faster by voice.
When to use: First drafts, quick notes, meeting documentation, anything where you can think faster than you can type.
The math: If you write 2 hours per day and dictation is 3x faster, you save 80 minutes daily.
6. General LLMs
Why this matters: You need the right model for the right task.
Claude for best writing and long-context tasks
NotebookLM for sole-context (no hallucination)
Veo 3 + Nano Banana for image and video generation
GPT-4/5 for general reasoning and coding
Each model has strengths. Using the wrong one wastes time and produces worse results.
The strategy: Match the model to the task. Don’t use GPT-4 for everything just because it’s familiar.
7. Meeting Tools
Why this matters: Meetings consume 30–50% of PM time. Automate the grunt work.
Tools like Granola, Fathom, Otter.ai, Tldv, and Fireflies transcribe, summarize, and generate action items automatically.
When to use: Every meeting. No exceptions.
The gain: 30 minutes saved per meeting. If you have 4 meetings per day, that’s 10 hours per week.
AGENTS
8. AI Coding Agents
Why this matters: Autonomous agents that actually ship code for you.
Tools like Linear, CodeGen, Devin, Sweep, and Codium handle bugs and small features end-to-end. You describe what needs fixing, the agent writes the code, creates the PR, and sometimes even deploys.
When to use: Bug fixes, small feature additions, routine maintenance, technical debt cleanup.
The difference: These go beyond assistance. They operate autonomously.
9. Full-Featured Agent Platforms
Why this matters: Complex workflows require orchestration.
Tools like n8n, Make, Activepieces, Workato, and Tray.io handle advanced multi-step automations with conditional logic, error handling, and complex integrations.
When to use: Multi-system workflows, enterprise integrations, complex data pipelines.
The power: These can replace entire parts of your operations stack.
10. Simple Agent Platforms
Why this matters: Most automations don’t need complexity. No-code builders get quick wins.
Tools like Zapier, Lindy, Relay, Bardeen, and Parabola connect apps easily for straightforward automations.
When to use: Simple workflows, quick wins, testing automation ideas before building custom.
Expert insights:
Zapier CTO interview
Lindy CEO interview
Relay CEO interview
The guideline: Start here. Graduate to full-featured platforms when you need more power.
How to Actually Implement This
Looking at 50 tools is overwhelming. Here’s how to approach it:
Phase 1 (Month 1): Foundation
- Pick 1 general LLM (Claude or GPT-4)
- Add 1 meeting tool (Fathom or Otter.ai)
- Start dictation (whatever’s built into your OS)
Phase 2 (Month 2–3): Discovery Tools
- Add prototyping (v0 or Bolt)
- Add customer intelligence (Dovetail or Enterpret)
Phase 3 (Month 4–5): Delivery Tools
- Add experimentation (Amplitude or Kameleoon)
- Add vibe coding if relevant (Cursor)
Phase 4 (Month 6+): Agent Platforms
- Start with simple agents (Zapier or Lindy)
- Graduate to full-featured if needed (n8n or Make)
Don’t try to adopt everything at once. Add one tool per month. Master it. Then add the next.
The Productivity Gap
Here’s what’s happening:
PMs without these tools:
- Spend days on research and synthesis
- Wait weeks for prototypes
- Manually document every meeting
- Create tickets for tiny changes
- Run one experiment at a time
PMs with these tools:
- Research in hours using AI intelligence platforms
- Prototype in hours using no-code builders
- Auto-document meetings with transcription tools
- Ship small changes themselves with vibe coding
- Run parallel experiments with prompt-based testing
The gap compounds every week.
After 3 months, the equipped PM has shipped 3x more, learned 3x faster, and validated 3x more ideas.
After a year, they’re not just ahead. They’re unreachable.
A Note for Leaders and IT
If you’re a product leader or in IT:
Stop buying Copilot for everyone and calling it your AI strategy.
PMs need these 10 categories of tools, not just one general-purpose LLM.
The cost of NOT providing these tools:
- Slower discovery
- Longer delivery cycles
- More meetings
- Less experimentation
- Lower productivity
The cost of providing them: Usually $50–200/month per PM.
The ROI is obvious.
What’s Missing?
These are my top 50 AI tools organized into 10 categories.
But the AI tool landscape evolves weekly. New tools launch. Categories merge. Use cases shift.
The framework matters more than the specific tools.
Think in buckets:
- Discovery (prototyping, customer intelligence)
- Delivery (coding, experimentation)
- Productivity (dictation, LLMs, meetings)
- Agents (simple and complex)
Find the best tool in each bucket for your specific needs. Master it. Replace it when something better emerges.
The PMs who win aren’t those with the most tools. They’re those who systematically cover all the buckets.
ChatGPT is 10% of what you need. Build the other 90%.