The AI Product Spectrum
Not every product needs AI at its core. Some products are fundamentally enabled by AI—they couldn't exist without it. Others benefit from AI features that enhance an already-solid value proposition.
Understanding where your product falls on this spectrum shapes everything from technical architecture to go-to-market strategy.
AI-First Products
These products exist because of AI. Remove the AI, and there's no product.
Examples: GitHub Copilot, Midjourney, ChatGPT, Jasper
Characteristics:
- The core value proposition IS the AI capability
- Users come specifically for what AI enables
- Product quality directly tracks AI model quality
- Competitive moat often depends on data or model advantages
When to go AI-first:
- You're solving a problem that was previously impossible without AI
- The AI capability itself is the differentiation
- You have deep AI/ML expertise on the team
- You're willing to accept that your product will need to evolve as AI capabilities change
Risks:
- Commoditization as AI capabilities become widespread
- Dependence on third-party model providers
- User expectations constantly rising with the technology
AI-Augmented Products
These products have strong value propositions without AI, but AI makes them better.
Examples: Notion AI, Grammarly, Figma with AI features, Shopify's AI tools
Characteristics:
- Core product solves real problems without AI
- AI features reduce friction or add capabilities
- Product can survive AI feature failure gracefully
- AI is a differentiator, not the foundation
When to augment:
- You have an established product with proven value
- AI can meaningfully reduce friction in existing workflows
- Your competitive moat is in the core product, not the AI
- You want lower risk—AI features can fail without killing the product
Risks:
- AI features feel bolted-on rather than integrated
- Users may not discover or value the AI capabilities
- Competitors can add similar features
The Hybrid Approach
Many successful products start AI-augmented and become more AI-first over time:
- Launch with proven non-AI value - Establish product-market fit
- Add AI features that enhance core workflows - Learn what users value
- Gradually make AI more central - As capabilities mature and user trust builds
- Eventually become AI-first - If the market and technology support it
This approach de-risks the journey while building toward an AI-native future.
Decision Framework
Ask yourself:
Is the problem solvable without AI?
- If no → AI-first is your only option
- If yes → AI-augmented might be safer
What's your AI expertise?
- Deep ML team → AI-first is feasible
- API integrators → AI-augmented is more realistic
How stable is the AI capability you need?
- Mature (classification, recommendations) → Either approach works
- Emerging (agents, reasoning) → AI-augmented hedges risk
What's your competitive moat?
- Data or model advantages → AI-first can work
- Distribution, brand, or ecosystem → AI-augmented makes sense
Implementation Considerations
For AI-first products:
- Build abstraction layers for model swapping
- Invest heavily in evaluation and monitoring
- Plan for rapid iteration as capabilities evolve
- Design for graceful degradation
For AI-augmented products:
- Keep AI features optional and progressive
- Ensure core product works without AI
- Use feature flags for easy rollback
- Measure AI feature adoption and value separately
The Bottom Line
There's no shame in being AI-augmented. Some of the most successful products add AI thoughtfully rather than leading with it.
The right choice depends on your problem, your team, your market, and your risk tolerance. What matters is being intentional about which path you're on.
