Strategy & Planning
Build a solid foundation for your AI product launch with clear strategy, validated use cases, and realistic planning.
Most AI product launches fail not because of technical issues, but due to poor strategic planning. Before writing a single line of code or training your first model, you need clarity on what you're building, why it matters, who it's for, and how you'll bring it to market.
This phase is about asking hard questions, validating assumptions, and creating a roadmap that keeps your team aligned throughout the launch process.
Define Your Vision & Goals
Core Questions to Answer
- What problem are you solving? Be specific. "Make content creation easier" is too vague. "Help marketing teams generate SEO-optimized blog outlines in 5 minutes instead of 2 hours" is clear.
- Why does AI solve it better? Can this problem be solved without AI? If yes, why is AI the superior approach? Cost? Speed? Quality? Scale?
- What's your vision for this product? Where do you see it in 1 year? 3 years? What's the long-term impact?
- What are your success metrics? Define concrete, measurable goals: user acquisition, revenue, engagement, retention, accuracy, or efficiency gains.
Set SMART Goals for Launch
Your launch goals should be Specific, Measurable, Achievable, Relevant, and Time-bound:
- Specific: "Acquire 1,000 beta users" not "Get lots of users"
- Measurable: Track sign-ups, activations, DAU, retention
- Achievable: Based on your resources, timeline, and market
- Relevant: Aligned with your overall business objectives
- Time-bound: "Within 3 months of launch" not "eventually"
Validate Your AI Use Case
Not every problem needs AI, and not every AI solution will be valuable. Before investing months in development, validate that your use case makes sense.
Validation Checklist
- □Customer interviews: Talk to 10-20 potential users. Do they have this problem? How much does it cost them today? Would they pay for your solution?
- □Technical feasibility: Can AI actually solve this problem with current technology? What accuracy is needed? Is that achievable?
- □Data availability: Do you have access to the data needed to build this? Can you collect it? What about privacy/legal constraints?
- □Economic viability: Can you build this profitably? Estimate costs (compute, data, engineering) vs potential revenue.
- □Competitive advantage: Why will you win? Network effects? Proprietary data? Superior model? Better UX? Domain expertise?
Red Flags to Watch For
- Nobody is paying for similar solutions (may indicate lack of real need)
- The problem can be solved more simply without AI
- Required model accuracy is unrealistic (e.g., medical diagnosis at 99.99%)
- No clear path to obtaining necessary training data
- High compute costs make the unit economics impossible
- Strong regulatory barriers that you can't navigate
Understand Your Market
Target Audience Definition
Get crystal clear on who you're building for:
- Demographics: Job titles, company size, industry, geography, budget
- Psychographics: Pain points, goals, behaviors, preferences, tech-savviness
- Current solutions: What are they using today? Why are they dissatisfied?
- Buying process: Who makes decisions? What's their evaluation criteria? Sales cycle length?
- Distribution channels: How will you reach them? Direct sales? PLG? Partnerships? Marketplaces?
Market Size & Opportunity
Estimate the market opportunity using the TAM/SAM/SOM framework:
- TAM (Total Addressable Market): The total market demand if you achieved 100% market share with no constraints
- SAM (Serviceable Addressable Market): The portion of TAM you can realistically reach with your product and business model
- SOM (Serviceable Obtainable Market): The realistic market share you can capture in the short-to-medium term
Example: TAM = $10B (all content marketing software), SAM = $2B (AI-powered content tools for B2B), SOM = $20M (what you can realistically capture in Year 1-2)
Competitive Analysis
Understanding your competitive landscape is critical. AI moves fast—new competitors emerge constantly, and incumbents add AI features quickly.
Competitive Analysis Framework
| Competitor | Features | Pricing | Strengths | Weaknesses |
|---|---|---|---|---|
| Competitor A | List key features | $X/month | What they do well | Where they fall short |
| Competitor B | List key features | $Y/month | What they do well | Where they fall short |
Define Your Differentiation
How will you stand out? Choose 1-3 clear differentiators:
- Superior accuracy/quality: Better model performance on key metrics
- Speed: 10x faster inference or generation
- Cost: Significantly cheaper due to efficiency innovations
- Specialization: Vertical-specific (e.g., AI for legal, healthcare)
- User experience: Dramatically simpler, more intuitive interface
- Integration: Works seamlessly with existing tools customers use
- Privacy/security: On-premise, local-first, or enhanced data controls
- Customization: Fine-tuning or personalization capabilities
Create Your Launch Timeline
Build a realistic timeline with clear milestones. AI products often take longer than expected—factor in time for model training, iteration, and unexpected challenges.
Sample 6-Month Launch Timeline
Month 1: Foundation
- • Finalize strategy and product definition
- • Complete technical architecture design
- • Set up infrastructure and development environment
- • Begin data collection and preparation
Months 2-3: Build MVP
- • Train initial models
- • Build core features and integrations
- • Implement basic UI/UX
- • Internal testing and iteration
Month 4: Private Beta
- • Launch to 20-50 beta users
- • Gather feedback and iterate quickly
- • Improve model based on real usage
- • Refine UX based on user behavior
Month 5: Polish & Prep
- • Comprehensive testing and QA
- • Implement safety and monitoring systems
- • Create documentation and onboarding
- • Prepare marketing materials and launch plan
Month 6: Public Launch
- • Execute go-to-market strategy
- • Launch publicly and acquire initial users
- • Monitor performance and respond to issues
- • Gather feedback for post-launch iteration
Key Takeaways
- Define specific, measurable goals before you start building
- Validate your AI use case through customer interviews and technical feasibility analysis
- Deeply understand your target market, their pain points, and buying behavior
- Analyze competitors and identify clear differentiation
- Create a realistic timeline with milestones—AI products take time to get right
- Be prepared to pivot if validation reveals flaws in your assumptions