Product Roadmap Planning
Build features that strengthen your tier positioning and win more deals
Stop feature creep. Use tier-based strategy to decide what to build next.
🎯 The Strategic Question Every AI Vendor Faces
Should you move up-tier (add more sophisticated AI capabilities) or down-tier (simplify for broader market access)?
⬆️ Moving Up-Tier
Example: Tier 2 (custom ML) → Tier 3 (deep learning)
⬇️ Moving Down-Tier
Example: Tier 2 (custom ML) → Tier 1 (basic ML/API)
Most vendors try to do both—and end up doing neither well. Your roadmap should pick ONE direction and commit.
đź“‹ Tier-Based Roadmap Framework
Use your current tier positioning to guide feature prioritization. Different tiers need fundamentally different product strategies.
Tier 0-1: Simplicity & Speed Strategy
"Instant value, zero ML expertise required"
- 1.More pre-built workflows: Industry templates, vertical-specific configs, out-of-box automation
- 2.Faster time-to-value: 1-click deployment, auto-configuration, guided setup wizards
- 3.No-code customization: Visual builders, drag-drop interfaces, business-user tools
- 4.Integration marketplace: Pre-built connectors to common systems (Salesforce, HubSpot, etc.)
- âś—Advanced ML features requiring data science skills
- âś—Custom model training (moves you to Tier 2)
- âś—Technical configuration options that confuse non-technical users
Tier 2-3: Customization & Performance Strategy
"Custom-trained models that outperform generic solutions"
- 1.Model performance: Better algorithms, hyperparameter tuning, ensemble methods
- 2.Feature engineering automation: Auto-feature creation, relevance scoring, data quality checks
- 3.MLOps capabilities: Automated retraining, drift detection, A/B testing, monitoring dashboards
- 4.Industry-specific models: Pre-trained templates for healthcare, finance, retail (faster POCs)
- âś—Over-simplified "wizard" interfaces (your users are technical)
- âś—Research-grade features with no business application (moves you to Tier 4-5)
- âś—Features that require PhD-level expertise to use
Tier 4-5: Innovation & Research Strategy
"Cutting-edge AI that competitors can't replicate"
- 1.Novel architectures: Research breakthroughs, patent-worthy innovations, conference publications
- 2.Scalability: Distributed training, GPU optimization, multi-region deployment
- 3.Explainability & trust: Model interpretability, bias detection, audit trails for regulated industries
- 4.Vertical depth: Deep domain expertise (medical imaging, financial fraud, etc.)
- âś—Broad horizontal features (you're winning on depth, not breadth)
- âś—Self-service onboarding (your buyers expect white-glove service)
- âś—Commodity features available in open source (you're premium-priced for a reason)
🎯 Feature Prioritization Matrix
Use this decision framework to evaluate every feature request or idea:
| Question | ✅ Build It | ❌ Skip It |
|---|---|---|
| Does it strengthen your tier positioning? | "Makes our Tier 2 custom ML even better" | "Moves us to Tier 3 complexity" |
| Will it win deals you're currently losing? | "3 lost deals specifically requested this" | "One prospect mentioned it once" |
| Can your target customers actually use it? | "Our typical 2-5 person team can deploy" | "Requires skills our customers don't have" |
| Does it justify higher pricing? | "Adds clear measurable value" | "Nice-to-have but won't increase prices" |
| Can you deliver it at quality within tier cost structure? | "Fits our current delivery model" | "Breaks our unit economics" |
The "Shiny Object" Trap
Vendors often chase the latest AI hype (transformers, diffusion models, agents) even when it doesn't fit their tier. Ask: "Does our target customer (Tier X buyer with Level Y data) actually need this, or does it just sound cool?"
⚔️ Competitive Feature Analysis
Don't just copy competitors—understand WHY they built certain features based on their tier positioning.
Tier-Based Competitive Analysis Framework
Are they Tier 0 (automation), Tier 2 (custom ML), or Tier 4 (research-grade)?
Example: "Competitor A is Tier 1 (API-based ML), we're Tier 2 (custom models). Different buyers, different needs."
Which features strengthen their position vs. pull them off-tier?
Competitor Feature: "1-click deployment"
Their Tier: Tier 0-1 (simplicity-focused)
Your Tier: Tier 2 (customization-focused)
Decision: Don't copy. Our buyers WANT technical control, not simplicity.
What's missing in the market for YOUR tier?
Market Gap: Tier 2 buyers struggle with model explainability (all competitors focus on accuracy only)
Your Roadmap: Build interpretability dashboard → differentiation within Tier 2
When to Copy vs. Differentiate
đźš« When to Say "No" to Feature Requests
Saying "no" is harder than saying "yes"—but it's often the right strategic decision. Here's when to decline features:
Request: "Add no-code wizard for Tier 3 deep learning platform"
Why say no: Tier 3 buyers are ML engineers who WANT technical control. Simplification confuses your brand.
Request: "Build enterprise-grade data governance for $10K annual license"
Why say no: Enterprise governance = Tier 3-4 feature. Your Tier 1 pricing model can't support it.
Request: "Build custom integration to our 30-year-old legacy system"
Why say no: One-off integrations are services, not product. Offer professional services instead of roadmap slot.
Request: "Support 47 data formats including XML from 1990s ERP systems"
Why say no: 3 months engineering for 1 customer = bad ROI. Standard formats only, or consulting engagement.
How to Say "No" Without Losing the Customer
Don't just reject—redirect to a solution that works:
↕️ Should You Move Tiers?
Sometimes the right strategic move is to change tiers entirely. Here's how to know:
⬆️ When to Move Up-Tier
"We started with your Tier 1 API solution, but now we need custom models (Tier 2). We're evaluating [competitor]."
Tier 1 maxes out at $50K/year. Customers want more capability but you can't justify higher prices at current tier.
Tier 1 features become table stakes. Competition drives prices down. Only way to maintain margin: move up-tier.
Before: API-based sentiment analysis ($20K/year)
After: Custom NLP models trained on customer data ($100K/year)
Result: 5x revenue per customer, but lost 60% of Tier 1 buyers (worth it if math works)
⬇️ When to Move Down-Tier
Tier 3 deals take 9-12 months. Market moving too fast. Simplify to Tier 2 for 3-6 month cycles.
Tier 3 requires PhD team for every customer. Unit economics broken. Tier 2 = broader team can deliver.
Only 50 companies globally can buy Tier 4 solution. Can't scale. Simplify to Tier 2-3 for 5,000 company TAM.
Before: Custom deep learning models ($500K+, PhD team required)
After: Pre-trained models with transfer learning ($150K, data scientist team)
Result: 10x more addressable customers, faster sales, profitable at scale
Multi-Tier Strategy Warning
Can you serve Tier 1 AND Tier 3? Usually no—your brand, pricing, sales team, and delivery model are optimized for ONE tier.
Exception: Clear product segmentation (different brands, teams, go-to-market). Example: Stripe (Tier 1 API) + Stripe Radar (Tier 2-3 fraud ML).
📢 Roadmap Communication Strategy
How you communicate your roadmap is as important as what's on it. Use tier positioning to guide messaging.
Public Roadmap (External)
- âś“Strategic themes: "Doubling down on Tier 2 custom ML performance"
- âś“Tier-specific benefits: "Faster training for teams with 2-5 data scientists"
- ✓What you WON'T build: "We're NOT adding no-code features—focused on technical users"
- âś—Avoid: Specific dates, competitor comparisons, overpromising
Internal Roadmap (Team)
- âś“Tier strategy: "Moving from Tier 2 to Tier 2.5 (custom + automated feature eng)"
- ✓Customer tier distribution: "80% Tier 2, 15% Tier 1, 5% Tier 3—optimize for majority"
- ✓Competitive gaps: "We're behind [Competitor] on MLOps monitoring—priority Q2"
- âś“Strategic bets: "70% tier-strengthening, 20% innovation, 10% tech debt"
Tier-Based Roadmap Announcements
Frame new features as reinforcing your tier positioning:
❌ Generic: "Introducing automated feature engineering!"
✅ Tier-Specific: "For Tier 2 teams: Automated feature engineering cuts model development time by 40%—no PhD required, just your 2-5 person data science team."
🎯 Your Roadmap Planning Action Plan
Ready to build a tier-aligned product roadmap? Here's your step-by-step guide:
Are you Tier 2 trying to serve Tier 1 buyers? Tier 3 with Tier 2 features? Get honest about where you really are.
What % of revenue comes from each tier? Optimize roadmap for the majority, not edge cases.
Run every backlog item through the decision framework. Kill anything that doesn't strengthen your tier.
Based on signals (sales cycles, pricing ceiling, commoditization), commit to a tier direction for next 12-18 months.
Make tier positioning explicit in roadmap docs, sales decks, and product announcements.
The Bottom Line
Most AI vendors dilute their positioning by chasing every feature request. Winners focus relentlessly on ONE tier—building features that make them the obvious choice for that specific buyer, at that specific maturity level, with that specific team size.
Your roadmap isn't just a feature list—it's your strategic moat.