AI Product Innovation & Commercial Strategy
Turn AI capabilities into a product differentiator, a pricing advantage, and a revenue driver.
The Problem
The board wants AI in the product, investors are asking about the AI roadmap, and the product team is shipping AI features—but the unit economics do not work and NRR is not moving. Per-seat pricing was designed for a world where value was static and predictable; AI features create dynamic, compounding value that per-seat pricing actively discourages customers from capturing. Meanwhile, the data flywheel that would create a genuine moat needs to be designed from day one, not retrofitted after the fact.
Our Approach
We work at the intersection of product strategy, pricing science, and data architecture to design the AI product portfolio and commercial model that compound together. This means co-innovating the AI product roadmap with your largest customers through a Forward Deployed Engineering model, designing the pricing transition from per-seat to usage-based or outcome-based, and architecting the data network effects that make your product harder to displace over time.
Commercial Model Diagnosis
We analyze your current pricing architecture, customer value data, and NRR cohorts to identify exactly where AI features are creating value that the commercial model is failing to capture.
AI Product Portfolio Design
We co-design the AI product roadmap with your CPO and key customers, prioritizing features that create measurable, recurring value and that anchor the new pricing architecture.
Pricing Transition Architecture
We design the transition from per-seat to hybrid or usage-based pricing, including the packaging, migration path for existing customers, and the sales motion for new business.
Data Flywheel & Board Metrics
We architect the data network effect that compounds AI value over time and deploy the board-ready metrics dashboard that makes the AI revenue story visible and defensible.
What You Get
- —AI product strategy and 12-month innovation roadmap
- —Pricing model transition plan (per-seat → hybrid → usage/outcome-based)
- —Forward Deployed Engineering (FDE) co-innovation program design
- —Data network effect architecture and flywheel design
- —Board-ready AI metrics dashboard with NRR and revenue contribution tracking
- —Competitive moat analysis and differentiation roadmap
Benchmark Targets
| Metric | Baseline | Target | World Class |
|---|---|---|---|
| AI Feature NRR Impact | Neutral or negative NRR impact from AI features | +5–10pp NRR improvement from AI-tier customers | +15pp+ NRR lift sustained over two quarters |
| AI Revenue Contribution | 0% of ARR from AI-specific pricing tiers | 10–20% of ARR from AI pricing tiers within 12 months | 30%+ AI ARR contribution with usage-based expansion |
| Pricing Model | 100% per-seat pricing across all tiers | Hybrid model with at least one AI usage tier | Full usage-based or outcome-based AI pricing in production |
Revolutionizing Pricing Science to Save Client Retention
Pillar C in action: how AI-driven pricing science created a patentable differentiator and saved a client renewal.
Read the full case study →