Preparing your experience
A practical framework for product leaders to assess AI readiness, define gating criteria, and navigate the journey from prototype to production platform at scale.
After shipping AI products at Google, Meta, and in robotics, I've seen the same pattern repeat: teams rush from demo to production without understanding the maturity stages in between. This leads to failed launches, burned budgets, and organizational trauma. The AI Product Maturity Model provides a roadmap for product leaders to navigate this journey systematically.
Traditional software follows a predictable path: prototype → MVP → scale → optimize. AI products don't work this way. They're probabilistic, not deterministic. They require continuous training data. They have emergent behaviors that only appear at scale. And they fail in ways that traditional software doesn't—hallucinations, bias, drift, and adversarial attacks.
Product leaders trained on traditional software often make critical mistakes:
The AI Product Maturity Model addresses these gaps by defining four distinct stages, each with clear gating criteria, investment requirements, and organizational implications.
Figure 1: The AI Product Maturity Model with four stages, gating criteria, team sizes, and investment ranges. Each stage builds on the previous with clear thresholds that must be met before advancing. Skipping stages leads to failed launches and technical debt.
Goal: Validate that AI can solve the problem at all.
Characteristics:
Key Activities:
Gating Criteria to Move to Stage 2: 1. Proof of Concept: Model achieves >70% accuracy on golden set 2. Cost Feasibility: Unit economics work at target scale (cost per inference × expected volume < revenue per user) 3. Latency Feasibility: P95 latency < 5 seconds (or acceptable for use case) 4. Stakeholder Buy-In: Leadership commits to Stage 2 investment
Investment Required: $50K-$200K (mostly engineering time)
Common Failure Modes:
Real-World Example: At Meta, we spent 6 weeks prototyping Instagram Calling quality prediction. We used a simple logistic regression model on network metrics to predict call quality. Accuracy was 75% on 50 test calls. Cost: $0.0001 per prediction. Latency: 20ms. That was enough to move to Stage 2.
Goal: Ship AI as a feature to a subset of users and validate product-market fit.
Characteristics:
Key Activities:
Gating Criteria to Move to Stage 3: 1. Quality: Model achieves >85% accuracy on eval set, <5% false positive rate 2. Engagement: Users engage with AI feature at >2x baseline (e.g., 2x more calls made) 3. Reliability: P95 latency < 2 seconds, 99.5% uptime 4. Safety: Harmful content rate < 0.2%, false positive rate < 2% 5. Unit Economics: Cost per user < 10% of revenue per user
Investment Required: $500K-$2M (team salaries, infrastructure, model training)
Common Failure Modes:
Real-World Example: Instagram Calling launched as a feature in 2018. We rolled out to 1% of users (10M people) in week 1, monitored quality metrics (call completion rate, latency, user reports), fixed edge cases, then ramped to 100% over 12 weeks. By month 6, we hit 75% DAU adoption with 99.9% uptime.
Goal: Scale AI feature to all users and optimize for cost, quality, and reliability.
Characteristics:
Key Activities:
Gating Criteria to Move to Stage 4: 1. Scale: Serving >1M requests per day with <$0.01 cost per request 2. Quality: Model accuracy >90%, false positive rate <1% 3. Reliability: 99.9% uptime, P95 latency <1 second 4. Business Impact: Measurable lift in revenue, retention, or NPS 5. Operational Maturity: Automated retraining, deployment, and rollback
Investment Required: $2M-$10M (team, infrastructure, model training, operations)
Common Failure Modes:
Real-World Example: At Covariant (robotics), we productized vision AI for warehouse picking. We optimized inference from 400ms to 120ms through model distillation. We built automated retraining pipelines that improved accuracy from 72% to 85% over 18 months. We deployed to 50+ warehouses serving 100K picks per day. Cost per pick: $0.002. Uptime: 99.8%.
Goal: Turn AI capability into a platform that other teams can build on.
Characteristics:
Key Activities:
Gating Criteria for Platform Success: 1. Adoption: >10 internal teams or >100 external developers using platform 2. Reliability: 99.95% uptime, SLA-backed guarantees 3. Governance: Full audit trail, access controls, compliance certifications 4. Economics: Platform generates >$10M annual revenue or saves >$50M in costs 5. Ecosystem: Active developer community with >50 integrations
Investment Required: $10M-$50M+ (platform team, infrastructure, partnerships, compliance)
Common Failure Modes:
Real-World Example: Google's Vertex AI started as internal ML infrastructure, then became a platform serving thousands of internal teams and external customers. It provides APIs for training, deployment, monitoring, and governance. It generates billions in revenue and powers Google's AI products.
1. Assess Current Stage: Where is your AI product today? Be honest about maturity gaps. 2. Define Gating Criteria: What metrics must you hit to move to the next stage? 3. Plan Investments: What team, infrastructure, and time are required? 4. Communicate Expectations: Align stakeholders on timeline and milestones.
1. Right-Size Infrastructure: Don't build Stage 4 infrastructure for Stage 1 experiments. 2. Invest in Evaluation: You can't improve what you don't measure. 3. Plan for Operations: AI systems require continuous monitoring and maintenance. 4. Build for Iteration: Assume you'll need to retrain and redeploy frequently.
1. Set Realistic Timelines: Stage 1 → Stage 3 takes 12-18 months minimum. 2. Fund Appropriately: Each stage requires 2-5x more investment than the previous. 3. Accept Failure: 50% of Stage 1 experiments should fail (if not, you're not taking enough risk). 4. Measure Business Impact: Don't advance stages without proving value.
Figure 2: Common anti-patterns in AI product development and their consequences. Skipping stages leads to failed launches, premature optimization wastes engineering cycles, ignoring evaluation prevents iteration, underestimating operations causes incidents, and premature platformization creates unused infrastructure. The right approach: respect the stages, build evaluation infrastructure early, and invest in operational excellence.
Symptom: Trying to go from prototype to production in one leap. Consequence: Failed launches, technical debt, organizational trauma. Fix: Respect the maturity stages. Each stage builds on the previous.
Symptom: Optimizing for cost/latency before proving product-market fit. Consequence: Wasted engineering effort, delayed learning. Fix: Optimize only after Stage 2 gating criteria are met.
Symptom: No automated evaluation pipeline, relying on manual testing. Consequence: Can't iterate, can't measure improvement, can't catch regressions. Fix: Build evaluation infrastructure in Stage 2, before scaling.
Symptom: No monitoring, no alerting, no runbooks, no incident response. Consequence: Outages, quality degradation, user trust erosion. Fix: Invest in operational excellence starting in Stage 2.
Symptom: Building a platform before proving a single use case. Consequence: Complex, unused infrastructure that slows down iteration. Fix: Only build a platform after 3+ successful Stage 3 products.
Figure 3: Investment growth curve across the four maturity stages. Each stage requires 2-5x more investment than the previous, with team sizes growing from 2-3 people in Stage 1 to 15-25 people in Stage 4. Investment ranges from $50K-$200K in experimentation to $10M-$50M+ for platform development.
The AI Product Maturity Model provides a roadmap for product leaders to navigate the journey from experiment to platform. The key insights:
1. Respect the stages. Each stage has distinct goals, investments, and risks. Skipping stages leads to failure. 2. Define gating criteria. Don't advance without proving value and meeting quality bars. 3. Invest appropriately. Each stage requires 2-5x more investment than the previous. 4. Build for iteration. AI products require continuous improvement, not one-time launches. 5. Measure business impact. Technical metrics matter, but business outcomes determine success.
The teams that master this framework will ship AI products that actually work in production, at scale, with sustainable unit economics. The teams that ignore it will burn budgets and lose organizational trust.
Where is your AI product today? What gating criteria do you need to hit to advance to the next stage? Use this framework to align your team, set realistic expectations, and navigate the journey from experiment to platform.
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