Frameworks Notes from the Jagged Frontier
Product Management Operating Model Enterprise AI

Product Health Diagnostic: Framework at a Glance

Framework overview · Full diagnostic →

The product system has five failure points: what you decide to build, what you fund, how you build it, whether it lands, and whether you learn from what happens next. A weakness in any one compounds the others.

The diagnostic at a glance
Product Health Diagnostic Flowchart Triage questions at top, five diagnostic columns, lever routing at bottom TRIAGE Answer each question. Any yes points to a section. Multiple failures: start with Strategy and Discovery. STRATEGY Features rarely used? Priorities unexplained? PORTFOLIO Resources spread too thin? No clear prioritization? BUILD Delivery slowing down? Same issues repeating? VALIDATION Solving the right problem in the real world? INTELLIGENCE Know who uses product and if they get value? DIAGNOSE Customer insight Problem definition Outcome linkage Prioritization discipline Strategic alignment DIAGNOSE Prioritization clarity Portfolio balance Build vs buy vs partner Financial visibility Kill discipline DIAGNOSE Capacity allocation Tech debt visibility Delivery predictability Definition of done Execution patterns DIAGNOSE Pain point resolution Value messaging Functional completeness Non-functional barriers Time to value DIAGNOSE Customer feedback Expansion signals Decision quality New opportunities Pricing intelligence CHOOSE THE RIGHT LEVER Strategy | People and incentives | Process | Tools | AI and automation process / people automation Process, people, or strategy fix Redefine discovery. Align incentives. Kill zombie products. Fix the process before building anything. AI app candidate Apply the Go/No-Go filter first: business value + data gate + org readiness For root cause questions and EBITDA impact See the full Product Health Diagnostic → Process first. An AI app built on a broken workflow produces broken outputs faster. If the same symptom appears across multiple sections, check incentives first.
Revenue impact by section
Strategy and Discovery
Better discovery raises feature adoption on the same engineering spend. Fewer wasted builds. Faster time to value for new customers.
Portfolio and Investment
Engineering capacity redirected from zombie products to high-value bets. Build vs buy decisions that do not create long-term liability.
Build, Velocity and Tech Debt
More shipped value per engineer. Recurring build failures erode customer trust and trigger SLA penalties. Tech debt addressed before it becomes the ceiling for scale.
Validation and Adoption
Higher production conversion. Faster time to value reduces churn risk in the first 90 days. Removing non-functional barriers unlocks expansion in stalled accounts.
Product Intelligence
Roadmap decisions grounded in real usage patterns. Expansion signals surface before CS misses the window. Pricing intelligence improves gross margin without losing customers.