VP of Product
Technical Debt & Platform Health
What You Do Today
Balance feature development with platform health — managing technical debt, performance, reliability, and scalability. Engineering wants to refactor everything; the business wants new features. You mediate.
AI That Applies
AI-powered technical health scoring that quantifies technical debt impact on development velocity, identifies the highest-ROI refactoring opportunities, and models long-term platform risk.
Technologies
How It Works
For technical debt & platform health, the system identifies the highest-roi refactoring opportunities. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The trade-off decision.
What Changes
Technical debt becomes quantifiable. The AI shows that this legacy component causes 30% of production incidents and slows every feature by 2 weeks — making the business case for investment.
What Stays
The trade-off decision. How much to invest in platform versus features is a business judgment that requires understanding both the technical reality and the market opportunity.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for technical debt & platform health, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long technical debt & platform health takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your board chair or lead independent director
“What data do we already have that could improve how we handle technical debt & platform health?”
They shape expectations for how AI appears in governance
your CTO or CIO
“Who on our team has the deepest experience with technical debt & platform health, and what tools are they already using?”
They own the technology infrastructure that enables AI adoption
a peer executive at a company further along on AI adoption
“If we brought in AI tools for technical debt & platform health, what would we measure before and after to know it actually helped?”
Their lessons learned are worth more than any consultant's framework
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.