Technical Debt
The governance-grade indicator of execution capacity. The technical mirror to Human Debt.
AI programs fail. Modernization stalls. Delivery slows. Not because teams don't work hard — but because execution capacity is constrained by accumulated technical debt.
AI does not create Tech Debt. It exposes it.
Organisations first feel Tech Debt as delayed AI ROI because AI amplifies execution friction. But the same debt quietly erodes the ROI of every technology investment — platforms, cloud, security, and transformation initiatives alike.
Technical Debt becomes measurable, explainable, and governable.
Technical Debt is not only measurable. It is dismantlable.
Technical Debt is treated here as a governance-grade execution capacity signal within institutional environments.
Framework origin: duenablomstrom.com
The Core Problem
Most organizations treat tech debt as a backlog of refactors, a developer complaint, or a vague "we'll fix it later." That framing fails leadership.
- How much is it costing us?
- Where is it blocking outcomes?
- What risk does it create?
- What do we do first?
- Inflates cost per change
- Increases incident probability
- Slows lead time and recovery
- Erodes reliability and security posture
- Makes AI adoption brittle and expensive
If you can't measure friction, you can't govern it.
The "LLM Era" Failure Mode
The default leadership plan is: "Add AI to speed everything up." But AI amplifies what's underneath.
Messy systems
Messy automations
Unclear ownership
Unsafe deployment
Brittle pipelines
Unreliable agents
Weak controls
Governance blowback
AI doesn't remove debt. It exposes it.
The Missing Layer: A Signal
Leaders already have signals for Revenue, Cash, Customer health, Security posture. But most do not have a credible signal for execution capacity.
Technical Debt assessment answers:
"Can we execute change safely, predictably, and at speed?"
What Tech Debt Assessment Is
A governance-grade measurement system. Not "another dashboard" — a decision system.
Detects execution friction across systems
Converts it into a decision-ready signal
Recommends priorities by ROI + risk
Tracks improvement over time
Produces board/audit-ready reporting
Three Key Outputs
- Comparable over time
- Comparable across domains/products
- Decomposable into drivers
- Defensible in leadership forums
"Our execution capacity is improving (or deteriorating), and here's why."
- Systems that tax delivery disproportionately
- Hotspots that create repeat incidents
- Services with highest risk concentration
- Structural coupling that breaks roadmaps
"Where is the debt that actually matters?"
- Smallest set of moves that unlock disproportionate value
- Sequenced interventions with measurable effect
- Cost + risk + time-to-impact estimates
"What do we do first, second, third — and what will it change?"
What We Measure
- Lead time drivers
- Coupling & complexity
- Build/release friction
- Test brittleness / coverage reality
- Incident recurrence
- Recovery time (MTTR drivers)
- Error budgets & reliability debt
- Security debt hotspots
- Access/sprawl risks
- Compliance control gaps
- Supply chain exposure
- Toil per release
- Manual interventions
- Runbook dependency
- Platform bottlenecks
Inputs (What We Analyze)
We work with what you already have. No "rip and replace." No months of instrumentation.
AI-Assisted Technical Debt Dismantling
PeopleNotTech deploys a structured AI-assisted reverse-engineering methodology.
- Generates controlled AI inputs
- Observes system outputs regardless of pass/fail
- Forms multi-variable behavioural fingerprints
- Produces derived machine-readable specifications
- Creates Technical Debt Reduction Artefacts
- Installs drift detection mechanisms
Delivered by specialised technical teams trained in the PeopleNotTech architecture.
This replaces undocumented behaviour with inspectable specification.