Create advanced Works with: Any AI

Technical Debt Prioritisation

Table output Has example

Make the case for maintenance work.

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Prioritise technical debt. List of debt items with descriptions: [DEBT_ITEMS]. Effects observed (incidents, slow delivery, bugs): [EFFECTS]. Engineering capacity: [CAPACITY].

Create a table scoring each item on impact on speed, reliability, security, and cost, with evidence, and the effort to fix. Classify into fix now, schedule, monitor, accept. Calculate the cost of delay in terms the business understands using my data and labelled assumptions. Draft a business case for the top item, a proposal for allocating a share of capacity with rules, and a communication to stakeholders. Include metrics to show improvement.

Add a section that translates each top item into a plain-language story about customer or business impact, such as slower delivery of new features by a stated amount, and a one-slide version for leadership. Provide a rule for tracking the share of capacity spent on debt over time, and a review of whether the earlier fixes produced the expected improvement.

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Item | Impact | Evidence | Effort | Class
Flaky checkout tests | Speed, reliability | 12 hours a week lost in reruns | 3 days | Fix now
Old payment library | Security | Vulnerability notice | 8 days | Schedule
Duplicate pricing code | Bugs | 3 incidents | 5 days | Schedule
Legacy admin page | Low | Rarely used | 15 days | Accept
Cost of delay (assumption): 12 hours x 4 weeks x 60 = 2,880 per month in engineer time.
Proposal: 20% capacity for debt this quarter.
Metrics: rerun hours per week; incidents from duplicated code.

• roadmap