Use this framework to identify, quantify, and remediate AI-specific liabilities before they silently erode your investments.
Traditional code scans can’t detect data drift, model degradation, or integration decay. If you’re not measuring these layers, your cost base is incomplete — and your ROI is overstated.
This ebook provides a practical framework for identifying, quantifying, and eliminating AI debt before it scales.
What You’ll Learn
Uncover Hidden Liabilities
Move beyond code reviews. Map risk across six layers of AI debt — from data to models to integrations.
Measure True Exposure
Score each AI component by severity, compounding velocity, and business impact.
Calculate Debt-Adjusted ROI
Translate technical debt into financial terms and unlock up to 29% higher projected ROI.
Sequence Remediation Correctly
Fix root causes first — stabilize data and infrastructure before optimizing downstream models.
Inside the framework, you’ll gain access to the complete ATDAF Self-Assessment Scoring Rubric. This hands-on instrument allows you to immediately evaluate your AI components across the six hidden debt layers.
Here is a preview of the questions you will use to classify your risk:
Data Debt:: Are lineage docs, schema alerts, and drift monitoring in place?
Infrastructure Debt:: Do you have reproducible training environments and CI/CD for model updates?
Governance Debt:: Are model cards complete, audit trails documented, and bias tests recorded?
Ambition Alignment:: Ensure your strategic goals scale at the same pace as your team's capacity.
Don't let your AI initiatives fail to scale due to invisible liabilities, undocumented models, or incomplete cost frameworks.
Devsu helps organizations accelerate product development and scale teams responsibly.
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