An AI-native roadmap commits more than architecture capacity.
It commits the organization to evaluate uncertain behavior, coordinate changes across component boundaries, handle exceptions, preserve a fallback, and decide when the dependency is no longer worth its cost.
The maintenance side is documented. Hidden Technical Debt in Machine Learning Systems describes risks such as entanglement, boundary erosion, hidden feedback loops, undeclared consumers, and data dependencies. NIST's AI Risk Management Framework and Playbook call for clear roles, viable non-AI alternatives, safe decommissioning, bypass procedures, backups, deactivation thresholds, and review of upstream and downstream effects.
Those sources do not prescribe one product architecture. They show obligations that a design review should price.
For one planned AI capability, answer five questions.
1. What customer outcome disappears when the model is switched off?
Name the outcome, not the feature. This reveals how much customer value depends on model behavior.
2. Which parts of the system were redesigned because that behavior is uncertain?
Review workflow, evaluation, permissions, exceptions, support, pricing, and decision rights. An interface change alone does not address the operating model.
3. How does use create reliable learning?
Identify the observed outcome, how it is evaluated, which future decision the evidence can change, and who has authority to make that change.
4. Who owns the complete outcome when the model is wrong?
Component teams may own the service, data, model, interface, policy, and manual exception. One accountable owner still needs authority to choose the treatment across those boundaries.
5. What is the credible fallback or exit path?
Define what the customer can still do, when a person intervenes, which guarantees remain, and what must change if the dependency becomes unacceptable.
The answers do not create a maturity score. They reveal whether the value justifies the maintenance, evaluation, ownership, and recovery commitment.
Use the complete review on one planned capability: https://galstyan.am/five-questions-before-calling-a-system-ai-native?utm_source=newsletter&utm_medium=email&utm_campaign=sts_2026_season_1&utm_content=nli_004_beehiiv_v1
Reply with the question your current design cannot answer yet.