Bad AI integration pattern
Adding AI because competitors mention AI is the fastest way to increase cost and uncertainty with little user impact.
The common pattern is surface-level generation features with no measurement model and no operational ownership.
Where AI usually works
The key criterion is measurable workflow improvement, not novelty.
- Classification tasks with repetitive structure
- Summarization inside high-volume workflows
- Recommendation systems with clear objective signals
- Draft generation where human review already exists
Evaluation before scale
Every AI feature needs baseline metrics: quality threshold, latency target, cost-per-call ceiling, and fallback behavior.
Without these thresholds, teams cannot decide if the feature should be expanded, revised, or removed.
Compliance and risk
In Europe, deployment decisions are inseparable from data minimization, consent, and traceability obligations.
AI integration must be designed with compliance architecture, not patched later.
Practical rule
If you cannot clearly define who benefits, how performance is measured, and what happens on failure, the feature is not ready.
AI should improve operations, not create ambiguity.
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