A national telecommunications provider deployed Decision Intelligence to make AI-driven churn prevention and network incident decisions transparent, defensible, and operationally actionable.
A national telecommunications provider deployed Decision Intelligence to make AI-driven churn prevention and network incident decisions transparent, defensible, and operationally actionable.
National telecommunications provider serving 15M+ subscribers with complex network infrastructure. Operations and customer analytics teams relied on AI models for churn prediction and incident prioritization.
AI models flagged high-risk customers without actionable reasoning. Retention teams couldn't personalize interventions.
Network operations received prioritized incidents without historical context, slowing root cause analysis.
Regulators required transparent decision logic. Existing models couldn't produce defensible explanations.
Deployed Decision Intelligence as a middle layer between AI models and operational systems. For each prediction, Decision Intelligence extracted causal factors, ranked feature importance, and retrieved historical analogues from similar past events.
Transformed model outputs into human-readable narratives: "Customer flagged due to 3 recent service outages (similar to Case #4521) + contract ending in 45 days." Linked directly to execution context and historical resolution patterns.
Every AI decision logged with full lineage: input features, model version, explanation reasoning, and human override actions. Created regulator-ready audit artifacts without additional manual documentation.
Model inventory, feature mapping, explanation surface design
Decision Intelligence integration with churn model (shadow mode)
Network incident model integration + historical analogue retrieval
Operations dashboard rollout with explanation UI
Regulatory review prep and full production deployment
If your AI systems influence operations or face regulatory scrutiny, let's discuss explainability.