AI Labs in Action
AI App Analyst (Enterprise)
Ask questions like: - “Who was impacted by Teams QoE degradation yesterday at HQ?” - “Which floors changed the most after the policy update?” - “What happened during the event surge between 2–4pm?”
Churn Propensity & Save Plays (CSP)
- Predict churn risk using QoE + behavior signals
- Produce save lists and recommended actions by cohort and location
Network Cost Intelligence (CSP)
- Connect application load to cost drivers (capacity, energy, transit)
- Recommend targeted actions that reduce spend without harming experience
Fraud & Abuse Detection (CSP + Interconnect)
- Detect patterns, quantify impact, recommend enforcement
- Produce proof packs for partner disputes
Event Surge Command Center (Venues)
- Detect surge drivers by space/app/cohort
- Recommend peak-hour playbooks to protect critical apps
How it works (high-level)
- Signal foundation: AppLogic application intelligence + QoE scoring + behavior context
- AI-ready fabric: Insights Data Storage (IDS) and Insights workflows
- Models + agents: Purpose-built models and agents tuned for network operations and business outcomes
- Proof loop: Before/after reporting so teams can trust results and institutionalize repeatability
Trust, safety, and responsible AI
AI Labs is designed for real operations, not demos: - Human-in-the-loop by default: recommendations are explainable and reviewable - Guardrails: role-based access, audit trails, and policy constraints - Outcome-first: models are measured against operational and business KPIs
Engagement model
1) AI Labs Workshop (2–4 weeks)
Define the outcome, data readiness, and success metrics.
2) Pilot (4–8 weeks)
Prove value on one domain (e.g., churn, event surges, fraud, capacity).
3) Production (ongoing)
Operationalize workflows, dashboards, and agents—then scale across segments.
