e& UAE taps Amdocs and NVIDIA to bring GenAI into day-to-day telecom operations
e& UAE is rolling out generative AI across customer care, retail, and network operations with Amdocs' amAIz platform, supported by NVIDIA AI Enterprise, NVIDIA NeMo, and NVIDIA microservices. The goal is straightforward: faster resolutions, smarter recommendations, tighter controls, and measurable efficiency gains.
For operations teams, this is a production deployment with guardrails, role-based access, and full logging and auditing baked in. It's built to hit KPIs without creating new risk.
Why operations leaders should care
- Reduce average handle time and boost first-contact resolution with telco-specific agents and skills.
- Improve store and field efficiency via agent assist, guided workflows, and better triage.
- Support network operations with proactive detection, faster root-cause analysis, and self-serve action suggestions.
- Control cost and accuracy with "best-fit" model routing and telco-tuned prompts.
- Stay compliant: RBAC, audit trails, and privacy safeguards are standard, not add-ons.
What's in the stack
- Amdocs amAIz: A GenAI platform with a library of predefined telco agents and skills.
- Guardrails and governance: Role-based access control, logging, auditing, and privacy controls to reduce operational and compliance risk.
- Model strategy: Matching tasks to best-fit foundation models for accuracy and cost efficiency.
- NVIDIA support: NVIDIA AI Enterprise, NVIDIA NeMo, and NVIDIA microservices for building, customizing, and deploying GenAI at scale.
Deployment approach that works for Ops
- Start with KPIs: AHT, FCR, MTTR, CSAT/NPS, cost per contact, truck rolls, SLA adherence.
- Pilot with guardrails: Use telco-specific agents in a constrained domain. Log everything. Keep a human in the loop.
- Integrate: Connect to CRM, OSS/BSS, knowledge bases, ticketing, and identity for RBAC.
- Train and iterate: Teach supervisors prompt patterns, run playbooks, and tighten decision boundaries based on real outcomes.
- Scale by domain: Move from care to retail to network ops once metrics hold steady.
Metrics to track in the first 90 days
- Average handle time (AHT) and queue time
- First-contact resolution (FCR) and containment rate for virtual agents
- Mean time to resolution (MTTR) in NOC workflows
- Truck rolls avoided and false dispatch reductions
- Agent assist adoption and recommended action acceptance rate
- Customer CSAT/NPS for AI-assisted interactions
- Cost per interaction and model spend per resolved case
- Compliance incidents, PII redactions, and audit completeness
Risks and how this setup addresses them
- Inaccurate answers: Telco-specific guardrails, retrieval patterns, and evaluation checks before actions are taken.
- Data exposure: RBAC, privacy policies, and audit trails limit who sees what and when.
- Model drift and quality swings: Continuous monitoring, test sets, and "best-fit" model routing.
- Cost overruns: Token budgets, per-use guardrails, and routing to lower-cost models where appropriate.
- Vendor lock-in: Microservices approach and model-agnostic orchestration provide flexibility.
- Latency: Caching, skill decomposition, and async background jobs for heavier tasks.
What leaders are saying
"We're deploying telco-specific agents with built-in guardrails to enhance customer care, retail, and network operations, delivering faster resolution times, smarter recommendations, and clear efficiency gains," said Marwan Bin Shakar, Acting CTIO at e& UAE.
Anthony Goonetilleke, Group President of Technology and Head of Strategy at Amdocs, called it an inflection point for production-scale GenAI experience innovation across telecom domains.
Chris Penrose, VP of Telco Business Development at NVIDIA, highlighted how agentic workflows and NVIDIA AI Enterprise help e& UAE deploy and scale AI applications faster, accelerating time to value.
How to prep your team
- Data readiness: Clean knowledge bases, label common intents, and map workflows end to end.
- Access and security: Enforce least privilege, SSO, and PII policies from day one.
- Process capture: Document decision trees for top call drivers and store ops use cases.
- Human oversight: Supervisor approvals for high-risk actions and clear escalation paths.
- FinOps discipline: Track model spend per outcome, not per token.
- Change management: Short training modules for agents, with live shadowing and feedback loops.
Bottom line for Operations
This isn't a lab demo. e& UAE is bringing GenAI into live systems with controls Ops teams expect: RBAC, auditing, privacy safeguards, and model routing tied to cost and accuracy. If you're planning a rollout, start with a single domain, wire in guardrails, measure relentlessly, and expand only when the numbers prove out.
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