Zain Group Puts AI in Action at Kuwait Workshop, Advancing Smarter Network Operations

Zain Group Brings IT Managers Together to Advance AI in Network Operations

Kuwait, 12 Feb - Zain Group held an innovation workshop under the theme "AI in Action: Smarter Network Operations." Led by chief technology officer Mohammed Al Murshed, IT management from across the company met to review progress, compare notes, and pressure-test next steps.

The agenda was practical: presentations and live demos that showed how AI can reduce incident noise, predict failures before they hit customers, and automate routine tasks across NOC and field operations.

Why this matters for Operations

• Cut mean time to resolve by automating detection, triage, and first-line fixes.
• Forecast traffic and failures to plan capacity and maintenance with fewer surprises.
• Lower energy use by tuning RAN parameters and site policies based on live conditions.
• Reduce ticket backlog with smarter prioritization and automated runbooks.

What was demonstrated

• Anomaly detection across KPIs, logs, and alarms with root-cause suggestions.
• Traffic and quality forecasting to guide proactive actions during peak windows.
• Closed-loop actions for common incidents (e.g., config rollback, node restart, parameter tweaks) with human approval.
• Assistive copilots for NOC teams to query telemetry, summarize incidents, and propose next steps.

A practical playbook for ops leaders

• Start where pain is highest: incident triage, change validation, and field maintenance scheduling.
• Get the data foundation right: unify KPIs, alarms, logs, configs, and ticket data with clear labels and retention.
• Use simple first: rules plus classical ML for 70% of cases; apply deep learning where scale and variance demand it.
• Pick tools that integrate: observability + AIOps + ITSM with open APIs and strong audit trails.
• Keep humans in control: approval gates, rollback plans, and clear accountability for auto-actions.
• Upskill the team: give NOC and field engineers hands-on training in data, automation, and prompt-driven workflows. See AI courses by job role and the AI Automation Certification.
• Plan procurement with exits: avoid lock-in by demanding exportable models, portable data, and contractual access to logs/metrics.

Metrics that matter

• MTTD/MTTR and % incidents auto-resolved.
• False alarm rate and alert volume per engineer.
• SLA breach rate and customer-affecting incidents per site.
• Truck rolls per 1,000 sites and energy consumption per site.
• Model drift incidents and time to retrain.

Risks to manage

• Data quality gaps that lead to noisy or wrong actions.
• Alert fatigue from poorly tuned detection.
• Security and privacy for telemetry and customer-impacting data.
• Change management: getting frontline teams to trust and verify automation.

What to do next

• Pick two high-impact pilots (e.g., alarm correlation and energy optimization) with clear owners and 90-day targets.
• Baseline metrics now, define guardrails, and set weekly checkpoints with NOC leads.
• Build a reusable runbook library and push successful automations into ITSM workflows.
• Benchmark against industry practices such as autonomous networks initiatives from TM Forum: learn more.

The workshop showed momentum and a clear direction: fewer manual steps, faster recovery, and smarter planning. For operations leaders, the path is clear-focus on measurable wins, keep humans in the loop, and turn demos into daily practice.