Tom Ayling, Chief Executive Officer, Gisual
"AI becomes operational when it understands the world outside the network. For network automation and autonomous networks, that means incorporating real-time intelligence from partners like Gisual who specialize in physical infrastructure such as the power grid. Integrating that insight into automated workflows is how organizations move from reacting to outages to engineering resilience," said Erian Laperi, Chief Technology Officer for Communications, Media & Entertainment and Technology (CMT) at Cognizant.
Every day, the same operational failure repeats itself hundreds of times across CSP operations centers worldwide.
A network alarm arrives. Automated systems spring into action, correlating data, running diagnostics, preparing response protocols. Then everything stops. An analyst must manually check whether there's a power outage at the affected site.
Minutes tick by. Should we dispatch? Should we wait? Is this a network fault requiring immediate repair, or a power event we can't control?
This manual intervention, this single point of dependency on external information, has become the critical bottleneck undermining billions in network automation investment. And it's getting worse.
The Numbers Don't Lie
22% of all network outage tickets are caused by power disruptions, not network failures. During severe weather? That jumps past 40%.
These aren't just statistics. The scale and scope are operational bottlenecks and workflow breakers by needing to get 3rd party data:
- Manual power verification delays network fault resolution by 18-24 minutes per incident. Analysts stop automated workflows to check utility websites. Meanwhile, real network problems sit unaddressed. Multiply this across thousands of daily incidents and the service impact compounds fast.
- Wasted triage on confirmed power outages. Teams spend 18-24 minutes per ticket running diagnostics when no network intervention is even possible.
- Unnecessary dispatches when power info is unclear. Without reliable data, ops teams play it safe and send someone. Whether it's €300-450 in Europe, $250-400 in North America, or comparable costs across Asia and Latin America, this "better safe than sorry" approach costs millions annually. And the technician shows up to find equipment just waiting for the grid to come back.
Here's the irony: CSPs built sophisticated AI-driven automation that can detect, diagnose, and self-heal faults without humans. But the moment power might be involved? We're back to checking utility websites that update every 30-60 minutes, calling local contacts, making educated guesses.
We automated everything except the one dependency that actually matters.
According to Rick Lievano, CTO for Microsoft Worldwide Telecommunications, “Advancing AI for network operations requires combining cloud-scale intelligence with specialized partners that deliver real-time insights. This allows automation to separate signal from noise and respond with confidence rather than delay. For proactively managing grid outages, Gisual has done this better than anyone in the industry.”
Learn more how Gisual can provide network automation during these times of grid instability here.
Why This Goes Beyond Efficiency
Grid reliability faces mounting challenges globally operators have massive growth targets, while simultaneously having to invest in strengthening the resiliency of the current legacy grid. Weather-related power outages in North America doubled over the past decade, with the U.S. seeing 20% annual increases since 2019. European networks wrestle with renewable integration and interconnection limits (55% of the continent's power system has limited import options during disruptions). Latin American operators deal with crumbling infrastructure (investment fell 40% between 2015-2021), resulting in outages lasting 16 times longer than in the EU. Asian markets face explosive electrification demand, with consumption growing 5-7% annually, far exceeding global averages.
Each region's grid challenges translate directly into CSP operational burden.
But frequency is only part of the problem. Information asymmetry is the issue.
Utilities and telecom providers operate on different timelines. A utility updates its outage map every 30-60 minutes (an eternity when your network response is measured in seconds). Some utilities provide detailed info, others just vague affected areas. Cross-border ops face entirely different data standards.
This forces CSP operations into constant uncertainty. Is there no reported outage because power is fine, or because the utility hasn't updated their system? Did power just return, or is the information delayed? Wait for clarity or dispatch immediately?
The result: Inconsistent decisions. Fragmented automation. Delayed customer communication. Undermined business intelligence.
The Solution: Automate the Manual Step
The answer isn't better manual processes. It's embedding real-time power outage intelligence directly into network operations systems, treating power status not as external information to verify, but as a real-time data layer enriching automated workflows from the moment incidents are created.
Automated enrichment integrates authoritative power outage intelligence into your network alarm stream in real-time. Incidents get automatically tagged with power context: power status (on or off), estimated restoration time, affected equipment and customers. Happens in seconds. No human needed.
Intelligent classification uses power-enriched data to distinguish network faults from power events at the point of incident creation. Machine learning analyzes network telemetry plus power status to determine root cause with high confidence, eliminating manual verification in most cases.
Dynamic routing automatically directs tickets based on classification. Power-related incidents bypass network dispatch entirely. Actual network faults get immediate attention from teams who aren't stuck in power verification bottlenecks.
Solutions like Gisual's power outage intelligence platform deliver this globally, giving CSPs a single reliable source of truth that plugs directly into existing NMS, OSS/BSS, and Ticketing systems.
The transformation is immediate: That 18-24 minute verification delay? Gone. Wasted triage on power outages? Automated. Unnecessary dispatches? Prevented.
The Business Case Is Clear
Eliminating power verification delays reduces mean time to repair for actual network faults by 18-24 minutes per incident. For a carrier handling 50,000 outage tickets (11,000 power-related), that's over 6,600 hours of accelerated resolution annually.
Preventing unnecessary dispatches generates millions in annual savings. Field teams only deploy when network intervention is actually possible.
Recovering analyst capacity previously burned on manual verification. Ops teams handle 25-35% more incidents with existing staff, or reallocate expertise to higher-value work.
Improved automation ROI. CSPs invested heavily in AI-driven operations. Automated power intelligence lets these systems operate at designed efficiency instead of getting bottlenecked by manual processes.
One European Head of Network Operations put it simply: "We can't control grid stability. But we can control how we respond to it. Embedding power intelligence means our automation works regardless of external conditions. That's not just efficiency, it's resilience."
Act Before the Next Incident Exposes the Gap
Grid reliability is declining. Your automation investment is getting undermined by a manual bottleneck that adds zero value. Every power verification delay costs response time. Every uncertain dispatch wastes resources. Every interrupted automation workflow represents capability you paid for but aren't using.
The technology exists. Real-time power outage intelligence integrates into network operations systems today. Leading CSPs are already implementing solutions like Gisual to close this gap, recovering millions in operational savings while building the resilience required for increasing grid instability.
The business case is clear. The operational impact is immediate.
You invested in autonomous networks, AI-driven diagnostics, orchestrated workflows. Don't let manual power verification be the dependency that undermines it all.
The grid may be becoming less reliable. Your network operations don't have to be held hostage by information gaps you can close right now.