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Blue Planet Introduces Proactive Network Operations

Blue Planet, a division of Ciena, introduced Proactive Network Operations (PNO), which enables AI-assisted operations to significantly improve the trouble-to-resolve process, forging a path toward a more adaptive network.

The new solution harnesses the power of automation and artificial intelligence (AI) to help providers not only predict but to also proactively avoid outages.

Using advanced machine learning algorithms, Blue Planet’s PNO can predict potential service disruptions with exceptional accuracy, foreseeing up to 95 percent of unplanned network outages based on analysis of Ethernet and optical loss of signal (LOS) anomalies. PNO can quickly pinpoint the root cause and then prescribe the best actions to preemptively resolve the issue.

Blue Planet has also created an OpEx savings calculator to estimate the potential savings by deploying PNO. According to Blue Planet’s estimates, providers on average can save up to 38 percent in trouble-to-resolve OpEx per year.

PNO can be integrated into existing provider environments regardless of which vendor systems and networking equipment are in place. Additionally, Blue Planet’s service professionals can help fine-tune and tailor PNO based on specific business objectives and network environment.

Kailem Anderson, VP of Portfolio and Engineering, Blue Planet, said: “More providers are realizing that AI is key to facilitating a more automated way of running their business. Blue Planet’s goal is to enable customers to apply intelligent, closed-loop automation to all aspects of their operations with the goal of realizing Ciena’s vision of a fully adaptive network.”
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Blue Planet Introduces Proactive Network Operations

Blue Planet, a division of Ciena, introduced Proactive Network Operations (PNO), which enables AI-assisted operations to significantly improve the trouble-to-resolve process, forging a path toward a more adaptive network.

The new solution harnesses the power of automation and artificial intelligence (AI) to help providers not only predict but to also proactively avoid outages.

Using advanced machine learning algorithms, Blue Planet’s PNO can predict potential service disruptions with exceptional accuracy, foreseeing up to 95 percent of unplanned network outages based on analysis of Ethernet and optical loss of signal (LOS) anomalies. PNO can quickly pinpoint the root cause and then prescribe the best actions to preemptively resolve the issue.

Blue Planet has also created an OpEx savings calculator to estimate the potential savings by deploying PNO. According to Blue Planet’s estimates, providers on average can save up to 38 percent in trouble-to-resolve OpEx per year.

PNO can be integrated into existing provider environments regardless of which vendor systems and networking equipment are in place. Additionally, Blue Planet’s service professionals can help fine-tune and tailor PNO based on specific business objectives and network environment.

Kailem Anderson, VP of Portfolio and Engineering, Blue Planet, said: “More providers are realizing that AI is key to facilitating a more automated way of running their business. Blue Planet’s goal is to enable customers to apply intelligent, closed-loop automation to all aspects of their operations with the goal of realizing Ciena’s vision of a fully adaptive network.”
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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...