As enterprises scale AI workloads, the underlying fiber network becomes a critical enabler – not just to move data, but doing so smartly and reliably. The second part of our 20-point guide explores the subtler dimensions of AI readiness: programmable and intelligent infrastructure, sustainable edge integration, governance and openness, and service assurance.
In sum – what to know:
Intelligence – software-defined networking, automation, orchestration, and telemetry give networks the smarts to dynamically manage traffic and resources.
Integration – edge compute brings AI closer to the action and reduces latency; open policies and partnerships support hybrid and flexible AI deployments.
Assurance – security, governance, and transparent SLA metrics ensure data flows are protected, compliant, and predictable, and raises trust in AI.
Note this article is continued from a previous post, discussing points 1-20 in an AI readiness checklist for fiber network providers. That instalment can be found here.
Having addressed the physical footprint, backbone scale, and the access and interconnect provisions in the first 10 items of our 20-point plan for AI readiness in fiber networks, we should take a look at the rest: the raw optics and geographic reach. Here, we pivot into the more subtle (but no less critical) dimensions: what makes a global fiber network smart, sustainable, controllable, and clever. These are captured under three lenses – demarcated below under the headers: programmability & intelligence, sustainability & edge, openness & assurance – describe the difference, ultimately, between fast fiber and an AI-enabled and AI-ready fiber fabric.
Programmability & intelligence, taken together, reflects the shift from static pipes to dynamic, policy-driven infrastructure: automation, orchestration, telemetry, and closed-loop control are central to optimizing AI-driven workloads. Sustainability & edge, as one, brings in the imperative of energy efficiency, on-site compute, latency-sensitive processing, and decarbonised operations. Because the network doesn’t just have to move data, it has to do so affordably, for both corporate pockets and environmental impacts, and to do it as close as possible to where the action is.
Openness & assurance encompasses governance, collaboration, and operational trust. It considers whether data flows are secure and compliant, whether providers facilitate flexible interconnection and ecosystem partnerships, and whether service layers are measurable and reliable. And so the discussion goes from whether enterprise data can even get to where it needs to go to whether the traffic routes can be trusted – as smart and sustainable in their operation and intent.
Programmability & intelligence (for automation, efficiency, control)
11 | Software-defined networking
A software-defined network (SDN) fabric allows a provider’s transport network to be controlled and reconfigured through software rather than hardware changes. AI workloads fluctuate – as model training, inference bursts, or replication cycles create data surges – and require wider bandwidth to be easily scaled and shorter paths to be easily configured between compute clusters. An SDN-enabled fiber network offers such flexibility to adjust routes, prioritize traffic, and manage resources in near real time. Enterprises should evaluate whether a provider’s SDN fabric covers both the backbone and metro networks, and whether it exposes APIs for direct orchestration of their own workloads.
12 | Automation and orchestration
AI workloads – especially large-scale training and inference – are no longer limited by processing power (in GPUs, TPUs, CPUs); they are increasingly governed by how quickly and efficiently data moves between compute nodes (for distributed AI training), data sources (sensors, systems, users), and storage and edge sites. If compute accelerators process terabytes per second, but the network doesn’t go fast enough, then you get transport bottlenecks and idle GPUs – and failed efficiency. So the network has to operate at a level that matches the the compute engine. Otherwise, the whole AI-infrastructure conceit falls over. In fiber (like all) networks, this means automation and orchestration – to enable dynamic provision, configuration, and management of services with minimal manual input. Providers with mature orchestration platforms can offer rapid deployment of wavelength circuits, scalable bandwidth, and integration with enterprise cloud or AI pipelines. So, yeah, assess your providers for which capabilities are truly self-service, API-driven, and supportive of complex, multi-site AI workflows.
13 | Network telemetry and observability
The ability to control network performance using telemetry and analytics matters. AI workloads can be sensitive to latency, jitter, and packet loss, and so the ability to monitor traffic flows, detect congestion, and optimize data transfers is key. Observability, based on the collection and analysis of telemetry data in the fiber network, also supports proactive troubleshooting and predictive maintenance, helping to prevent bottlenecks before they impact AI training or inference disciplines. Enterprises should look for providers offering comprehensive dashboards, API access to metrics, and granular visibility across both metro and long-haul networks.
Sustainability & edge (for energy and compute integration)
14 | Optical innovation and roadmap
The spiralling exchange of data across the edge-cloud continuum puts a strain on network bandwidth. AI demand is growing exponentially – as the market likes to tell anyone who will listen – and so transport networks cannot just stand still. Fiber providers have to upgrade their optical transport layer to keep up. To this end, they are investing in ‘next-gen’ technologies, including higher-capacity wavelengths (400G, 800G, 1T; see above), and advanced modulation schemes and new fibre types. Flexible-grid DWDM (dense wavelength division multiplexing) increases capacity by allocating spectrum more dynamically and efficiently. Open line systems allow faster upgrades through a mix-and-match approach to different vendor equipment. Coherent modulation optics boost efficiency by sending more data per wavelength over longer distances. It is important to review your provider’s roadmap to understand planned tech upgrades and capacity increases. These are particularly important for emerging AI requirements, including ultra-low-latency interconnects and expansion into new regions.
15 | Power availability and efficiency
The reliability and sustainability of power infrastructure underpinning network nodes, data centers, and edge facilities is critical. AI workloads – especially large-scale training and inference – place heavy demands on both compute and network equipment, making consistent, efficient power delivery a prerequisite for uninterrupted performance. Providers with robust, redundant power systems and a focus on energy efficiency can support these workloads while reducing the risk of downtime and minimizing operational costs. Enterprises should evaluate not only the reliability of power at key sites but also the provider’s commitment to sustainable operations – which includes the use of renewable energy, intelligent cooling, and energy-efficient optics, and influences both financial and environmental outcomes.
16 | Edge compute integration
As a check-point for enterprises, edge compute integration measures how closely a provider’s network connects to compute resources near the source of data – from edge nodes and micro data centers to IoT clusters. Low-latency processing is critical for real-time AI workloads, such as autonomous vehicles or industrial analytics. Providers that offer on-network edge compute, hybrid deployment support, and APIs for orchestrating workloads between central and edge sites make it possible to run AI closer to where data is generated. Again, and as always, enterprises should review whether their provider enables edge colocation or partner access, ensuring latency-sensitive AI applications perform efficiently across the fiber-edge ecosystem.
Openness and assurance (for collaboration, compliance, and trust)
17 | Security and data governance
Security and data governance cover the measures a provider takes to protect data in transit and at rest, and to comply with relevant regulations. AI workloads often involve sensitive information or proprietary models, making confidentiality and compliance essential. Providers offering encryption options, role-based access controls, secure multi-tenancy, and certifications such as ISO, SOC, or GDPR can ensure data flows remain protected and auditable. So check with your provider – about whether they support Layer 1/2 encryption, adhere to regional data laws, and align with internal governance frameworks for AI and ML data. Strong security and governance practices reduce regulatory exposure while enabling trust and accountability across AI operations.
18 | Open access policies
An open access policy defines how freely a provider allows multiple operators, enterprises, or partners to use its network. Openness enables flexible interconnection, multi-cloud routing, and third-party connectivity without locking enterprises into a single ecosystem. Providers that support open-access fiber, neutral colocation, and on-demand interconnection give enterprises the flexibility to build diverse AI deployments efficiently. Enterprises should review the provider’s transparency policies, including contractual terms, pricing, and provisioning processes. Open-access networks accelerate innovation, simplify integration, and lower costs across multiple ecosystem partners.
19 | Ecosystem and partner openness
Related, ecosystem and partner openness measures how well a provider integrates with third-party services, cloud platforms, AI toolchains, and technology partners. Many/most AI workloads require some collaboration across clouds and networks, making seamless interconnects critical. Providers that cultivate ecosystem partnerships and offer open APIs can simplify multi-vendor operations; orchestration tools also help reduce friction. Enterprises should assess whether the provider facilitates connectivity to hyperscalers, internet exchanges, and edge platforms – and makes this available via flexible SaaS models – to ensure workflows run efficiently across the broader ecosystem.
20 | Service assurance and transparency
Service level assurance (SLA) and transparency, geared around clear metrics, will define how reliably a provider delivers predictable performance and how clearly performance is measured and communicated. AI workloads are sensitive, so visibility into uptime and throughput is essential. Guarantees around response times, in case (when) deliveries fail, should also be on the checklist. So enterprises should review contractual SLA terms, historical performance data, monitoring dashboards, and sundry escalation procedures. Providers with API-accessible SLA telemetry – the more granular, the better – allow AI orchestration systems to respond more dynamically to network conditions, and should be able to guarantee that mission-critical workloads run predictably and efficiently.
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And so we reach the end; by way of a conclusion, it should be considered that ‘AI readiness’ cannot be gleaned or summarised in a single benchmark. In fibre networks. it is not just about scale or speed, but also about intelligence, flexibility, sustainability, and transparency. It is discernible in the total combination of a provider’s physical scale, optical sophistication, and digital programmability. So, for enterprises, there are lots of questions to ask. But even those depend on the enterprise’s own AI workloads, and the startpoint, almost before brandishing such a checklist with your local network provider, is to do due diligence on your own AI lifecycle – from data ingestion and training to inference and distribution. And then ask the questions.