Render Networks CEO Stephen Rose argues that true network intelligence starts in the trench
Telcos have poured significant time, money, and energy into AI for network operations and customer-facing chatbots, but there’s a phase of the business that remains much more analog — actually building network infrastructure. Render Networks, a construction management platform company led by CEO Stephen Rose, is targeting that gap by embedding AI directly into fiber deployment.
Plenty of broadband construction projects exceed their budgets, driven by rework, miscommunication, and workflows architected for a paper-based world. Modernization efforts have typically meant digitizing what already exists, rather than rethinking the underlying fragmentation.
Render’s goal is to take a slightly different approach. The platform converts GIS designs into field-ready work packages complete with topology, quantities, and standards validation, then deploys computer vision and speech-to-text to capture as-built data in real time as crews work.
We recently had an opportunity to interview Render Networks CEO Stephen Rose on its approach to building out networks using AI, and how that impacts its operations and the end result.
Most of the industry hype around telco AI focuses on network management and customer support chatbots. Why has the physical construction phase been slower to adopt AI, and what is the tipping point we’re seeing now?
Much of the early focus on AI in telecom has centered on domains that sit outside the network build, such as network management and customer support. Construction has lagged because, for a long time, the industry’s response was to digitize existing workflows rather than redefine how the work itself is done. Paper processes became digital artifacts, but the underlying fragmentation remained. Data lives across GIS, CAD, PDFs, spreadsheets, and dailies and is typically captured after the fact rather than as the work progresses.
The more fundamental issue is that much of this technology was never designed with the field in mind. When tools are misaligned with how work actually happens, adoption suffers. And when adoption suffers, data quality degrades. That creates a structural ceiling on how effective AI can be.
What has changed is the way AI is now being applied to close that gap. Rather than operating as an analytical layer on top of incomplete data, AI is increasingly embedded in the execution itself. Multi-modal capabilities such as speech-to-text and computer vision allow information to be captured naturally in the flow of work. At the same time, multi-model systems can reason across designs, quantities, locations, and standards, interrogating how a build is progressing rather than simply recording what happened.
The tipping point is the convergence of usability and accountability. Programs like BEAD have shifted AI from a nice-to-have into an operational necessity. Verified outcomes, trusted as-builts, and field adoption are no longer optional. When AI aligns with how people work and supports participation across the organization, it becomes a force multiplier rather than an additional layer of complexity.
We know that bad data in means bad AI out. How does Render ensure the digital as-built is clean and structured enough to feed other Telco AI systems like Digital Twins or predictive models?
The integrity of any AI system is ultimately determined by the integrity of the data that underpins it. In network construction, that integrity often breaks down very early. Designs originate in GIS, but as work moves into CAD and then into the field, the connection between what was intended and what actually happens on the ground is frequently lost. Updates made during construction rarely flow back upstream, leaving operators with reconstructed as-builts and an enduring gap between design and reality.
Our view is that this is not so much a tooling issue as a structural one. If data is allowed to fragment as work progresses, no amount of downstream intelligence can fully correct it.
Render is designed to preserve continuity. We keep GIS at the center of the process and maintain a single, connected data thread from design through construction and closeout. Render transforms GIS designs into fully scoped, field-ready work with embedded topology, quantities, spatial logic, and standards validation. As construction progresses, as-builts are generated continuously and verified in real time rather than recreated after the fact.
The result is data that is complete, structured, and trustworthy enough to support predictive models, digital twins, and other AI-driven systems without requiring reconciliation later.
The concept of the Digital Twin is big amongst telcos right now. Do you see your platform as foundational to the creation of the Digital Twin? And how does AI verify that the digital twin matches the physical reality in the ground during the build?
From a construction operations perspective, we see Render as a living digital twin today. The key requirement for any digital twin is context, AI needs complete, connected, and trustworthy data in order to reason effectively.
Render provides that foundation by maintaining continuity across design, construction, and as-built. With that context in place, AI can observe how a project is unfolding, compare planned versus actual quantities, locations, sequencing, and standards, and identify where issues are likely to emerge before they become problems.
Over time, this shifts planning from reactive to proactive. The digital twin stays aligned with physical reality because it’s continuously informed by verified field data in near real-time.
Where is AI actually delivering measurable value today in the field — for example in compliance, quality assurance, or workflow automation — versus areas like permits that remain largely manual?
You have to distinguish between computational speed and institutional inertia. AI delivers massive, measurable value today in areas where the builder or operator has ‘sovereign’ control over the data. If you own the data, you can optimize it. If a government entity or a third party owns the process, the AI hits a wall.
1. Real-Time Quality Assurance (The Death of Rework)
The most expensive word in this industry is rework. Historically, Quality Assurance was a post-mortem activity. A supervisor would drive to a site three days after the crew left, find a shallow trench, and order a truck to turn around. That kills margins.
Today, we use Computer Vision to turn every field worker’s smartphone into an automated inspector. The AI analyzes site photos against the original design in real-time. It catches a shallow trench or a loose fitting before the hole is filled. It moves the source of truth from a subjective human guess to an objective digital proof. That alone is adding 3% to 5% back to the bottom line by eliminating the bounce back of crews.
2. The Administrative Offload (Ending the ‘Second Shift’)
The greatest hidden value of AI is the recovery of human time. For decades, a field tech’s day ended at 4:00 PM on the site and began again at 7:00 PM at the kitchen table, typing out daily logs. It’s the Second Shift, and it’s a major driver of burnout and error.
We will see Speech-to-Workflow and NLP today used by crews who can narrate their work as they do it. The AI doesn’t just transcribe the text, it parses the data directly into the construction management system. We will see closeout cycles drop from weeks to hours. When you can close a project on Friday and get paid by Monday because the documentation is perfect, that is a cash-flow game changer.
3. Why Permitting is Still the Manual Bottleneck
You asked why permitting is still stuck. It’s not a technology problem; it’s an institutional interoperability problem.
I can use AI to generate a perfect, constructible permit drawing in ten seconds. But if the municipality’s review process requires a human clerk to manually cross-reference a 1970s zoning map, my AI’s speed is neutralized.
Where we are winning in permitting is Data Readiness. AI can be used to ensure every permit application is 100% complete and compliant with local codes before it’s submitted. We can’t make the clerk work faster, but we can ensure they never have a reason to send the application back for more info. We’re eliminating the RFI loops that typically add 30 to 60 days to a project.
The Bottom Line: AI isn’t digging the holes, but it’s ensuring we only dig them once. We are building a Digital Twin of the network that is 95% to 98% accurate to the original design. That makes the network easier to maintain, more valuable to investors, and far more profitable for the firm that built it.
How does Render help enable the AI readiness of a region, and how much faster can Telcos get to market using an AI-led build versus a traditional one?
AI readiness ultimately depends on execution. You can’t offer AI-enabled services without reliable, accurate infrastructure underneath them.
Render enables that by defining the work up front and maintaining complete, connected data across crews, contractors, and management. That gives agentic AI the context it needs to guide smarter resource allocation, sequencing, and planning decisions with far less manual effort.
In practical terms, applying intelligent automation across the build lifecycle has allowed customers to materially compress timelines. Projects that once took eight years can now be delivered in four. That acceleration directly impacts how quickly regions can bring new services to market and begin realizing value from their network investments.
Looking five years out, do you see the role of the Network Planner evolving into more of an AI Supervisor? How should Telcos be retraining their operations teams today to handle this shift?
Yes, that shift is already beginning and it isn’t just the role of the Network Planner that will change. We will see a whole new class of roles underpinned by AI. The role of the network planner is moving away from producing static designs and toward supervising AI-driven plans, scenarios, and forecasts. In many cases, AI will increasingly handle the first pass of constructible design by processing geospatial data, imagery, and other mapping inputs at a scale and speed humans simply can’t match.
That doesn’t remove people from the process, it changes where their judgment is applied. Planners will spend less time drawing and more time validating outcomes, managing exceptions, and making trade-off decisions. The goal is not perfect automation but competent automation, where the gap between planned and as-built networks is narrow, predictable, and measurable
Over time, as AI learns from real construction outcomes, we expect original designs and final as-builts to converge much more closely, within defined tolerance bands. In practical terms, that means aiming for a maximum deviation of roughly 2 to 4 percent between what was designed and what ultimately gets built.
