Anyone who has worked in the broadcast industry long enough has at least one story of taking something off the air—and when it happens, the pressure immediately kicks in to restore service as quickly as possible. In practice, that may involve hardware swaps, signal redirection and even repatching inputs and outputs.
Those time-sensitive situations don’t often allow for someone standing by to capture all the changes being made in the moment. And even when it is not an on-air emergency, sometimes changes need to be made rapidly, established processes fall by the wayside and documentation suffers from neglect.
Maintaining good documentation is central to any mission-critical system and this doesn’t change in the world of AI. Decades ago, systems design, maintenance and troubleshooting primarily involved working with purpose-built hardware and centered around signal flow. Systems were often planned on a chalkboard and designed on a drafting table.
Now, the scale and complexity of the systems have grown significantly. The introduction of computers to media brought with it powerful tools for creativity and efficiency, but also the complexity of software-based systems. Now, with the transition to IP, the documentation complexity compounds further.
And, as we step into the AI-infused future, both the complexity and potential for bad data increase yet again. We face a world where we must document virtualized systems and complex, nondeterministic software such as AI agents as they spread through our organizations. Modern approaches to media systems documentation are needed.
Our challenge lies now in the rapid expansion of documentation scope. The format we are familiar with is the AutoCAD detailed design drawing. This format has proven useful to illustrate signal flow and the physical layer. However, it can rarely capture all the important, logical details such as network configuration data.
The traditional broadcast drawing is insufficient. Just like during the transition to computer-assisted design many decades ago, those who embrace new methods and tools early on will benefit the most in the new documentation paradigm.
Shifting From Drawings to Data
Images, drawings and diagrams have always been one of the most effective ways for humans to interact with complex datasets. Computers, by contrast, are better suited to process huge amounts of numeric data. AI is now able to help us bridge these two worlds. Soon, many design engineers will be using AI to produce system designs. There are guaranteed to be differing opinions, but one principle is clear: data and visualizations both remain essential.
Drawings can be created from the data, and data can be updated instantly with changes made to drawings and even the real-world telemetry of system changes. A single source of truth becomes obtainable. Models can be trained on device configurations, network connectivity patterns, and efficient infrastructure design.
This means the right tools will be able to suggest requirements, develop high-level designs and, with human oversight, output the broadcast design artifacts for complete systems.
For teams that are not bound by legacy industry norms, it creates an opportunity to evolve into a completely new world of design processes. Consider Netbox, an open-source tool for network documentation upon which Deloitte has built a broadcast toolset called Ndox.
Database-backed approaches aren’t new. AutoCAD has a database under the hood. But unlike AutoCAD, Netbox and Ndox are aligned with the underlying data model. Instead of just lines and rectangles, it’s a relational database of cables, devices and IP addresses. This approach allows users to keep drawings or new visualizations in lockstep with design information because they are generated directly on demand from the live database.
The IP Documentation Challenge
Data is intrinsic to modern broadcast documentation. What once was a stack of printed drawings now requires a folder of spreadsheets. The maintenance of that data is frequently where broadcasters encounter long-term challenges. And it is getting harder.
Just 10 years ago, IP addresses might have been stored in a basic Excel spreadsheet. However, with the introduction of ST 2110, this has quickly snowballed into a collection of spreadsheets to manage all the multicast signal data.
We now need more than just accurate records. We need a proactive means of validating, managing and identifying conflicts in the current system. To meet the constantly evolving needs of a modern broadcast facility, closing the loop between design, commissioning and ongoing monitoring becomes necessary.
The key is to go beyond system design and create a design system. This is the premise of tools like Ndox—a nexus of information for hardware, virtualization and all the connections between. This is also the point where AI has the potential to greatly enhance data quality, or if inadequate controls are in place, to corrupt it.
Where AI Fits, and Where it Creates Risk
AI significantly decreases barriers to entry for building tools that can manipulate critical systems. This offers many advantages but also introduces the risk that users less familiar with system intricacies may inadvertently compromise them. Even if users find AI assistance helpful, at the macro level, ungoverned AI usage may increase data silos and documentation sprawl. It can also lead to new operational risks.
AI can improve documentation, but to do so it must be folded into structured data processes and governance. We do not need hundreds of spreadsheets to be replaced by hundreds of custom-built, vibe-coded apps that do not talk to each other.
AI is starting to become a powerful tool to replace the tedious, manual efforts of engineers. However, AI has limitations. For example, the need to compress context can easily lead to loss of relevant data. To leverage AI properly, the best approach is to limit a given use to a narrowly defined scope.
AI works best at solving problems in small pieces. AI can’t yet grasp the decision-making factors of the technicians who have built today’s broadcasting facilities. Technology on its own will not replace human creativity and judgment.
As always, the theme here is change. Technical evolution is always underway. The challenge for you is to find that right moment to jump into the next wave. For documentation, that time is now.
