Operationalizing Innovation: How AI Will
Change Skill Development and Validation

By: Maria Popo

AI's Real Opportunity: Scaling Human Capability 

Broadband operators are used to managing change. Fiber networks are expanding, hybrid environments are becoming more complex, and software-driven operations are reshaping how teams monitor and improve performance. Artificial intelligence is entering that environment quickly, but the real question is not whether operators can deploy another tool. It is whether their people can absorb new technology, apply it consistently, and prove they are ready to use it in the field.

That is where AI can make a practical difference. Not as a replacement for skilled practitioners, and not as a shortcut around training, but as a tool to scale human capability. AI can help operators capture expert knowledge, guide learning in the flow of work, and validate competency based on real operational performance. Used well, it can shift workforce development from periodic training events to continuous learning that happens as part of everyday work.

The timing matters. Fortune Business Insights projects the global broadband services market will grow from $608.70 billion in 2026 to more than $1.15 trillion by 2034, an 8.3 percent compound annual growth rate. That growth means more networks to build, more assets to maintain, and more decisions to make every day. The industry cannot meet that demand through infrastructure investment alone. It also has to scale the knowledge, confidence, and judgment of the people responsible for making those networks work.

The workforce challenge is already visible. Pew Research found that 41 states and Washington, D.C., identified workforce challenges in their Broadband Equity, Access, and Deployment or Digital Equity Act (BEAD) plans.

Pew’s numbers point to a structural issue. The industry needs more people, but it also needs new ways to develop people faster and more consistently. Classroom instruction, shadowing, and certification will continue to matter. But traditional models can take workers out of the field, depend on scarce experts, and often measure completion rather than capability.

AI can help close that gap by making expertise more accessible at the moment it is needed.

What Is Agentic AI, and What Is Its Role in Network Operations?

One of the most useful developments is agentic AI: systems that can plan, coordinate, and guide actions across a multi-step process. In network operations, that might mean helping a technician follow a troubleshooting path, recommending next steps based on telemetry and service history, or guiding a customer care representative through a complex service issue. The goal is not to remove the person from the decision. The goal is to extend expert-informed decision-making to more people, more consistently.

Every operator has pockets of deep expertise that are difficult to scale. It may be a senior technician who knows where to look when a service issue appears, or an engineer who recognizes the pattern behind a recurring impairment. Too often, that knowledge lives in individual experience, informal notes, or habits that newer workers learn only if they are paired with the right mentor.

Making Expertise Scalable 

Consider fiber blowing. Done well, it requires preparation, equipment setup, route awareness, pressure control, cable handling, and disciplined execution. Done poorly, it can create safety issues, failed installs, damaged materials, and costly rework. AI-enabled guidance could help a technician confirm the sequence, identify risks, check setup conditions, and document the result. The technician still performs the work and owns the outcome. But the process becomes more consistent, and learning happens in context.

The same principle applies to proactive network maintenance. AI can analyze large volumes of network data, flag anomalies, and suggest where to look first. That can make teams faster and more precise. But it does not eliminate the need for human judgment. A person still has to understand the local plant, assess trade-offs, decide what action is appropriate, and be accountable for the result.

Learning to Use AI Properly in the Flow of Work 

Finding the right balance between professional talent and where technology can assist is essential. AI is strong at pattern recognition, retrieval, summarization, and process guidance. It can reduce time spent searching for information and help newer employees learn from accumulated experience. What it cannot do is replace critical thinking, ethical oversight, leadership, or accountability. It cannot build trust with a customer, mentor a colleague through a difficult situation, or make a values-based decision when the data is incomplete. Those remain human responsibilities.

That is why AI in workforce development should be framed as augmentation: helping people get better faster.