Your bank app goes blank, your cloud storage hits a wall, and you're left staring at a spinning wheel of death while the office IT guy pretends he knows why. It turns out the people keeping the internet running are essentially flying a plane while building the engine mid-flight. Nikhil Handigol, who serves as Co-Founder and Chief AI Officer at Forward Networks, argues that the networking industry is fundamentally broken. We've reached a point where businesses operate massive, global infrastructures without any real, provable way to know how those systems will behave when things actually change.

In any other field, this kind of 'fingers-crossed' approach would be a scandal. If a pharmaceutical company tested new drugs the way engineers handle network updates, there'd be riots. Engineers usually model molecular interactions before clinical trials and software developers run rigorous tests in staging environments before letting code touch the real world. Networking teams, however, are still relying on tribal knowledge and manual reviews that look more like an old-school committee meeting than modern engineering.

In networking, where operational resilience is directly tied to business success, changes are still implemented based on outcomes that can't be verified before they happen.

This operational gap isn't just an annoyance; it's a massive financial drain. Gartner reports that a single hour of unplanned network downtime can set a company back more than $500,000. Once you factor in the ripple effects across an entire business, the total losses often skyrocket past the $1 million mark. Most of these outages are self-inflicted, caused by human errors during routine maintenance. The situation looks less like a technical hurdle and more like an expensive habit.

Companies are drowning in data, but it's the wrong kind of information. Engineers have tools for monitoring, configuration management, and incident response, but these systems don't talk to each other. They provide fragmented, noisy alerts that tell you something is broken after the fact, rather than giving you a map of the network's behavior before a change is made. Critical infrastructure keeps failing because we're making updates based on guesses while pretending we've got a plan.

The AI Illusion

There's a massive push right now for IT leaders to dump their old processes and switch to AI agents that can manage networks at machine speed. While that sounds great on a slide deck, it's a dangerous game. If your foundation is unverified, adding AI is just like putting a supercharged engine in a car with no brakes. It doesn't fix the operational gap; it just accelerates the rate at which your network can make catastrophic mistakes at scale.

To bridge this divide, firms are looking toward the concept of a 'network digital twin.' This involves building a mathematically accurate model of every single device in the network. By treating each device as a transformation function, engineers can run a simulation to see how a change will affect traffic, security, and connectivity before they ever push a button. It's the difference between guessing where a wire goes and having a complete, high-definition blueprint of your entire digital estate.

If we move to this model, the results are game-changing. Change cycles that used to take days of agonizing review boards and 'method of procedure' paperwork can be slashed to hours. Engineers move from being glorified traffic cops cleaning up outages to architects building the future of their company's infrastructure. This approach stops the bleeding, allowing new cloud expansions and AI workloads to launch without the constant fear that they'll break the backbone of the organization.

For businesses in places like Lagos, where connectivity is the lifeblood of the growing tech ecosystem, these bottlenecks are even more punishing. When global services fail, it's often because these legacy operational gaps haven't been addressed. By adopting deterministic modeling, network teams can finally start delivering the reliability that modern businesses and their users deserve. Network teams are now positioned to stop the guesswork and start engineering with certainty.