The hype and fear surrounding AI are immense. Every day, business leaders are bombarded with new features, platforms, capabilities, threats, and concerns. The topic of AI has become divisive in some circles, leading to exaggerated claims on its potential and pitfalls.
Like all things, the truth is somewhere in the middle of the two extremes.
However, within the AI adoption frenzy, there are concerning stories of companies that invest in AI but fail to achieve the expected results. Why? It’s not a mystery, it’s their network.
Let’s explore how even exceptional AI products can and do come up well short of targeted goals through no fault of their own.
Setting the Stage: A Mythical AI Disaster Story
Once upon a time, there was a mid-sized firm with massive growth ambitions. They saw AI as a cost-effective way to gain an edge over larger, more established competitors.
The firm conducted its research, identified AI software solutions, and collaborated with a partner who promised a rapid return on investment.
Excited, the firm gave the green light to the project, and in no time, their workflows were modernized with the power of AI software for analytics, automation, and other tasks. But then the storm came. When everything was up and running, the results were a downgrade.
Processing times lagged, dashboards often froze, real–time data wasn’t accessible, predictive outputs arrived late, and overall firm productivity dropped massively. What was meant to be an efficiency advantage became an obstacle.
Was the software installed incorrectly? Was the salesperson to blame? Was the firm lied to? No. Their IT infrastructure was outdated.
AI Is Only As Good As The IT Infrastructure
AI adoption is beneficial, but only if you implement the necessary IT infrastructure to support it. Old networks weren’t designed for the demands of AI — or anything even close to it.
AI is bandwidth-hungry. It has an insatiable appetite for transmitting data across the network. Legacy servers, outdated switches, and undersized cabling create choke points. There’s simply no way they can support the machine learning algorithms or gigabit real-time operational data of AI, let alone cloud-scale processing.
Those built-in bottlenecks of the legacy network throttle performance, while latency cripples data flow. All combined, it means AI can’t learn fast enough to meet its potential benefits.
What Happens When Legacy Systems Meet AI
Any mismatch between cutting-edge software and legacy hardware will produce unwanted consequences and massive frustration. Your upgrade expectations will likely fall short of the promise, and workflows will be less efficient than before.
Here are some of the ways companies experience AI upgrade disappointment.
Lost Productivity
Slower than before, data transfers bring every operation to a snail’s pace. The side effects include delayed automation responses, slow file transfers, troubles logging into platforms, and ultimately, idle staff.
Slow Data Processing
Processing speed is the lifeblood of AI technology. When processing slows, its ability to synthesize data, deliver insights, or act on the data lags significantly.
Downgraded Customer Experiences
When you move slowly, clients feel it. Especially when the pace is slower than previously established performance times. Service issues often result in diminished projects or, in worst-case scenarios, client defection.
Modernize Your IT Network Before Adding AI
Let’s go back to the story about the mid-size firm. The lesson is an important one for businesses. Their plan wasn’t wrong, but it was incomplete.
After realizing they wanted to improve operations using AI, their first call should be to an established IT partner — one who specializes in business-grade IT systems. On that call, the firm needed to outline its current position, plans for AI, and request an assessment. Doing that would have revealed issues with switches, servers, routers, cabling, and other factors that would have impeded AI performance. The assessment isn’t intended to prevent the firm from adopting AI; instead, it aims to highlight what else needs to be done to deliver IT ROI and meet performance expectations. Sometimes that’s achieved with an all-in-one program where everything is upgraded and integrated at once. Alternatively, for budgetary reasons, the firm and IT partner could develop a phased approach that spreads the investment and upgrades over time. Both strategies can and do work. The key isn’t choosing one of the other; it’s about making the call right away and creating a complete plan. Only then can technology investment pay off and workflows really benefit.
Ready to Future-Proof Your Business with AI?
AI innovations are a must-have for future-proofing business operations. Identifying the best way to achieve those AI aspirations means working with an IT expert. Matrix-NDI solves the challenges of AI integrations by unlocking the full ROI of your IT infrastructure. We design and install networks built for maximum speed and perfectly matched to bandwidth demands.
Why work with Matrix-NDI?
With on-staff Registered Communications Distribution Designers (RCDDs), coast-to-coast service coverage, and partnerships with leading data networking providers—including Extreme Networks, Nile, and others—Matrix-NDI delivers the expertise and reach to support your technology goals. We invite you to connect with us to see how our expertise, partnerships, and national reach can help solve your challenges.
Contact Matrix-NDI to get started. Let’s build smarter, safer, more connected spaces — together.
