Network Architect
Evaluate & Select Network Technologies
What You Do Today
Assess emerging technologies — Open RAN, network slicing, SASE, 400G optics, edge computing — against your network requirements. Run lab trials, conduct vendor bake-offs, and make technology selection recommendations that the network will live with for a decade.
AI That Applies
AI aggregates vendor performance data from industry trials, analyzes specification compliance, and models the total cost of ownership across technology options including migration costs.
Technologies
How It Works
The system ingests specification compliance as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Technology evaluation becomes more data-driven. AI synthesizes trial results and industry benchmarks that would take weeks to compile manually.
What Stays
The judgment to bet on a technology that's promising but unproven, the ability to see through vendor marketing, and the strategic vision to align technology choices with business direction are irreplaceable human skills.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for evaluate & select network technologies, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long evaluate & select network technologies takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your engineering manager or VP Eng
“What data do we already have that could improve how we handle evaluate & select network technologies?”
They're deciding which AI developer tools to adopt team-wide
your DevOps or platform team lead
“Who on our team has the deepest experience with evaluate & select network technologies, and what tools are they already using?”
They manage the infrastructure that AI tools depend on
a senior engineer who's adopted AI tools early
“If we brought in AI tools for evaluate & select network technologies, what would we measure before and after to know it actually helped?”
Their experience shows what actually works vs. what's hype
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.