Nerds On Site - Edmonton/Red Deer

10/14/2026

$19 a month for one person's business calls sounds reasonable until you add a second person and watch the bill climb.

That's the math a lot of solo owners and small teams run into with per user pricing. Quo's entry plan starts at $19 per user per month, and every new team member adds to that. Unitel Voice starts at $9.99 a month with 500 minutes and 3 users included, so for a small team the gap gets wide fast.

Cheaper isn't automatically better, though. The real question is what you're paying for. If you mostly need a professional number on your phone, clean call handling, and personal and business calls kept separate, paying extra for collaboration features and AI tools you never open is just overhead.

Setup time is a cost too, and it's one people forget to count. A tool that takes weeks of tweaking to feel right isn't really saving you money, even if the sticker price looks good.

Which matters more to you right now, a shared inbox for your team or a business line that just works without you babysitting it?

Looking for a simpler, cheaper alternative to Quo (formerly OpenPhone)? See how Unitel Voice compares on pricing, setup, and features.

10/13/2026

Okay, I'll admit I rolled my eyes the first time a client asked me to set up OKRs. Another acronym, another spreadsheet nobody opens. Then I looked at the numbers. According to a Haufe Talent study cited in a recent Custom Ink article, 60% of employees at companies using OKRs have a clear picture of their company's strategy, compared to 37% at companies without them.

Where I've seen this actually work with the teams I consult for is in the boring details. Keep it to three to five objectives per quarter, because any more and everyone is busy but nobody finishes anything. Post the goals where people can see them every day, and hold a short weekly check in to talk about what's blocked. Most of the collaboration trouble I get called in to fix isn't people not caring. It's nobody knowing who owns the decision or what the team is actually aiming at.

Could your team's quarterly goals fit on one screen? If they don't, I'd bet the delays are already showing up in your project tracker.

The new year is a strategic opportunity for corporate teams to align everyone around shared objectives, reinvigorate company culture, and set the foundation for meaningful achievements. Whether you’re leading a department of five or an organization of 500, how you approach goal-setting in January ...

10/12/2026

Somebody just seriously floated putting AI computing in orbit. My first reaction was "wild." My second was "okay, but who decides what it gets used for?"

That's the part I keep coming back to from this piece on AI expanding beyond Earth. AI isn't really just software anymore. It's infrastructure. Every new generation needs more chips, more power, more cooling, and bigger data centers. So engineers are exploring advanced nuclear, space, based solar, and yes, orbital computing. Some of those ideas will flop. Some will work.

What stuck with me is this. Unlimited computing doesn't give you unlimited wisdom. I've sat in plenty of IT projects where the technology was never the real problem. The problem was that nobody stopped to ask what we should build, who it actually helps, and what happens when it goes sideways.

When you're deciding on your next tech move, do you spend more time on the "can we" or the "should we"? Curious where people land on this one.

Cosmic Abundance: How AI is driving demand for clean energy and space-based infrastructure—and why wisdom matters more than technology.

10/11/2026

Last month a client proudly showed me their brand new AI tool like it had just won them the lottery. I asked what it was doing for revenue. Silence. Then came the answer: "Well, it writes our ad copy really fast." 😅

Fast copy is nice. But it isn't a growth strategy.

What I tell people is to pick ONE thing you want to improve, like lead quality, conversion rates, or keeping customers from quietly walking away. Measure where you're at today. Then let the AI loose on that one task and see if the numbers actually move.

If they don't move, cut it. Most tools are like my old gym membership. They look great on the credit card statement but do nothing if you never walk through the door. 🏋️

The other thing nobody wants to hear is that AI output still needs a human checking it. I've seen these tools confidently make stuff up in a report. Trust, but verify.

So what's the one marketing task in your business you'd love to hand off to a machine? Be honest, I won't judge. 😂

10/10/2026

Most people hear "natural language processing" and picture a chatbot. That's only one corner of it. 🤖

This article breaks down six ways businesses are using NLP, and a few of them caught my attention. Sorting through piles of contracts, reports and policy documents is a headache for most teams, and NLP can help make sense of that mess. Sentiment analysis can also show you how customers actually feel about your brand, not just what your survey says.

The one I keep coming back to is how it helps different departments understand each other. Pilots describe a problem one way, engineers read it another way, and NLP can bridge that gap by making the documentation easier to follow. Clearer documents, fewer misunderstandings.

My take is simple. Start with one specific problem you want to solve, then see if NLP fits it. Trying to automate everything at once is how projects stall.

Which part of your business do you think would benefit most from NLP? Drop it below, I'm curious what you're seeing out there! 👇

BusinessTalk

Natural language processing (NLP) is not just a subset of artificial intelligence (AI); it’s a transformative force that empowers computers to understand, interpret, and communicate like humans.

10/09/2026

Most project requirements tell you what a system should do. Almost none of them say how well it needs to do it. That gap is where a lot of projects quietly go sideways. 🤔

Examples make this click faster than theory. Respond to user action in less than 10ms. Recover from any operational disruption in less than 4 hours. Be available 99.999% of the time, which is the famous "five nines."

The approach that stood out to me is keeping a standard set of these for the whole organization, then trimming and tuning it for each project. Some won't apply. If a project has nothing to integrate, integration requirements can go. The rest get tightened with real numbers so the architects and developers know exactly what they're building toward.

A requirement like "the system should be fast" is basically a wish. "Respond in less than 10ms" is something you can actually test.

Which of these would be hardest to pin down on your current project? Or have you ever had a "fast enough" requirement come back to bite you? Share your stories below! 👇

Here are some non-functional requirement examples to help you on your journey to more successful technology projects.

10/08/2026

Your IT budget just got approved. More money than last year. You should feel good about this.

Then you run the numbers and realize 72% of it goes to keeping the lights on. Legacy systems that work fine but cost twice as much to run as they should. Custom workarounds nobody documented. That one ERP from 2008 that only one person really understands.

The frustrating part? This isn't unusual. For a 50, person organization with a $250K IT budget, technical debt often eats up $180K in maintenance while only $69K actually goes toward growth or innovation.

A Forrester report found that 75% of technology decision, makers expect technical debt to reach a severe level in 2026. And when companies ignore it, their AI projects see ROI drop by 18% to 29%.

What I'm seeing with clients: the ones who win don't ask for more budget. They start with visibility. They map what's actually consuming resources. Then they prioritize ruthlessly. Companies that actively manage technical debt free up engineers to spend up to 50% more time on strategic work.

The difference between a team that's stuck in maintenance mode and one that's actually building something comes down to one decision: treating modernization as a growth enabler instead of a cost center.

What's eating your budget right now? Legacy systems, infrastructure patches, knowledge silos? Drop it in the comments.

10/07/2026

Watching teams get crushed by alert fatigue right now. Monitoring systems are firing off so much data that people genuinely can't tell signal from noise anymore. Your SIEM generates thousands of alerts per day, but which ones actually matter?

This is where AIOps stops being a buzzword and becomes table stakes. Machine learning systems that can actually learn what normal looks like for YOUR infrastructure, predict failures before they happen, and handle the routine stuff autonomously. No more humans drowning in telemetry.

The other reality check: most orgs are still treating monitoring like a fire extinguisher. You wait for the alert, then react. AIOps flips that to proactive. Track efficiency, spot deviations early, let the system handle remediation while you sleep.

If you're still manually triaging alerts in 2026, you're losing time your competitors don't waste. What's your current monitoring setup actually catching versus what's just noise?

10/06/2026

Most enterprise shops are still stuck in pilot purgatory with their AI automation projects.

I just looked at a fresh survey from Gartner covering Q1 2026, and 80% of enterprises now have at least one production AI agent running. Sounds impressive until you look at what's actually happening. A lot of those pilots are stalled before they generate any real value. It's not because the tech doesn't work. It's because teams are swinging for the fences when they should be bunting.

The move that's actually working? Pick one specific process. Something with clear inputs, clear outputs, and a human checkpoint built in. Automate that thing end to end. Get it working. Prove the ROI. Then move to the next one.

Not "deploy an autonomous agent across the business." That's how you end up with shiny object syndrome and a pile of failed projects. The winning teams right now are treating agentic AI like a precision tool, not a sledgehammer.

What's your experience been? Are you seeing pilot projects actually convert into production wins, or are they just sitting there collecting dust?

10/05/2026

Just looked at the latest Gartner data on agentic AI and something jumped out: 80% of enterprises say they have at least one AI agent in production right now. That's up from 33% two years ago.

But here's where it gets real. McKinsey's follow, up research shows most of those deployments are stalling before they actually deliver value. The gap between "we launched an agent" and "this thing is saving us money" is massive.

I'm seeing this play out with clients. They pilot an AI agent, get excited about the proof of concept, then hit the scaling wall. Governance breaks down. The agent starts making decisions nobody actually wants it to make. Teams don't trust it. It sits there doing nothing.

The teams actually pulling this off aren't trying to automate everything at once. They're going narrow. Back, office workflows first. Finance approvals. Compliance tasks. Stuff that's repetitive, rule, based, and doesn't require human judgment calls. Once that works, they move to the next thing.

The other piece they get right? They build governance from day one, not after things break. Who owns the agent? What happens when it fails? What do we audit? Those conversations happen before deployment, not after.

If you're sitting on an AI automation project that's stalled, that's probably where to start. Is the scope actually manageable? Do you have buy, in on how the thing gets monitored and controlled?

What's happening with AI automation in your world right now?

A data-backed look at which AI automation trends enterprises are actually deploying in 2026 — agentic AI, back-office automation, and identity governance — versus what's still just hype.