The AI Landscape
This page is written for business directors and owners — not IT departments. If you've ever nodded along in a meeting about AI while secretly thinking "I have no idea what that actually means," you're in the right place.
The Basics
Jargon Buster
No jargon, no acronyms without explanation. If a supplier uses these terms and can't explain them this clearly, that's a red flag.
"ChatGPT"
Software trained on text that predicts what word comes next. Good at generating text, not at thinking. The foundation of most AI tools you'll encounter.
"A robot"
A software program that can take actions on your behalf — reading emails, writing reports, monitoring systems. It doesn't just chat — it does.
"An AI assistant"
A tool that helps you do tasks faster, but you're still driving. It sits next to you — it doesn't work for you independently.
"Skynet"
An AI system that runs tasks independently on a schedule — like an employee who doesn't need constant supervision. It checks in, reports progress, and asks for help when unsure.
"Being stuck with a supplier"
When switching away from a vendor becomes so expensive and disruptive that you're effectively trapped. Microsoft Copilot is designed for this — your data becomes part of their ecosystem.
"Complicated IT setup" / "Government stuff"
AI that runs on your own hardware, not in someone else's cloud. Your data stays on your premises. No vendor can turn it off or see it. The difference between owning your team and renting one from a landlord.
"AI lying"
When AI generates confident-sounding text that's factually wrong. Not malicious — it doesn't know the difference between true and false. This is why human oversight matters.
The Honest Assessment
A Note for Directors
IT teams are brilliant at keeping things running. But their job is stability, not transformation. Here are some things to consider.
IT's job is to keep systems running and risks low. That's exactly right — and it's also why they're not the best people to advise on transformation. Their incentives align with "wait and see," which is sensible for operations but potentially costly for strategy.
If AI automates tasks your IT team currently manages, they may instinctively resist it — not because they're wrong, but because they're human. This doesn't make their concerns invalid, but it does mean they need weighing alongside other perspectives.
When IT explains AI in technical terms, it can feel impenetrable. But if you can't challenge the explanation, you can't make an informed decision. This page exists to give you the language to ask the right questions.
We don't replace your IT team. We work alongside them, translating between business strategy and technical reality. Your IT team keeps things running. We help you understand what's possible, what's sensible, and what to start with.
Common Myths
You might hear: "The technology isn't mature enough. We should wait 2-3 years."
The question isn't whether AI is ready — it's whether you're ready to start from where you are. Starting small now builds capability. Waiting means falling behind permanently.
You might hear: "We've already experimented. It didn't do much for us."
Using ChatGPT once and deciding AI isn't useful is like using a hammer once and deciding construction doesn't work. A tool isn't a system. We build systems — multiple AI capabilities working together, configured for your business, running daily.
You might hear: "It's too risky — it'll cost jobs and damage morale."
We handle the routine so your people can focus on what humans do best — relationships, judgement, and strategy. The person who understands the AI integration becomes the most valuable person in the room.
You might hear: "The budget isn't there. It's expensive and the ROI is unproven."
Our Quick Wins tier starts from less than you're probably paying for Office 365. It pays for itself — or we stop. The real cost is the opportunity cost of doing nothing while competitors adopt.
The AI Provider Landscape
The AI industry wants you to pick a side. Cloud or local. Open source or proprietary. Co-pilot or agent. The reality is more nuanced — and more interesting.
Microsoft, Google, and Amazon want you embedded in their ecosystem. Co-pilot is designed so that once your data, workflows, and institutional memory are inside it, leaving becomes prohibitively expensive. This isn’t conspiracy — it’s bundling strategy. Ben Thompson has written extensively about it. The switching cost for a large enterprise leaving Copilot is estimated at $1–5M+.
Vendor Lock-in
Once your AI “understands your company better than you do,” you can’t leave. Your data becomes their moat.
Dependency Pricing
Start cheap, scale expensive. Once you’re dependent, the price goes up. It’s the cloud playbook all over again.
Single Point of Failure
OpenAI had a triple outage in June 2024. Builder.ai collapsed after raising $445M. 74% of enterprises would face significant disruption if their primary AI vendor failed.
Open-source models sound like the answer to vendor lock-in. And they’re part of the solution. But “free” doesn’t mean “no strings attached.” Meta’s Llama fails 9 out of 10 OSI criteria for being truly open source. Mistral started open, now withholds training data and pushes their cloud platform. The playbook is emerging: release weights for free to commoditise competitors, then gatekeep the value.
Open Weights ≠ Open Source
You can use the model, but you can’t see how it was trained, what data it was trained on, or whether commercial use rights might change.
Free Model, Expensive Everything Else
The model is free. Hosting, fine-tuning, maintenance, and expertise to run it? That’s the real cost. “Free puppy” economics.
We’re not anti-open-source or anti-cloud. We’re pro-honest. The answer isn’t “cloud everything” or “local everything” — it’s understanding the trade-offs and choosing your dependencies with eyes open.
AIAI View
We run multiple AI providers — not because we can’t decide, but because dependency on one is the real risk. Local models handle 80% of daily work on hardware we own. Cloud models handle the heavy lifting when needed. The direction of travel is more local, not less.
Own the routine
Local models for daily work. Your infrastructure, your data, your rules. No vendor can turn it off.
Expand your team
Choose who joins, who leaves, and when. Cloud models as team members you bring in for specific work, not landlords.
Choose your dependencies
Be intentional. Know what you rely on and why. The cost of simplicity is dependency — and dependency is more expensive than complexity.
85% of enterprises shifted AI workloads back on-prem by mid-2025. The direction of travel is clear. We’re already there.
Next Step
The fact that you're here means you're already ahead of 80% of businesses. Let's have an honest conversation about what AI could actually do for yours.
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