Why One AI Model Won't Rule Them All: Choose the Right Tool for Each Job

Hello folks, welcome to the first, uh, AI briefing of 2026, uh, from the not very luxurious location of Luton Airport, um, as I go to Poland for a month to go and help a client, um, deal with some, uh, delivery of software. And surprise, I was mulling over what to discuss today. And I thought the obvious one to start the year, um, is that of course there are many, many different AI models these days and platforms with which you can leverage them. Don't forget that not every, um, platform was created equal. Not every model was created equal.

And they're all there to serve different purposes. For example, if you use, um, uh, Copilot inside of your organization, it's great. It's embedded into, uh, Office. It's embedded into Windows. You can ask it a trillion questions.

You can try and dig into how to leverage a Copilot to deal with your mundane day-to-day tasks and soft email delivery, um, document analysis, all those types of things. It's great for stuff like that. But it's also not necessarily great for, I don't know, programming, for example, where you've got different, uh, models that do better jobs. For example, you have, uh, Claude and their Opus models. Uh, you've got, uh, GPT-5 Codex.

And you've got, uh, the stuff coming out of Google with, uh, Gemini. All those models are created with a different purpose in mind and allow you to do different things. So just because you've deployed one piece of software inside of your, uh, environment doesn't necessarily mean that it's the one that's suited for every task. Whilst the models are often multipurpose and you can ask, I don't know, uh, Claude something about linguistics, and I'm sure it will tell you. But it doesn't necessarily mean it's the best linguistics model out there, nor is Claude necessarily the best thing to send and receive emails.

Maybe Copilot is. And so just bear that in mind when you're deploying stuff. Test the different environments. Test different scenarios. Test different use cases.

Because you'll find that as models evolve, as software improves, the use cases will change and the requirements will be different. There you go. Quick and easy today in the blazing sunshine before I go and hop on a plane. Um, yeah, make sure you test different engines, different models for different purposes, and don't just rely on one size fits all across your environment. Um, I hope that has been useful.

Stay tuned for more stuff coming over the course of the next, uh, few months as we delve more into AI, the use cases of AI, and how to leverage it best in the workplace. I'm off to catch a flight, so bye for now, and I'll see you tomorrow. Why hire when you can partner? Concept Cloud's leading engineers build your startup's prototype without the overhead. Launch faster.

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