What to Know About AI Models Before you Choose One

**TL;DR**
If you’ve heard names like GPT-4o, Claude 3.7, or DeepSeek-R1 and thought, “I don’t know what that means or why it matters,” you’re not alone. Most leaders are being shown AI model comparisons without any context. This blog explains what an AI model is, how it shows up inside tools, and how to think about them in terms of tasks not only features. You don’t need to understand the specs. You need to understand what the model is being used to support.

Why this keeps coming up

You’re a non-technical executive. Maybe in people and culture, operations, or the public sector. You’ve sat through a vendor demo. You’ve heard someone mention GPT-4o. You’ve been asked whether a particular tool is using Claude or Gemini. And somewhere in that conversation, the expectation shifts.

Suddenly, you're meant to choose.

But you were never taught how to evaluate an AI model. You don’t even get to see it directly as it’s embedded inside a product. The language sounds technical, the stakes feel high, and you're expected to have an opinion.

The real problem is not your lack of knowledge. It's that most internal AI conversations start in the wrong place.

How to think about AI models without the tech overload

Start with the work, not the model
AI models are statistical engines trained on text, data, and rules. They sit behind the scenes of AI-powered tools. You’re not interacting with them directly. You’re using a product — and that product is using a model to generate, summarise, suggest, or respond.

So don’t start with model names.

Start with the task:

• What job are we trying to make easier?
• Who is doing that job today?
• What matters most:  speed, quality, reliability, explanation?
• What happens if it gets something wrong?

This frame lets you assess fit for purpose not just model performance.

Real differences, real implications

Now that you’re focused on the task, model differences start to matter but only in context.

Here’s what’s surfaced in practice:

• Claude 3.7 Sonnet is known for careful, structured reasoning. It’s been used in policy, legal, and compliance teams for document interpretation and summarisation.
• GPT-4o is well integrated with business tools and handles fast, fluent responses in both text and visual tasks. It works well inside Microsoft 365.
• DeepSeek-R1 is strong in Mandarin and multilingual scenarios, so it’s being explored in call centre support and translation-heavy operations.
  

Each model has different strengths. None is universally best. If you haven’t scoped the task, the model comparison won’t help.

Where leaders go wrong

Many leadership teams try to pick a model without first agreeing on where AI fits. Before choosing a tool, your team needs to:

• Map one or two clear processes where AI might help
• Identify risks, controls, and who will use the output
• Agree what success looks like: saved time, reduced rework, improved response quality?
  

Once that’s clear, the model choice often becomes obvious. Or it becomes a vendor’s concern not yours.

What this changes for your team

Once your team stops comparing AI models in isolation and starts thinking about tasks, workflows, and context, the conversation gets clearer.

You no longer need to know the technical differences between Claude and GPT. You just need to know:

1. What function the model is performing
2. Whether that matches your operational need
3. And if the tool using it meets your safety, privacy, and performance criteria
   

This shift reduces overwhelm, improves alignment, and gives your leadership team a shared framework to evaluate future tools.

 This approach isn’t hypothetical. Organisations are using it today.

• A policy team in government uses Claude 3.7 to check consistency across long-form documents. The priority is accuracy and transparency.
• A research team uses Perplexity (powered by various models) to source quick, citation-based summaries. The focus is speed and breadth.
• A contact centre uses tools powered by GPT-4o for resolving image-based queries and responding in real time within integrated systems.
  

Each use case led to the right model for that workflow. The decision wasn’t about specs. It was about the job to be done.

Next steps: How to build your team’s confidence

If your executive team feels like it's stuck comparing terms it doesn’t understand, don’t keep pushing through. Step back.

Book a fundamentals session that gives your team a shared foundation.

Focus on:

• What an AI model is — and what it isn’t
• Where it fits inside real tools
• How to frame adoption using business logic, not technical specs
  

The goal isn’t to become fluent in AI model architecture. The goal is to make confident, coordinated decisions about where AI fits in your organisation.

Ready to move to clarity?

Our AI Fundamentals Masterclass is designed for leadership teams who are being asked to make AI decisions without the technical background.

You’ll leave with:

A shared understanding of how AI models support real work
Clear criteria for evaluating tools and vendors
Practical guidance on what to prioritise, where to test, and how to avoid mistakes
→ Book a Fundamentals Masterclass