It’s become a recurring conversation in boardrooms, pitch decks, and strategic planning sessions:
“Should we build our own AI model?”
At first glance, it’s an attractive idea. Your own model. Trained on your data. Unique to your brand. It sounds like a digital goldmine an IP asset and competitive differentiator all rolled into one.
But like most things in tech, the reality is a little more complex.
Why Businesses Want Their Own Models
- Control – No reliance on third-party APIs or shifting terms of service.
- Data Sovereignty – Sensitive or proprietary data stays in-house.
- Brand Differentiation – “Powered by our own AI” carries weight.
- Long-Term Cost Avoidance – Avoiding per-token fees from OpenAI or Anthropic.
- Investor Appeal – Owning your own model can signal innovation and deep capability.
These are valid motivations. But in many cases, they’re built on assumptions that don’t fully align with the reality of building and maintaining production-ready AI.
The Reality Check
Here’s what’s often overlooked when businesses decide to go down the DIY AI route:
- You don’t just build an AI model. You build an entire AI department.
That means MLOps, DevOps, data engineers, prompt engineers, and researchers. This isn’t a weekend sprint, it’s a multi-month, multi-million-pound commitment. - Data is your fuel, and most businesses don’t have enough of it.
To train a model worth using, you need massive volumes of structured, labelled, high-quality data. Not just logs and emails. Not just “we’ve got loads of data.” Real, usable data. - Model performance isn’t the only metric.
Security, governance, ethics, explainability, latency, integration, ongoing tuning… these all matter. Especially if you're operating in regulated sectors like finance, health, or government.
What’s Often Forgotten: You Can Customise What’s Already Built
Many businesses don’t need to build from scratch. Today’s AI leaders, OpenAI, Anthropic, Cohere, Mistral, offer models that can be fine-tuned or embedded into your workflows with incredible precision.
Think of it like using AWS to host your infrastructure instead of building your own datacentre. You can still deliver innovation and control the experience, without becoming a cloud provider yourself.
The Punchline
Owning your own AI model might make sense if:
- You’re at Google-scale
- You have access to rare or proprietary data no one else does
- You’re trying to solve a niche problem that general models can’t address
- You're creating true IP, not just another chatbot
But for most businesses?
The real magic isn’t in building models. It’s in how you apply them.
Next Up in the Series:
Part 2 – “Pre-Built AI: The Underrated Powerhouses”
We’ll explore how to get strategic value from models like GPT-4 and Claude without reinventing the wheel.
