For the last couple of years, most AI conversations I’ve had started the same way. Which model is best? Which one is smartest this month? Who just topped the leaderboard? I get why. The models were the shiny part. But I’m noticing the question changing, and I think it’s the right change.
The model is becoming the engine. What actually decides whether AI is useful in your business is everything bolted around it: the harness, the tools, the data it can reach and the rules it works within.
The engine isn’t the car
A brilliant model sitting on its own knows a lot about the world and nothing about your business. It can’t see your customer list, your contracts or yesterday’s Teams meeting. It can’t raise a ticket, update a Planner task or check a device in Intune. Without connections, it’s a very well-read stranger.
Microsoft 365 Copilot is a good example of where the value really sits. Microsoft describes Copilot experiences as differing by data grounding, integration depth and licensing, with work grounding coming through Microsoft Graph. Notice what that list doesn’t lead with. It’s not the model. It’s what the model is plugged into.
Picture two businesses using the same underlying model. One just chats with it. The other has it grounded in SharePoint, connected to their line-of-business system and able to run a Power Automate flow. Same brain, wildly different outcomes.
Tools are the new skills
This is why I’m paying so much attention to the Model Context Protocol. MCP is an open-source standard for connecting AI applications to external systems. In plain terms, it’s a common plug. Build a connector once and many AI tools can use it.
Microsoft has already brought it into Copilot Studio, where you can extend an agent with tools using MCP. That changes how I think about building agents. The hard work is no longer picking a model. It’s deciding which tools an agent gets, what data it can see, and what it’s allowed to do with them.
I touched on a version of this when I wrote Why developers don’t own this one. The value of an AI tool at the command line came from what it could reach and run, not from the model’s raw IQ.
What this means for MSPs
If you run an MSP, this is good news. Model choice is turning into a commodity decision that vendors will keep changing underneath you. The harness is where your expertise lives. That’s the bit you can design, secure and support.
Here’s where I’d focus:
- Grounding: is the data Copilot reaches current, permissioned properly and worth trusting?
- Connectors: which systems should an agent touch, and which should it never see?
- Guardrails: what can an agent read versus actually change, and who approves the risky steps?
- Identity and audit: can you tell, through Entra and Purview, what an agent did and on whose behalf?
That last point matters. Every new tool you connect is also a new door. Exposed or poorly authenticated MCP servers have already shown up in security research I covered in the CIA Brief 20260516. A great harness is a secure harness.
Stop asking which model
I’m not saying models don’t matter. Better reasoning helps. But the gap between good models is shrinking, while the gap between a well-connected agent and a lonely chatbot is enormous.
So next time someone asks me which AI is best, I’ll answer with a question. Best at what, connected to what, and trusted to do what? Get those answers right and the model almost picks itself.
