The Model Won’t Matter
But something else.
A few weeks ago Microsoft announced that Medium 3.5 from Mistral will join the party as it become available in Copilot Studio. While I wrote the LinkedIn announcement post, Anthropic published Opus 5 and GPT-5.6 was announced few weeks later.
With Mistral AI we get an European Model option for our Agents in Copilot Studio, which is really great. We will get in-region data control and an alternative to the US providers.
And while every other week the next best AI model is announced by any provider, Microsoft made this a USP for Copilot. So the strategy is clear, multi-model approach, different providers but everything integrated in one platform. Which in my opinion is one of the smartest moves Microsoft made and is one of the strongest USPs.
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While everyone Stares at the next best Model
Open LinkedIn on any given week and you’ll see it: Opus 5 ships, the feed lights up. GPT-5.6 gets announced, everyone has a take. Each model release gets treated like a new iPhone has launched.
I get the excitement (and I am excited as well). The models are genuinely impressive, but here’s a reality check:
90% of users can’t actually decide which model is best for which task.
Ask the average Copilot user whether Opus 4.8 or GPT-5.5 fits their next prompt and you’ll get a blank stare.
🫵🏼 How do you handle the speed of model release cycles? Share it with us in the comments
And in customer projects, almost nobody picks Copilot because of which model sits underneath. The main reason is the Microsoft strategy, the data is already here, the permissions are (or at least they should be) in place and of course Microsoft is by default integrated in the tools you use the most everyday.
And I pledged it several times: It’s not enterprise thinking to switch the platform from one model to the other, whenever a new (best) model arise. But to have a platform strategy, invest in that platform and build the capabilities - that’s enterprise thinking.
Microsoft understood this earlier than most. While competitors fight to have the single best model, Microsoft made a different bet:
make the model swappable and own everything else.
Governance, data control, orchestration, the ability to pick the right model per scenario, often without the user even noticing. If the model becomes a commodity, the company that controls the layer above it wins.
And through the IQ-Layers every model becomes better thanks to the organizational context. Here’s a deep dive if you’re interested
How a new AI Provider actually Arrives
There’s a detail in the Mistral launch worth slowing down on, because it tells you how this machine works.
Today, Mistral runs outside Microsoft’s Boundary. Microsoft’s standard data residency and Customer Copyright commitments don’t fully apply yet. Mistral’s own terms govern its use.
But here’s the point: This is a starting state, not an end state.
It was the same with Anthropic. When Claude came to Copilot & Copilot Studio, it was a subprocessor, first outside of Microsoft’s Boundary, nowadays it is inside Microsoft Boundary and everyone in Europe is waiting for the EU Data Boundary. By the way same with Grok, which is also in preview and limited to Copilot Studio. For both admins need to opt in.
That’s the pattern. Microsoft can attach a new model fast, ship it under opt-in terms, then pull it into the governance framework over time. The “outside the boundary” status is not a flaw in the strategy.
The ability to scale is an important part. First outside Microsoft Boundary than become part of the enterprise rules with Microsoft Boundary and/or then EU Data Boundary.
What this Means for your AI Strategy
If you’re a decision-maker, the temptation is to keep asking.
“which model is the best one right now?”
It feels like the important question. It isn’t, at least not the one you should build on.
Need a kickstart for your Copilot Journey, check out my LinkedIn Learnings Courses. 👇
The model that’s best today won’t be best in six months (or maybe even not in 2 weeks). If your AI strategy is pinned to a specific model, you’re rebuilding your foundation every release cycle and keep in mind that the release cycle is under 100 days for every provider. That’s exhausting and expensive.
The better question:
What’s the layer and platform that stays constant while the models change underneath it?
For Microsoft customers, that layer is Copilot, Copilot Studio and the M365 platform around it. The governance, the data controls, the orchestration, the per-scenario model choice. You set up your governance, your agents, your guardrails and your data boundaries once. The models flow through that structure and get swapped as better ones arrive, without you tearing down what you built.
So keep in mind:
Copilot is a platform
The Platform has a multi-model approach
Work IQ, Foundry IQ and Fabric IQ make every model better
Invest in the platform and your employees, have a great UI for AI and see the models as a flexible building block that can and will change over time.
Here’s the Deal
Multi-model isn’t free of friction and I’d be doing you a disservice to pretend otherwise.
GPT is still the default for Copilot. Every new model you enable is a decision, not a default. Mistral, Claude, Grok and all that will follow in the future: All need a deliberate opt-in, a look at where data gets processed, a check against your sector’s and organization’s rules. But governance can’t stop at one place. The same questions apply to Copilot Chat, to agents inside the M365 apps, to Copilot Studio and to anything where a model touches your data.
Then there’s the user side: Right now, open Copilot Chat and you’re already looking at the model choice and it feels more like a countdown. GPT5.5 till GPT5.2. Maybe also Claude is there as well. For the average user, that picker feels like a quiz they didn’t sign up for.
“Which model should I use for this email draft?”
isn’t a question your people can answer, and honestly, it shouldn’t be theirs to answer.
This is where leadership comes in. You can’t just hand users a menu of models and call it freedom. You need to take them by the hand. Sensible defaults for most tasks. Clear guidance when it matters. That’s why I like the “auto-mode”, it simplifies for the users and it’s for 80% probably the best fit.
On a Personal Note
I’ve watched a lot of smart people spend a lot of energy on the wrong question over the last year. Which model is best, which benchmark moved, which lab is ahead this month and should we switch now.
It’s the most visible part of the AI story, so it gets the attention. But it’s not where enterprise value gets decided. The customers I work with don’t win or lose on the model. They win or lose on whether they built something stable enough to absorb whatever model comes next.
Microsoft saw that: The multi-model move is one of the smartest decisions Microsoft made in my opinion. And I guess that “best AI” model will matter a little less every quarter.
For an enterprise that has to live with its decisions for years, that’s worth more than any benchmark.
Pick the platform that lets the model stop being your problem. Then go build something on it.