Have you ever thought about how many different AIs a typical knowledge worker encounters in a single day? Here’s my quick list:
A typical knowledge worker could easily have 5+ different AI implementations in daily use. Each one works differently, and has its own interface, policies, and data handling. None of them talk to each other.
And they’re all controlled by different entities.
When AI is built into applications, the app vendor controls everything. Gmail’s AI follows Google’s rules. Salesforce’s AI follows Salesforce’s rules. Microsoft Copilot follows Microsoft’s rules.
When AI lives in your OS, Microsoft or Apple controls it. They decide what it can see, what it can access, how it’s governed. You inherit the guardrails and telemetry components that they choose to expose.
When AI lives in your browser, you’re depending on the browser maker to set the boundaries and secure it.
When AI lives as standalone tools like ChatGPT or Claude, you’re back to managing another SaaS product with enterprise admin features bolted on.
Each one creates a governance silo with its own admin console, policy language, and security model.
A lot of people ask questions like, “Which AI should we adopt?” But that’s not the right question anymore.
You’re going to have AI from all these sources whether you want it or not. Microsoft is baking Copilot into Windows and Office. Google is baking Gemini into Workspace and Chrome. Every SaaS app is racing to add AI features. Device manufacturers are adding NPUs and on-device AI capabilities. Workers are using whatever AI helps them get their work done. Fragmentation is reality.
Trying to “standardize on one AI” is like trying to “standardize on one app” in 2010. So the real question isn’t which source to choose, it’s where you establish your governance boundary.
We’ve seen this before. Applications started on client/server, then moved to the web, then to mobile, then to SaaS. Each transition fragmented the landscape, and each vendor tried to own the stack. Eventually someone won by providing the abstraction layer that worked across all of them.
The same thing is happening with AI right now. Every vendor is trying to be your AI layer, and every layer has valid use cases. The winner won’t be the best AI model or the most integrated AI feature. The winner will be whomever provides the governance abstraction that works across all the layers.
I’m seeing enterprises try to solve this in predictable ways:
This doesn’t work because you’re not focusing on one source, so even the best governance at one layer still leaves policy holes at the others. So instead of governing AI by controlling (or limiting) the layer it lives in, you need governance that works across layers.
The workspace is your governance boundary
This is where the concept of the workspace becomes critical. Not the workspace as “a collection of apps,” but the workspace as I defined it recently: the place where apps, identity, security, and context converge.
The workspace as a concept is bigger than the apps, OS, or device. It’s where work actually happens, and the only place where you can apply consistent governance across all the AI implementations your workers are actually using.
Think about what you need to govern AI in the workspace:
All of these are intertwined. A change in device posture could change the context, which changes what the worker can see, which changes what the AI working on their behalf can show them, etc. This means a single AI can’t get the full context to adequately control its behavior via its narrow view of the larger workspace. This can only be done from the outside, at the workspace level, since that’s where all these layers come together and where the actual work is done.
Even though the AI technology is new, the governance is familiar. You need:
Where AI lives determines its capabilities, but where you govern it determines your risk.
Luckily we’ve all been building this infrastructure for decades, and we’re now fortunate that the same workspace that delivers secure access for humans can deliver secure access for AI.
Rather than asking “which AI should we adopt?”, start asking “where do we establish our governance boundary?”
AI is showing up in every layer of your EUC stack. The question is whether you have a unified way to govern it or whether you’re going to manage dozens of disconnected AI implementations with no common policy surface.
At Citrix, we think about the workspace as that governance layer. We’re not trying to force everyone into one AI vendor or make sudden and drastic changes to how EUC is managed. Instead, we focus on the control plane that works across whatever AI implementations your workers actually use. This way of securing the fractured, worker-focused AI landscape will be critical in the coming years.
Let AI live where it works best. Govern it where work happens.
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Brian Madden is a VP & futurist at Citrix. He writes about the future of work, AI in the workplace, and the evolution of Citrix.
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