Why Copilot often fails to move the business forward
Useful for taking notes or drafting proposals faster, but as soon as you want AI to truly collaborate within your workflow, you hit a wall. Why? Because AI projects in Copilot that go beyond simple efficiency gains are often, and rightly, led by IT. More value emerges when the business itself takes the lead, using AI agents that combine domain expertise, model flexibility, and seamless integration with data and tools.
3 okt 2025
3 minutes
Radboud Langenhorst
Where Copilot does help
The bridge Copilot cannot cross
Why waiting is not an option
Copilot is too often seen as the AI savior in the boardroom. It’s familiar, built into trusted software, and easy to start with. That’s exactly why I often hear: “Why doesn’t AI Opener build its agents inside Copilot?”
The answer has several layers, but the most important one is this: real business value from AI only happens when the business leads, not IT.
With simple actions and prompts, marketers can quickly gain efficiency using Copilot: generating meeting notes, drafting proposals faster, or speeding up standard content. Copilot also offers a gallery of ready-made examples and templates that can make your AI work easier. It is a helpful source of inspiration and a good starting point, but not a full solution for structuring your business workflows.
Once you want to scale up to autonomous AI agents that are truly integrated into your marketing stack, think Monday, Hootsuite, campaign managers or Adobe, things often start to break down. That is when you need IT. And IT usually faces priorities, silos and long timelines, while the business and the rest of your organization need speed and direct impact.
Even when IT has time and the data is well-organized, one crucial element is still missing: translating domain expertise into AI behavior. You cannot solve that with Copilot, not with IT, and not with a few prompt experts.
A strong AI agent is built together with the business so you can:
Capture domain expertise and nuances in the system prompt.
Add context through RAG or RAC and define logic: creative or strict, with built-in compliance and quality control.
Connect more sources to make the output richer, more consistent, and more valuable.
A practical example:
At a mid-sized FMCG company (meat alternatives), the marketing team across six countries could barely handle more than product launches, discount campaigns, and PoS materials. Once we properly set up the domain expertise and context, we could add many more data sources. From R&D input and competitor analysis to cultural cooking habits and changing regulations. The result was content that wasn’t just faster and more consistent, but also far richer and more relevant.
Doing this well requires time and involvement from the business and from AI experts within that business.
The second argument is that the AI puzzle is far from complete.
The market is moving incredibly fast. Microsoft itself demonstrates this: fully betting on OpenAI today, adding Claude tomorrow, and developing its own models the next. That is not a sign of confidence, but of uncertainty and internal debate.
Copilot makes the choices for you, within the Microsoft suite. It sounds convenient, but it means you are tied to the decisions and limitations of a single provider. The business has no influence over which model is used for which task.
A model-agnostic agent platform works differently:
Dynamically selects the right model for each task.
Can switch or combine models as soon as something new or better becomes available.
Balances quality and cost, using premium models only where needed and efficient ones where possible.
That is not theoretical luxury, it is business logic. Why run everything on an expensive premium tool when 70% of your work can be done just as well by a more efficient alternative?
A common counterargument is: “In the long run, Microsoft will get there, and at least you won’t need another tool.” But what is “the long run” anymore? Five years, like it used to be? In AI terms, that’s an eternity.
The reality: companies that are deploying business-driven agents today can already:
Rewrite product portfolios for better discoverability
Enter new markets faster
Add hyper-personalization to campaigns
Work more efficiently across departments
Optimize their value propositions
And much more..
So the real questions are: who takes the lead? How fast do you want to move? And how long can you keep your team motivated while you’re still coloring inside the lines?