Copilot Studio: a guide for Dutch companies (2026)

Microsoft Copilot Studio: the Power Platform offering for agents

Copilot Studio is Microsoft's answer to the AI agent trend, launched in 2024. For organisations already running fully on Microsoft 365 (M365, Teams, SharePoint, Power Platform), it is the most obvious route to build agents without first hiring a dev team. For everyone else it is usually the wrong choice. This article explains what it can do, what it costs and when you are better off picking a different route. No sales pitch, just how it works.

What is Copilot Studio?

Copilot Studio is a low-code platform inside Microsoft Power Platform that lets you build AI agents without a traditional programming language. The agents run on GPT-4 and GPT-4o, connect to your M365 data through Microsoft Graph, and can be deployed in Teams, on websites, in Power Apps or as a telephony bot. Microsoft positions it plainly: an agent in a day, not in a quarter.

For functional SME use cases that actually holds up. An HR bot that answers leave-policy questions based on the staff handbook in SharePoint. A sales bot that generates quotes from Dynamics 365. An IT helpdesk agent that reads out ticket categories and routes them onward. Without needing Python, your own infrastructure, or external vendors.

For organisations already running fully on Microsoft 365, Copilot Studio is the obvious route. For everyone else it is usually the wrong choice.

What can Copilot Studio do?

Copilot Studio is strong at four things.

First: fast rollout. An agent is operational within days, not weeks. The drag-and-drop conversation designer, combined with 250+ ready-made connectors, makes prototyping extremely fast. For a proof of concept inside a Microsoft stack it is unmatched.

Second: ready-made M365 integration. SharePoint, OneDrive, Outlook, Teams, Excel and Power BI come along without you having to manage API tokens. For RAG (retrieval-augmented generation) on your own documents, configuration is three clicks instead of three weeks of building. That is not a marketing claim, that is how it actually works.

Third: built-in governance. The Power Platform Admin Center gives IT control over who builds agents, which data they can see and how long transcripts are kept. For companies with DLP and compliance requirements that saves months of setup time.

Fourth: default settings that take the AI Act into account. Microsoft documents per agent which models are used, which data flows out, and delivers logging out of the box. With the right configuration you are often directly AI Act compliant. For the broader AI Act context see /ai-act.

What Copilot Studio does not do well

Four pitfalls to know in advance, because they only become clear once you have already committed.

Paying per message gets expensive at volume. The pricing model charges per "message". A plain question is one message, a question with a tool call (a SharePoint search, for instance) counts as two or three, a complex multi-step answer five to ten. At high volumes (customer service with 10,000+ interactions per month) the bill climbs to amounts where a custom solution on Anthropic or OpenAI is five to ten times cheaper.

There is no on-premise or EU-only option without contortions in Azure. Data leaves the M365 tenant for Microsoft AI services. For clients with strict GDPR requirements (sectors like healthcare, law, or defence) that is often a dealbreaker, or it forces Azure OpenAI Service with a private endpoint and a separate deployment pipeline. At that point you are effectively already building a custom agent, just via a detour.

The capabilities as a self-directed AI agent are limited. Copilot Studio agents are primarily conversational with workflow actions. True self-directed AI (being given a goal, drafting a plan, selecting tools and adjusting the plan when something fails) is possible via Custom Agents, but still limited compared to frameworks like LangGraph or a Python loop of your own. For multi-step planning with unknown inputs, custom often works better. See /ai-agents for the full approach to AI agents.

And there is lock-in on Power Platform. An agent you build in Copilot Studio only runs inside Power Platform. Migrating to another platform or your own stack means: rebuilding.

The pricing model explained

Per message, not per user. That distinction is important.

Microsoft applies (as of early 2026) a base price of roughly 200 euro per tenant per month for 25,000 messages, with add-on costs of a few euro cents per message above that. For an agent handling 1,000 real user interactions per day (a small customer service team), that climbs to 60,000 to 100,000 messages per month: about 500 to 1,000 euro in licence costs. The same usage volume for a custom implementation with Anthropic Claude or GPT-4 typically costs 100 to 300 euro in API tokens. The difference sits in the added value from Microsoft (governance, M365 integrations, low-code), not in the raw model cost.

For functional bots with 50 to 500 messages per month (internal FAQ, bots that look up information on request), Copilot Studio is genuinely competitive and the overhead is negligible. The break point sits somewhere around a few thousand messages per day. Above that, you need to run the numbers.

When is Copilot Studio the right choice?

Four situations where the advice is clear.

If your organisation already runs fully on M365, the added value is maximal. Identity (Entra ID), files (SharePoint), email (Outlook), chat (Teams), processes (Power Automate), data (Dataverse): a ready-integrated agent uses all of that directly. A stack of your own would have to rebuild those same integrations, and that takes weeks that add nothing to your use case.

If the use case is functional and stable, it also fits well. Question-and-answer on top of your own documents. A workflow trigger based on a question. Categorising and routing a ticket. Nothing involving dynamic multi-step planning or elaborate judgment.

If your IT team is choosing low-code and has no dev team of its own, Power Platform users, BI analysts and process owners can build agents themselves. For scaling AI implementation across the organisation, that is a major advantage.

And if the volumes stay manageable. Up to a few tens of thousands of messages per month, the cost-benefit ratio is favourable. Above that, it pays to run the numbers on alternatives before you are locked in.

When is a custom agent smarter?

Four situations where a custom alternative fits better.

At high volumes with predictable behaviour. Customer service with 50,000+ interactions per month on a well-defined domain runs better on a direct LLM API with a RAG stack of your own. Five to ten times lower TCO over three years, and you keep control over your own prompts and data.

With strict EU-only or on-premise requirements. Sectors like healthcare, law, and finance with GDPR-sensitive data. Custom on Azure OpenAI with a private endpoint, or an open model (Llama, Mistral) on your own infrastructure, fits better than the default cloud model behind Copilot Studio.

With complex AI that plans tasks in steps on its own. An agent that has to break down a process, plan subtasks, select tools dynamically and pick a new approach when something fails. LangGraph or a custom build in Python with Claude or GPT as reasoning engine gives you far more control than the Copilot Studio flow designer.

With voice agents as a core capability. For voice-first use cases (call centres, intake bots), ElevenLabs, Vapi and Retell are far ahead of the Copilot Studio voice channel. A native voice engine plus a custom flow delivers better voice quality with lower latency and more flexibility.

Hybrid: two tracks side by side

In practice many clients run hybrid. Copilot Studio for internal functional bots (bots for HR FAQs, IT helpdesks, or sales pricing) where the M365 integration is worth its weight in gold. Custom agents for customer-facing voice or high-volume use cases where TCO and flexibility are decisive. One tenant, two kinds of agents, one governance layer. Not sexy, but workable.

DataDream builds both. DataDream is tool-agnostic: per use case, DataDream weighs whether Copilot Studio, a custom agent on Vapi, Retell or ElevenLabs, or a purely API-driven Python implementation gives the best value for money. No partner lock-in, no biased advice. For RPA replacement see /rpa.

How do you start?

Three steps for organisations weighing Copilot Studio.

Step one: inventory the use cases. Not "we want to start using Copilot Studio", but "our HR team gets 200 questions per month about leave, pay, and sick days". Per use case: what is the volume, how stable is the input, which data is needed and which compliance requirements apply? A free Quickscan via /ai-scan maps this out in an hour.

Step two: test small first. A bounded use case, two to four weeks in a Power Platform tenant, with a limited user group. Measure what it delivers in time or quality, and what it costs in messages. Compare with a custom alternative at the same usage volume. Without data, the advice is a guess.

Step three: scale up or switch. If the pilot scores well on both value and cost, you scale to the next use case. If it proves too expensive, limited, or slow, you rebuild the same use case as a custom agent without having tied the organisation to the wrong platform.

For strategic advice on the stack choice (Copilot Studio versus custom versus hybrid) see /ai-strategie. For pure custom agents see /ai-agents. Want me to look at which route fits your use case? Book a free call.