This post is part of our Microsoft Copilot, Explained series, which starts with everyday productivity tools like email and meetings before expanding into broader workflows and AI agents.

“Our data isn’t ready.”

It’s one of the most common reasons teams delay adopting AI. The concern makes sense. For years, business systems broke down when data was inconsistent. CRM records with missing fields, spreadsheets with merged cells, and email threads without clear subject lines were not merely inconveniences; they were blockers.

So when AI entered the conversation, many teams assumed the same rules applied:
Clean up the data first. Standardize everything. Fix the gaps. Then maybe consider AI.

But here’s what’s changed: AI doesn’t work like those older systems. It doesn’t require perfection to be useful. And waiting for perfect data often means never starting at all.

Why “Perfect Data” Feels Like a Prerequisite

The instinct to clean up first isn’t unreasonable. It’s a learned behavior.

Traditional business systems like ERP, CRM, reporting tools were built on rigid structures. They depended on exact field matches, consistent formats, and predictable inputs. When data was messy, reports failed. Automations broke. Workflows stalled.

That experience taught teams a logical lesson: get the data right before you build on it.

AI works differently.

Instead of requiring rigid structure, AI interprets context and patterns. It’s designed to handle variety, like unstructured text, mixed formats, incomplete information, and still deliver value.

That shift matters more than it might sound at first. It means the barrier to entry in a meaningful way.

You don’t need perfect data to start with AI. You need enough context for AI to be useful. And in most cases, you already have that.

How Copilot Works with Real-World Data

Why AI Doesn’t Require Perfect Data

One reason teams hesitate is uncertainty about what “enough context” actually means. The good news is that Copilot is designed to work with the data you already have, even when it’s not perfectly organized.

How Copilot Handles Different Data Types

Copilot uses generative AI, which means it can interpret different types of data:

• Unstructured text like emails and meeting notes
• Numbers in spreadsheets and reports
• Images such as receipts or screenshots
• Mixed formats where all of those appear together

Unlike older systems that required everything to match a specific structure, AI looks for meaning and patterns. It adapts to how your team actually works, not how a system assumes you should work.

With Copilot, Your Data Stays in Your Tenant

Copilot also operates inside your Microsoft 365 tenant. It uses your data, your permissions, and your organizational context not generic content from the internet. This is what Microsoft means by calling Copilot “your AI assistant for work.” It’s tuned to your environment, not trained on everyone else’s.

From a security and privacy perspective, Copilot works within your tenant’s existing controls. Your data stays inside your organization’s boundaries, governed by the same policies already in place.

The shift from “structured data required” to “AI interprets context” is what makes starting possible, even when your data isn’t perfect.

What You Learn by Starting to Us Copilot for AI

When teams begin using Copilot in Outlook, Teams, Word, or Excel, something unexpected happens. They don’t just get faster at email or meetings. They learn how to work with AI.

At first, it’s trial and error. You ask Copilot to summarize a thread or draft a response. Sometimes it’s spot on. Sometimes it’s close, but needs refinement. Over time, you learn what context helps, how to phrase requests, and where human judgment still matters.

That back-and-forth teaches you something deeper: how AI thinks about workflows.

Teams start to recognize that AI isn’t just responding to prompts; it’s identifying patterns, connecting context, and reflecting the workflows they already follow. The more they use it, the more confident they become experimenting.

The practice itself builds confidence. You start to recognize AI as a tool you can shape, not a black box that either works or doesn’t.

And that confidence matters, because it carries forward.

From Copilot to AI Agents in Business Applications

Once teams are comfortable using Copilot in Microsoft 365, many start asking a natural next question: Can AI do this in our business applications too?

The answer is yes, and it works on the same principle.

What Are AI Agents in Dynamics 365?

AI agents are built to handle workflows inside business systems like Dynamics 365 Business Central, Dynamics 365 Sales, and other ERP or CRM platforms. Where Copilot in Microsoft 365 helps you draft, summarize, and analyze, agents take things a step further. They automate the repetitive back-and-forth you used to handle manually, such as routing approvals, flagging exceptions, pulling context from multiple sources, and preparing decisions for review.

By the time agents show up, you’ve already learned how AI interprets workflows. You recognize agents as automated versions of the prompting and pointing you were doing yourself in Microsoft 365.

How AI Agents Work Without Perfect Data

Microsoft has built out-of-the-box agents for common workflows in Dynamics 365 applications. These are called AI agents, and they follow the same design philosophy as Copilot in Microsoft 365. They’re built to work with real-world data, not wait for perfection.

How Agents Work Without Perfect Data

Take the Expense agent in Business Central as an example.

When a receipt image comes in, the Expense agent extracts key details: vendor, amount, date, category. It matches the expense to company policies, checks for exceptions, and routes it for approval. It doesn’t need perfectly formatted receipts or flawless vendor records to function. It works with the information available, flags what’s unclear, and moves the process forward.

Similar agents exist for Sales Order processing and Bank Reconciliation each designed to handle the repetitive, time-consuming parts of workflows that teams know well but would rather not do manually.

The principle is the same across all of them: AI adapts to context, not rigid structure. And because you’ve already practiced working with AI in Microsoft 365, the transition to agents feels less like a leap and more like a natural expansion.

Want to see how the Expense agent works in practice? We’re hosting a live demo in March where we’ll walk through real workflows in Business Central.

What “Ready” Actually Means

So what does “ready” look like if perfect data isn’t the threshold? It’s simpler than most teams expect.

You’re ready if:

• You’re already working in tools like email, meetings, documents, and spreadsheets, or handling repetitive workflows in business applications
• You’re open to experimenting and learning as you go
• You’re willing to start small and let value build over time

That’s it.

You don’t need a formal AI strategy. You don’t need a dedicated AI team or a multi-phase implementation roadmap. You don’t need to wait until every field is filled, every naming convention is standardized, or every process is documented.

You need curiosity and a willingness to start small.

• For teams already using Microsoft 365, that often means enabling Copilot in Outlook, Teams, or Excel and seeing where it helps first.
• For teams using Dynamics 365, it might mean exploring AI agents in Business Central or Sales.

And for teams evaluating platforms? The principle is the same: AI works best when it’s embedded in the tools your team already relies on — not bolted on as a separate project.

The teams seeing value from AI aren’t the ones who waited for everything to be perfect. They’re the ones who started where they were, with the tools they already use, and built confidence one workflow at a time.

Start Where You Are

Waiting for perfect data often means never starting at all.

AI, including Microsoft Copilot and the agents built into Dynamics 365, is designed to work with the complexity, variety, and imperfection that come with real work. It doesn’t require you to transform your data before you begin. It requires you to begin, and let capability grow from there.

The practice builds confidence. The confidence opens up new possibilities. And the possibilities compound over time.

AI doesn’t require perfect data to start, but starting is often what helps you see how capable AI already is.