Blog Post 5: The Road Ahead – Preparing Your X++ Code for an AI World

Blog Post 5: The Road Ahead – Preparing Your X++ Code for an AI World

Introduction

We have journeyed through the immediate benefits of Copilot in D365 Finance & Operations—from generative help to automated collections. But for those of us who live in Visual Studio and write X++, a bigger question looms: How does this change our job?

In this final post of our series, we look at the future of development in D365 and how you can start writing code today that is ready for the AI of tomorrow.

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Caption: The path forward for Dynamics developers.

The Shift: From Logic to Context

Historically, X++ developers focused on writing complex business logic. While that is still important, the new paradigm requires us to focus on Context and Metadata.

Why? Because AI (like Copilot) doesn't read your raw X++ code classes; it reads the metadata that describes your data.

1. Data Entities are King: If you want Copilot to query your custom tables, you must expose them via Public Data Entities.

2. Labels Matter: "Field1" and "Field2" won't cut it anymore. Meaningful labels and help text are now the "prompts" that tell the AI what your data means.

3. Clean Core Strategy: The more you customize the standard application, the harder it is for standard AI models to function correctly. Extending via events and avoiding intrusive customization is more critical than ever.

The Developer’s New Toolbelt

Microsoft is not just putting AI in the app; they are putting it in our tools.

• GitHub Copilot for X++: It is becoming increasingly capable of understanding X++ syntax, helping generate boilerplate code, write test classes, and explain complex legacy code.

• Power Platform Integration: The future of "customization" is often a Power App or Power Automate flow wrapped around D365 logic. Developers need to be comfortable stepping outside of Visual Studio.

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Caption: Writing code that serves as building blocks for AI.

Preparing for the Future

To stay relevant, start doing these three things today:

1. Refactor for Readability: Write clean, modular code. AI tools can better optimize and test code that is well-structured.

2. Embrace Dataverse: Ensure your key tables are synced to Dataverse via Dual-write or Virtual Entities. This is often the bridge AI uses to access your data.

3. Learn the "Why": Don't just take requirements at face value. Ask why a user needs a specific report. Often, an AI query or a standard workspace is a better, more maintainable solution than a custom SSRS report.

Conclusion to the Series

The AI revolution in ERP isn't coming; it's already here. From the Generative Help that trains our users, to the Collections Coordinator that secures our revenue, and the Natural Language Queries that democratize our data.

As developers, we are the architects of this new reality. By embracing these tools and shifting our mindset, we don't just "keep up"—we lead the way.

Thank you for joining me on this 5-part journey into the world of Copilot in D365 FO! 

Post 4: Just Ask – Unlocking Insights with Natural Language Query

Post 4: Just Ask – Unlocking Insights with Natural Language Query 

Introduction

For decades, getting answers from an ERP system meant one of two things: mastering complex grid filters (Advanced Filter/Sort) or waiting days for a developer to build a custom report. If you wanted to know "Who are my top customers in California?", you had to know the exact field names and logic to get the list.

In Part 4 of our AI series, we look at Natural Language Query, a feature that democratizes data access by letting you simply ask for what you need.

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Caption: No code, no filters—just plain English.

What is Natural Language Query?

This feature allows users to interact with data grids and lists using everyday language. Powered by Copilot, D365 Finance & Operations translates your plain English questions into the complex query syntax the system understands.

Instead of clicking "Filter" -> "Add Condition" -> "Field: Status" -> "Value: Open," you simply type: "Show me open orders."

Examples of What You Can Ask

The flexibility is impressive. Here are a few ways users are utilizing this today:

• Sales: "Show me sales orders from last month where the total is greater than $10,000."

• Inventory: "List all items with low stock levels."

• Finance: "Display vendors with payment terms of Net 45."

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Caption: The system dynamically applies filters based on your question.

Why This Matters

1. Speed: Ad-hoc analysis becomes instantaneous. You don't need to export to Excel just to filter data.

2. Accessibility: Managers who aren't "power users" of D365 can still get their own answers without relying on IT.

3. Discovery: It helps users find data even if they don't know the exact technical name of the column (e.g., typing "Client" might correctly search the "Customer Name" field).

A Note for Developers

You might be wondering: "Does this bypass security?" The answer is no. Copilot respects all existing security roles and Row Level Security (RLS). If a user doesn't have permission to see a specific legal entity or data field, Natural Language Query won't show it to them.

However, as developers, we need to ensure our Table and Field labels are clear. Copilot relies on meaningful metadata to understand that "Qty" means "Quantity." 

The AI Revolution in ERP – Meeting Copilot in D365 Finance & Operations

 Introduction

The world of Enterprise Resource Planning (ERP) is undergoing a massive shift. For years, we’ve relied on manual data entry, complex grids, and rigid reporting. But with Microsoft's introduction of Copilot in Dynamics 365 Finance & Operations, the game has changed. This isn't just a chatbot; it's an AI assistant deeply embedded into your daily workflows.

In this first post of our 5-part series, we will explore what Copilot actually is within the D365 FO ecosystem and why it matters for developers and end-users alike.

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Caption: AI meets ERP: The new standard for business operations.

What is Copilot in D365 FO?

Copilot is an AI-powered assistant that leverages Large Language Models (LLMs) to interact with your data. Unlike traditional automation that follows strict "If/Then" rules, Copilot understands natural language.

It allows users to:

• Ask questions about their data in plain English.

• Draft content like collection letters or emails automatically.

• Analyze trends without needing to build a complex Power BI report from scratch.

Key Capabilities at a Glance

Feature	Description	Benefit
Generative Help	Context-aware guidance inside the app.	Reduces training time for new users.
Collections Coordinator	AI-generated summaries of customer accounts.	Speeds up cash flow recovery.
Natural Language Query	"Show me sales from last week."	Removes the need for complex filtering knowledge.

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Caption: The Copilot sidecar providing instant account summaries.

Why Should Developers Care?

For us developers (specifically X++ engineers), this changes how we think about customization. We are moving away from building rigid reports and toward enabling data entities that AI can understand. The future of development involves configuring Copilot to ensure it accesses the right data securely.