Software development has been around since the 1940s.We started with punch cards, then machine language, followed by assembly, high-level programming languages, low code, no code, and now AI-assisted coding. Along the way, several tools have been developed to make programmers’ jobs easier, from card sorters and verifiers to debuggers and IDEs. Now, with the advent of AI, we have large language models (LLMs) writing code for us, but I don’t think it’s quite there yet. In this article we’ll see how AI assists developers, what it can do for us today, its limitations, and where it’s headed. The concept of AI began in the 1950s when researchers tried to imbue machines with the magic to think. Early systems followed set rules, but as computers improved and data became more available, smarter methods emerged, such as machine learning, natural language processing, and neural networks. Large Language Models (LLMs) grew from these advances, using huge amounts of data and computing power to understand and create language. This marked a shift from fixed rules to models that learn on their own. By 2025, AI has taken root in most fields, even in places we might not have expected.For example, robotic bees — tiny drones designed to mimic bee behavior, are now being used to assist with pollination in areas where natural bee populations are struggling. These drones combine machine learning and computer vision for navigation, flight control, pollination strategies, and swarm intelligence. Usage Copilot is integrated with both Visual Studio Code and Visual Studio, and it comes with a few LLMs built in by default.Currently, these include Claude Sonnet 3.5, GPT-4o, o3-mini, and Gemini Flash 2.0.If you want to add more models, you’ll need a subscription for Copilot Pro. We can use Copilot Chat to prompt these models directly in the sidebar chat, whether to generate a specific functionality or create an entirely new file. Here, I asked it to create a simple sales order.Notably, it kept the key details — Customer, Item, and Quantity — as parameters without requiring any input. From here, we can click a button to apply the changes to the open file. At the bottom, we can see which file Copilot is currently using as a reference.If we want to stop Copilot from referencing that file, we can click the eye button. We can also ask it to make changes to the generated code. Now, I noticed that while it has parameterized the “Customer No.” for the sales order, it hasn’t actually used it anywhere in the code. If I point this out to Copilot… Instead of using Copilot Chat, we can also get recommendations directly within the file.Here, I’m trying to write a function to delete a sales order based on the given SO No. I can just tab my way into writing the method. One common way I’ve used copilot is to add Guard clauses to methods that I’ve written. For instance –  Here, it is referring to Customer and Item record variables, which don’t exist yet. But if I go to the variables section then it knows what I’m trying to do and suggests the same. Now, if I were to make it handle something complex, that’s when the cracks start to show. For example, pulling data from an API and creating customers would require several steps — authenticating with the API, fetching the data, parsing it, handling errors or logging, and finally creating the customers. We get the following as an output – Here, we can see that while it has a surface-level understanding of the code structure and the steps needed to achieve the goal, it struggles with the details. This could be because, unlike open-source languages like Java, Python, or C++, there isn’t as much publicly available source code for AL. I believe Microsoft Documentation would have helped to some degree, but instead, it tends to guess what the correct methods or fields should be. To its credit, the generated code isn’t far off from being functional, especially considering the simplicity of the input prompt. The structure it provides is still a solid starting point and much better than writing everything from scratch. Another example of these “hallucinations” is when it suggests methods that don’t actually exist, like this- However, once you show it what the correct method is, it suggests that –  To go one step further, I asked the different models to create an entire project based on the below prompt –  Findings: o3-mini 1. The objects it generated had the fewest errors.2. It was the simplest and closest to compiling successfully.3. It returned all the text in a single response, so I had to manually create files from it. GPT-4o 1. Created a Readme.md with project requirement details.2. Automatically generated the necessary project files.3. Farthest from compiling successfully, with most requirements missed.4. There were plenty of hallucinations, including methods that don’t exist in AL at all – like this example below. Gemini Flash 2.0 1. Created a Readme.md with project requirement details.2. Automatically generated the necessary project files.3. Added launch.json, settings.json, and app.json.4. Didn’t meet all requirements but managed to lay some groundwork.5. Struggled with code structure in several places, though still significantly better than GPT-4o.6. Had at least a couple of pages with zero errors. Claude Sonnet 3.5 1. Created a Readme.md with project requirement details.2. Automatically generated the necessary project files.3. Added launch.json and app.json.4. Included a test codeunit, though it had errors.5. Created a permission set for the objects generated.6. All files had one or more errors. In my opinion, Claude and o3-mini are the most useful for coding assistance. HumanEval is a test developed by OpenAI to assess how well language models can write code.It includes 164 programming problems where the model must generate accurate and functional Python code. The HumanEval leaderboard aligns with my assessment as well. Pricing While all these models offer a free trial with a limited set of tokens, they can become quite expensive if you don’t monitor your usage. Below … Continue reading Transforming Development: How Copilot is Revolutionizing Developer Productivity →

Microsoft is rapidly integrating Copilot across its ecosystem, empowering users with AI-driven assistance in various business processes. As enterprise systems become more connected, AI gains deeper access to data, enabling automation that eliminates tedious tasks and lets users focus on strategic decisions. In Dynamics 365, Copilot can help sales teams by generating Sales Quote Lines or Sales Order Lines by providing a rough prompt.  In this blog, we’ll explore how to leverage Copilot for a more efficient sales workflow by taking a sample use case. References Copilot in Business Central Overview Sales Line Suggestions with Copilot Scenario One fine morning your sales team receives an email from a customer who’s looking to try out your product. He sends your team an email. Your team goes to Business Central and creates a Sales Quote. In the lines section, they click on the Copilot button and click on “Suggest lines”. They can add the text the customer sent them directly or with some minor changes. And Copilot will find the best matching item and suggest some lines to the User. You can adjust the matching criteria to your required –  Permissive means that all keywords are optional. This option typically generates the most suggestions. Balanced is a blend of required and optional keywords. This option typically generates fewer suggestions. Precise means that all keywords are required. This option typically generates the fewest suggestions. Fast-foward a few days, the Customer is happy with your product and sends a bigger order. We can paste the entire description again into the suggest lines box. Copilot handles minor mistakes like spelling errors and mismatched totals without any intervention. And your new sales quote is ready! The accuracy of sales lines suggested by Copilot rely heavily on the quality of data present in the system. I’m not sure why Microsoft hasn’t included the same functionality for the Purchase side of things but I’m sure it’s not too far off in the future.  There’s already a BC Idea raised for this (Please vote it!).  If you need further assistance or have specific questions about your ERP setup, feel free to reach out for personalized guidance. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.

Introduction PowerApps continues to evolve with new features that simplify formula creation and make app development more accessible for everyone. The recent updates bring innovative tools like natural language-based Power Fx formula generation and enhanced formula explanations. In this blog, we’ll explore these new features and provide actionable tips and tricks to help you leverage them effectively in your apps. 1. Generate Power Fx Formulas Using Natural Language One of the standout updates is the ability to create Power Fx formulas using natural language instructions. This feature is perfect for both beginners and experienced developers looking to save time. How It Works: Practical Tip: Use natural language for complex formulas that are hard to write manually, such as: This approach accelerates formula creation, reduces errors, and lowers the learning curve for new users. 2. Enhanced Formula Explanation for Better Understanding Have you ever been puzzled by a long or intricate formula? The enhanced formula explanation feature can help by providing plain language explanations for selected parts of a formula. How It Works: Practical Tip: 3. Multi-Language Support in Formula Generation With the growing global adoption of PowerApps, formula generation now supports multiple languages. This feature ensures that users can work comfortably in their preferred language. How It Works: Practical Tip: Use this feature when collaborating with teams across regions. It allows contributors to describe actions in their native language, making formula generation inclusive and efficient. 4. Speed Up App Development with AI Assistance AI-based suggestions in the formula bar aren’t just for natural language inputs. They can help optimize existing formulas and suggest best practices as you build. How It Works: Practical Tip: Examples below Hope this helps Conclusion The latest PowerApps formula updates are game changers for app developers. From generating formulas with natural language to debugging them with enhanced explanations, these features simplify app development and make PowerApps more accessible to users of all skill levels. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.