Intune Advanced Analytics: How It Compares to Other Tools — cover image showing the two device query channels flow on a laptop, with Jannik Reinhard (Microsoft MVP) and jannikreinhard.com

Intune Advanced Analytics: How It Compares to Other Tools

Intune Advanced Analytics: How It Compares to Other Tools

In this blog post I want to look at Microsoft Intune Advanced Analytics and compare it, in plain words, to the other analytics tools that are out there. This is the topic I know well. Before I started writing blogs and running my own company, I spent years as the tech lead for AIOps in a large enterprise. Part of my job was to evaluate analytics and digital employee experience (DEX) platforms — Nexthink, Aternity, HP’s analytics solution and several more. So this is not a marketing piece. It is what I learned from running these tools at scale, and where I think Microsoft’s approach is genuinely different.

Here is my honest summary up front: most of these platforms cook with water. They are mature and capable, but they largely solve the same problems in the same way. The hard part was never the dashboard — it was building a business case that survived a second look, because every one of them came with its own agent, its own data store, its own portal and its own license. That is exactly the cost that Microsoft Intune Advanced Analytics removes.

Worth knowing before you read on: From July 1, 2026, Microsoft Intune Advanced Analytics is included in Microsoft 365 E3 and Microsoft 365 E5 as part of Microsoft Intune Plan 2. The separate add-on that used to cost around 10 USD per user per month is now part of the plan. Many teams already own this and don’t know it yet.

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Intune Policy Manager AI-powered policy descriptions and conflict analysis dashboard.

AI-Powered Intune Policy Documentation and Conflict Analysis

AI-Powered Intune Policy Documentation and Conflict Analysis

If you manage Microsoft Intune at scale, you know the pain: hundreds of policies, most of them with empty or outdated descriptions, and zero visibility into which settings overlap or even contradict each other across policies. I’ve seen this in pretty much every tenant I’ve worked with and honestly, it’s one of the most underestimated operational risks in modern endpoint management. This is where AI-powered Intune policy documentation and conflict analysis comes in.

So I built a tool to fix it. It builds on the same idea I explored in Create your own Intune Co Pilot using Azure OpenAi Studio, but takes it further with automated Intune policy documentation and conflict analysis. Let me walk you through it.

Intune policy documentation tool showing conflict analysis dashboard
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CLI Tools vs MCP: Better AI Agents With Less Context

CLI Tools vs MCP: Better AI Agents With Less Context

CLI Tools vs MCP: Better AI Agents With Less Context

Let’s be honest: MCP (Model Context Protocol) was supposed to be the universal connector between AI models and the real world. A clean, structured protocol that lets your AI agent talk to any tool through a standardized interface. Sounds great in theory. In practice? I’m increasingly reaching for good old CLI tools instead — and I’m not alone.

After months of building AI agent solutions and working with both approaches in real-world enterprise scenarios, here’s my take: CLI tools are the better choice in many cases, and the reason is surprisingly simple — context efficiency. Microsoft’s own guidance on CLI tools like the Azure CLI reflects how mature this tooling has become.

CLI tools for AI agents shown in a terminal window next to MCP
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Build a Microsoft Intune AI Agent with Foundry

Build a Microsoft Intune AI Agent with Foundry

Build a Microsoft Intune AI Agent with Foundry

We’ve all built PowerShell scripts to query Intune, wrapped them in some automation, and called it a day. It works. But with Azure OpenAI Service and models like GPT-4.1 and GPT-5.2 optimized for tool calling, there’s a more interesting approach—building an actual Intune agent that can talk to your Intune environment in plain language.

Instead of writing a script for every query, you build one Intune agent that understands natural language and calls the Graph API on your behalf. Ask it “which Windows devices are non-compliant?” and it figures out the right API call, executes it, and summarizes the results. It’s not magic—it’s function calling with a nice interface.

In this post, I’ll walk you through two different approaches to building this Intune agent: the classic direct SDK approach and the newer Microsoft Agent Framework. Both use the same underlying Graph API client, but differ in how they orchestrate the AI. Let’s dive in.

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Convert Intune Device Groups to User Groups via Graph API

Convert Intune Device Groups to User Groups via Graph API

Convert Intune Device Groups to User Groups via Graph API

I currently attend at the MMS Fort Lauderdale conference, where an attendee asked a good question: Is it possible to convert device groups to user groups, and vice versa? The answer is both yes and no. While there’s no out-of-the-box functionality in Intune to turn device groups to user groups directly, it is possible by leveraging the Microsoft Graph API.

Diagram showing how to convert device groups to user groups with the Microsoft Graph API
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Graph Batch Endpoint

Graph Batch Endpoint

Graph Batch Endpoint

This is only a small blog post but maybe for most of you very helpful, especially if you work a lot with Microsoft Graph. Often the problem is you want to run multiple calls and then you have to loop through the single items or have a long line of calls. The Microsoft Graph Batch Endpoint solves exactly this by letting you combine many requests into one single HTTP call.

While writing another blog post, I found out that there is a batch endpoint for MS Graph. In this blog, I will show you how you can use the Graph Batch Endpoint and give you also an example script that you can adapt for your own automations.

Graph Batch Endpoint overview diagram
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IME Log summarizer

IME Log Summarizer for Microsoft Intune

IME Log Summarizer for Microsoft Intune

In this short blog I want to show you how you can build an IME Log Summarizer that uses GPT to get a summarization of the Intune Management Extension log. This IME Log Summarizer script will read the Intune Management Extension Log file in the ProgramData/Microsoft/IntuneManagementExtension/Logs folder and will pass the latest content of the log to GPT, so you spend less time scrolling through thousands of noisy log lines.

Troubleshooting app and policy delivery on Windows endpoints almost always comes back to this one log file, and reading it by hand is slow. An IME Log Summarizer turns that raw, verbose log into a short, readable report that highlights what actually went wrong on the device. If you already manage devices with Microsoft Intune, this is an easy win you can set up in minutes.

IME Log Summarizer local workflow for Intune Management Extension logs
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Build a No-Code Intune Copilot in Azure OpenAI Studio

Build a No-Code Intune Copilot in Azure OpenAI Studio

Build a No-Code Intune Copilot in Azure OpenAI Studio

This post walks you through how to build a no-code Intune Copilot in Azure OpenAI Studio — using only the Azure portal, your Intune tenant, and a Microsoft Graph app registration. By the end you will have a working chat interface grounded on Intune device data, deployable to Teams or as a custom web app.

The simplest approach to deploying a chatbot is to do so without any coding. If you want to take this idea further into a more advanced agent scenario, you can also check out how to build a Microsoft Intune AI Agent with Foundry. This is precisely what I aim to demonstrate. Such a chatbot can be tailored to utilize your custom documentation, knowledge articles, or any other resources you wish to integrate. This method simplifies the process, making it accessible even if you’re not well-versed in programming. By leveraging existing documents and knowledge bases, you can create a chatbot that is both informative and aligned with your specific needs and content.

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GPT Remediation creator

GPT Remediation creator

GPT Remediation creator

The GPT Remediation creator helps you tackle one of the trickiest parts of modern client management: remediations play a pivotal role in proactively identifying and resolving end-user issues. Additionally, they serve as a valuable tool for enforcing specific settings or configurations that may not be natively supported in Microsoft Intune. However, the process of crafting these scripts can often be intricate and time-consuming.

Imagine a solution where you can simply describe your desired configurations, and a tool generates the necessary scripts for you. If you find this idea appealing and are keen to explore such a solution, this blog is tailored to meet your exact needs.

GPT Remediation creator results in the web interface
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The easy way to make data science with Intune

Data Science with Microsoft Intune — Quick Start

Data Science with Microsoft Intune — Quick Start

This is a Quick Start to data science with Intune. The post walks through how to pull Intune data into a notebook, do meaningful exploratory analysis on devices, apps, and compliance, and turn the result into something an admin team can actually act on. If you have ever wanted to do real data science with Intune without building a heavy reporting pipeline, this is the fastest path I know.

As you know I like everything what is related to data science and Intune. In this blog I will show you a solution how you can get some insights about your Intune environment you did not have before. The goal is simple: take the raw signals Microsoft already collects and make them easy to understand at a glance.

The easy way to make data science with Intune
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