Choosing between Microsoft Copilot, AI Builder, and Azure OpenAI isn’t just about picking the shiniest tool—it’s about understanding how each platform fits real-world business needs in a fast-evolving Microsoft ecosystem. In this guide, you'll find a side-by-side comparison covering the core features, technical architecture, total cost, and how well each platform plays with both Microsoft and non-Microsoft environments.

For IT leaders, architects, or anyone making technology decisions, it’s critical to dig into the differences: Which tool empowers non-developers? Which offers the most control over custom AI? Where do you get the best integration, and what’s the lowest hurdle to start seeing business value? This breakdown arms you with facts, not fluff, so you can match solutions to your business goals—and avoid costly missteps down the road. Let’s set the record straight before you commit that budget.

copilot vs ai builder vs azure openai — 12 surprising facts

1. Model access vs managed copilots: Azure OpenAI provides direct access to GPT-family models (fine-tuning, embeddings, temperature control), while Copilot Studio focuses on building task-specific copilots with orchestration and UI — surprising because Copilot Studio hides model details but adds productized workflows.
2. Enterprise data handling differences: Azure OpenAI emphasizes tenant isolation and bring-your-own-encryption options for raw model calls; Copilot Studio and Azure AI Foundry emphasize integrated data connectors and governance layers — meaning governance, not model accuracy, often drives choice.
3. Customization depth: Azure OpenAI supports low-level fine-tuning and prompt engineering; Copilot Studio offers higher-level skill composition and templates; Azure AI Foundry emphasizes rapid pipeline assembly for multimodal inputs — surprising that customization can be deeper in the raw API than in the "no-code" copilot builders.
4. Multimodal capabilities: Azure OpenAI models support text and embeddings and some image features via multimodal models; Copilot Studio and Azure AI Foundry are built to orchestrate multimodal flows (text, image, documents, connectors) — surprising that orchestration platforms often enable broader multimodal apps than single-model APIs alone.
5. Compliance and certification: All three are positioned for enterprise compliance, but Azure OpenAI has specific contractual terms and region-bound deployments for advanced regulatory use; Copilot Studio integrates MS Purview and governance; Azure AI Foundry is designed to centralize compliance pipelines — surprising that compliance features can vary more than the underlying model capabilities.
6. Latency and scale trade-offs: Direct Azure OpenAI calls can be lowest-latency for simple prompts; Copilot Studio and Azure AI Foundry introduce orchestration layers (logging, retrieval, tool use) that may increase latency but add reliability and observability — surprising that richer features can cost measurable response time.
7. Tooling for retrieval-augmented generation (RAG): Azure OpenAI supplies the core model and embeddings; Copilot Studio provides built-in RAG templates and memory for copilots; Azure AI Foundry focuses on enterprise-grade vector stores and pipelines — surprising that RAG is often easier to implement via the higher-level platforms than from scratch with raw model APIs.
8. Pricing visibility: Azure OpenAI pricing maps to model compute and tokens; Copilot Studio and Azure AI Foundry often bundle platform usage, orchestration, and storage which can make costs less predictable — surprising that higher-level convenience can obscure per-call cost control.
9. Developer experience differences: Azure OpenAI caters to engineers wanting full control of prompts, tuning, and SDKs; Copilot Studio targets product builders with UI-driven copilot creation; Azure AI Foundry is oriented toward data engineers integrating ML pipelines — surprising that the same organization might need three different skill sets to fully leverage the ecosystem.
10. Extensibility and plug-ins: Copilot Studio is designed to connect to Microsoft 365, custom connectors, and actions easily; Azure OpenAI is model-centric and requires custom integration for connectors; Azure AI Foundry standardizes connectors into reusable pipelines — surprising that Copilot Studio can deliver out-of-the-box business integrations faster than raw model use.
11. Security posture for code generation: Azure OpenAI can be used to generate code but requires developer guardrails; Copilot Studio includes patterns for safe actions and governance when enabling copilots to act on behalf of users; Azure AI Foundry provides code validation and pipeline checks at scale — surprising that tooling and governance are the chief differentiator for safe code generation, not the model itself.
12. Vendor lock-in and portability: Solutions built directly on Azure OpenAI (API calls and standard formats) are more portable to other LLM providers with translation work; Copilot Studio and Azure AI Foundry often create richer app glue and integrations that increase migration cost — surprising that higher productivity platforms can increase long-term lock-in risk even as they speed delivery.

Feature Comparison of Microsoft Copilot, AI Builder, and Azure OpenAI

When you’re sizing up Copilot, AI Builder, and Azure OpenAI, think of them as three tools cut for very different jobs, each with its place on the Microsoft roster. Copilot brings AI-powered, ready-to-use help right inside apps you already rely on, like Microsoft 365 and Dynamics. AI Builder serves up codeless, drag-and-drop model building for business users, making it easy for non-developers to automate tasks or create insights within the Power Platform.

Then there’s Azure OpenAI, which hands you the raw horsepower of advanced OpenAI models—think GPT and DALL-E—offering total flexibility for developers and architects looking to build more ambitious or bleeding-edge AI solutions, either in Microsoft’s cloud or as part of larger apps. The scope, intended users, and level of required technical skill couldn’t be more different between these platforms.

What’s most important, though, is understanding which scenarios each solution is built to solve, and what it takes to get them up and running. Whether you’re chasing instant business gains, deep customization, or heavy-duty scalability, each option shines under certain conditions—and, well, stumbles under others. The sections below will unpack these distinctions in detail, helping you map platform strengths and gaps to your real-life business cases.

Feature Comparison Table for Microsoft AI Solutions

• Deployment Type:Copilot: Embedded in Microsoft 365/Dynamics for instant access, no separate infrastructure or hosting needed.
• AI Builder: Delivered as part of Power Platform with low-code/no-code access inside Power Apps and Power Automate.
• Azure OpenAI: Cloud-based API service running in Azure, requiring dedicated setup and management by IT or developers.
• AI Model Customization:Copilot: Limited; mostly relies on out-of-the-box generative AI with some minor personalization via user context.
• AI Builder: Moderate; users can train custom machine learning models with business data using guided interfaces.
• Azure OpenAI: High; full flexibility to fine-tune, prompt-engineer, and deploy your own LLMs and generative apps.
• Integration Depth:Copilot: Tight integration within Microsoft 365 and Dynamics 365; operates inside existing workflows and apps.
• AI Builder: Deeply connected to Power Platform and Power Automate, supports many Microsoft and some third-party connectors.
• Azure OpenAI: Broadest reach; can be integrated into custom apps, chatbots, or any solution with API/data connections.
• User Skill Required:Copilot: None to minimal—built for end-users, no coding knowledge needed.
• AI Builder: Low—geared for power users and business analysts, with drag-and-drop UI.
• Azure OpenAI: High—requires technical skills, development experience, and understanding of AI models.
• Scalability:Copilot: Scales automatically within Microsoft ecosystem for individual and org-wide use.
• AI Builder: Limited by Power Platform and licensing; well-suited to departmental or process automation.
• Azure OpenAI: Enterprise-grade scalability—supports large-scale deployment, custom workloads, and high-traffic apps.

Customizability and Flexibility Across the Three Platforms

Microsoft Copilot is designed for simplicity and speed, offering limited customization. Users interact with pre-built AI capabilities inside Office or Dynamics, but can’t alter the logic or underlying models. This approach keeps things straightforward but restricts advanced flexibility—what you see is what you get.

AI Builder shifts the needle, targeting business analysts and power users with codeless, no-code customization. It lets you build, train, and deploy machine learning models using a guided interface. You can tailor AI workflows to fit business processes—think document automation or sentiment analysis—without needing to write code, but there's a ceiling to complexity and scale.

Azure OpenAI gives developers the keys to the kingdom. Here, you have granular control over prompt design, model tuning (including fine-tuning), and workflow integration. Want to train on proprietary company data? Need a conversational AI that adapts on-the-fly or interconnects with third-party APIs? Azure OpenAI is built for maximum flexibility, provided you have the technical chops to harness it. The tradeoff: higher setup and maintenance demands, but sky's the limit when it comes to capability.

In short, Copilot favors out-of-the-box ease, AI Builder supports moderate custom business automation, and Azure OpenAI unleashes full developer-level flexibility for advanced scenarios.

Technical Comparison of Copilot, AI Builder, and Azure OpenAI

Diving beneath the surface, each Microsoft AI solution is powered by a distinct technical backbone. For organizations evaluating these platforms, assessing the technology stack, underlying architecture, and integration approach is crucial—not only from a performance perspective, but also to understand how well each can scale and connect with broader enterprise systems.

Microsoft Copilot thrives within the interior walls of Microsoft 365 and Dynamics, delivering AI as a service that’s tightly woven into those platforms’ architectures. AI Builder builds on the Power Platform, enabling model deployment across Power Apps and Power Automate with managed connectors and built-in governance. Meanwhile, Azure OpenAI stands on its own as a flexible, cloud-first API service built to hook into just about anything, provided you can wire it up and scale it on Azure infrastructure.

The architecture, integration depth, and supported AI models play major roles in shaping what’s possible on each platform. Below, we’ll peel back the layers and show how these underlying choices affect real-world system design, scale, and outcomes—especially for businesses looking to future-proof their AI investment.

Architecture and Integration Capabilities Explained

• Copilot:Embedded architecture—runs directly inside Microsoft 365 and Dynamics 365 apps, utilizing existing workflows and data security.
• Integrates natively with Microsoft Graph and organizational data, but limited access to external APIs or third-party apps.
• Scales organization-wide with Microsoft’s cloud infrastructure, supporting seamless deployment across hundreds or thousands of users.
• AI Builder:Built into the Power Platform, leveraging connectors for integration with SharePoint, Dataverse, Power BI, and other Microsoft tools.
• Offers managed APIs and no-code interfaces, plus the ability to automate across both Microsoft and select external systems.
• Scales well for citizen development, but can be limited by Power Platform quotas and licensing constraints.
• Azure OpenAI:Cloud-native API service on Azure, enabling integration with any app that can call web APIs—supports REST, Azure SDKs, and more.
• Scalable for enterprise with load balancing, advanced security, and global deployment options.
• Open access for integration with third-party apps, custom microservices, and hybrid cloud deployments.

Comparing AI Models and Language Processing Capabilities

Microsoft Copilot is powered by advanced generative AI models developed in partnership with OpenAI, most notably adaptations of GPT-4 for language tasks. These models are fine-tuned for business productivity, summarization, email drafting, and contextual document analysis within Microsoft 365 and Dynamics environments.

AI Builder, in contrast, utilizes a blend of Microsoft proprietary models and automated machine learning (AutoML) frameworks. Here, users can build and train models for tasks like prediction, classification, object detection, and text extraction, all from within the Power Platform. While you won’t get the raw generative power of GPT-4, the platform still covers common business workflows and repetitive tasks through guided AI.

Azure OpenAI stands out with direct access to the latest OpenAI models—GPT-4, GPT-3.5, Codex, DALL-E, and more—plus the ability to fine-tune and expand these models for domain-specific language understanding, intent recognition, and generative content. Developers can select from a model catalog, control prompt engineering, and even inject custom data or knowledge bases for advanced applications like chatbots, Q&A engines, or multi-lingual interfaces.

In summary, Copilot offers advanced but fixed generative capabilities tuned for business tasks; AI Builder gives guided, no-code model creation for structured business problems; Azure OpenAI delivers the deepest AI muscle with the broadest model catalog and customization potential—for those willing to get hands-on with the tech.

Use Case Examples for Each Platform

The true test for Microsoft Copilot, AI Builder, and Azure OpenAI isn’t just what they promise, but what businesses are actually doing with them out in the wild. That’s where you really see their strengths and limitations on parade.

For most organizations, the right solution comes down to the real roles these tools play. Some use Copilot to turbocharge day-to-day work with productivity shortcuts. Others turn to AI Builder to automate document handling and drive workflow improvements, all without developer involvement. The heavy lifters—Azure OpenAI—enable organizations to develop their own sophisticated AI-driven applications, whether that’s intelligence embedded in customer service, intelligent knowledge retrieval, or advanced language processing at scale.

In the next sections, we’ll walk through concrete examples and dig into the impact—think improved turnaround times, happier customers, and fatter bottom lines. This practical perspective puts shiny feature lists in context and shows how each platform stacks up when it hits the business frontline.

Real-World Use Case Examples

1. Copilot for Automated Meeting Summaries:One financial services firm leveraged Copilot in Microsoft Teams to generate action item lists and discussion highlights from meeting transcripts. They drastically cut post-meeting admin time and improved project follow-up accuracy. The takeaway: Copilot excels at automating everyday productivity tasks in its native Microsoft environments.
2. AI Builder for Intelligent Document Processing:A global manufacturing company uses AI Builder’s form processing model to extract invoice details from scanned PDFs, pushing the data into Dynamics 365. This reduced manual entry errors and freed staff for higher-value work, with configuration accomplished by power users. Lesson learned: AI Builder is ideal for digitizing business processes without heavy development overhead.
3. Azure OpenAI for Custom Virtual Agents:A large retailer built a multi-lingual customer support chatbot on Azure OpenAI. This virtual agent understands complex customer intent, integrates with internal databases, and answers both basic and nuanced inquiries. Outcomes included 24/7 customer support and decreased resolution times. The flexibility of custom AI models gave them a true edge, but required skilled developers and model tuning.
4. Cross-Platform Workflow Automation:An insurance provider combined AI Builder automation (for forms and emails) with Azure OpenAI for deeper language understanding, connecting both to a legacy claims system via Power Automate. This hybrid setup demonstrates how organizations pick and mix tools for layered automation and smarter customer service, without being boxed in by a single platform.

Business Impact Analysis and ROI Considerations

Studies show organizations deploying Copilot in Microsoft 365 report up to 30% efficiency improvements in repetitive tasks, especially writing and data retrieval. AI Builder delivers ROI by empowering non-technical teams to automate processes, often reducing process cycle times by 50% or more.

For Azure OpenAI, the impact is driven by innovation and scalability. Gartner notes that enterprises building custom LLM-based applications see transformative gains in digital experiences and customer self-service, leading to higher CSAT and faster time-to-market—though ROI depends greatly on the complexity of implementation and ongoing management costs.

Evaluating Cost Considerations and Pricing Models

Microsoft Copilot generally follows a user-based licensing model, charged as an addon to your Microsoft 365 or Dynamics 365 subscription. Prices are predictable but can add up when scaling across large user bases.

AI Builder operates on capacity-based credits, where organizations purchase AI credits that cover certain model runs, predictions, or processing tasks. This pay-for-use approach means direct costs follow actual usage, but careful monitoring is needed to avoid surprise overages—particularly with high-volume automations or document handling.

Azure OpenAI charges based on API usage, with per-token or per-image pricing that can scale rapidly depending on application demands. While this unlocks flexible, high-scale deployments, it also introduces variable pricing—so estimating total cost requires analyzing expected traffic, data volumes, and planned model usage. Additional charges may accrue for storage, compute, or data egress on Azure.

Hidden costs can creep in from integration, training, and maintenance—especially with Azure OpenAI projects or customized Power Platform builds. Some companies manage costs by combining Power Automate for routine workflows (fixed pricing) with spot usage of Azure OpenAI for advanced cases, balancing predictability and innovation. A detailed cost assessment is essential up front so budgets don’t get blindsided down the road.

Comparing Ease of Use and Getting Started Essentials

Getting up and running with Copilot, AI Builder, or Azure OpenAI varies a lot in effort and expertise. For businesses, this can mean the difference between quick time-to-value and months of development just to see a return. So, it’s smart to know the learning curve before leaping in.

Copilot offers instant integration as soon as it’s enabled in your Microsoft 365 or Dynamics instance—no coding, setup, or backend work required. AI Builder leans on no-code, click-and-configure workflows, perfect for business users and analysts who want to automate tasks or build simple prediction models without calling IT into every meeting.

Azure OpenAI packs the most punch, but also demands the most upfront investment. Developers set up resources, configure authentication, and design custom prompts or integrate APIs—meaning the first mile can be steeper, especially for teams without AI or cloud backgrounds. The next sections will break down step-by-step onboarding tips for each: no one wants to run before they crawl.

Getting Started Guide for Each Platform

• Copilot:Enable Copilot licenses in Microsoft 365 admin settings or via Microsoft licensing portal.
• Assign licenses to user accounts and ensure users have compatible subscriptions (Microsoft 365 E3/E5 or Dynamics 365).
• Show end-users how to access Copilot functionality within each supported app—no installs required.
• AI Builder:Access AI Builder from inside the Power Apps or Power Automate interface.
• Choose a prebuilt model or start a new custom model using provided wizards and data connectors.
• Deploy and test the model in a Power App or automated flow, and monitor usage via Power Platform analytics.
• Azure OpenAI:Request and configure Azure OpenAI Service access from the Azure Portal (requires Azure subscription and review process).
• Deploy a resource, generate API keys, and review usage quotas.
• Build and integrate custom apps with your preferred SDKs (Python, .NET, etc.), using sample prompts and code provided by Microsoft.

Ease of Use Evaluation and Learning Curve

Copilot stands out for plug-and-play simplicity—if you can use Microsoft 365, you can use Copilot, and most users require minimal training. AI Builder lowers the barrier for business users and power users, delivering intuitive no-code interfaces and extensive documentation for self-service model building.

Azure OpenAI has a much steeper learning curve, best suited to professional developers, AI specialists, or large IT teams. Mastery involves understanding AI model selection, cloud setups, and security best practices. In all cases, Microsoft provides documentation, but hands-on trial and error is often required for Azure OpenAI to unlock its true value.

AI Agents, Conversational Capabilities, and Advanced Applications

There’s a world of difference in how Copilot, AI Builder, and Azure OpenAI approach AI agents, conversational bots, and complex AI-driven applications. For some businesses, it’s about smart virtual assistants giving users a leg up in productivity or automating support tickets. For others, it’s the lure of next-gen conversational AI that can adapt in real time and integrate with multiple business platforms.

Choosing the right platform often comes down to balancing out-of-the-box conversational intelligence, real-time recommendations, and the flexibility to build advanced virtual agents tailored for complex business scenarios. Copilot excels at providing conversational support within Microsoft 365 and Dynamics 365, while AI Builder lets users craft simple chatbots and workflow automations—no code needed. Azure OpenAI powers the most sophisticated agents, enabling multi-turn conversations, advanced context awareness, and integration with virtually any app or data source.

Of course, weaving AI agents into your business brings up questions around security, governance, and how much control you have over their behavior—a key theme in recent conversations about shadow IT and risk management. For deeper insights and best practices on safely deploying AI agents—especially within Microsoft 365—see practical governance strategies at AI agents and Shadow IT threats, as well as essential control plane architecture advice at Securing AI agents: Safe governance best practices. These resources detail the hidden risks and practical control strategies that every IT leader should know before deploying AI-driven automation at scale.

AI Agents Comparison and Real-Time Assistance Features

• Copilot: Delivers conversational AI features directly inside Microsoft 365 and Dynamics apps. Supports proactive recommendations, contextual answers, and summarization—no setup needed by end users.
• AI Builder: Enables quick creation of chatbots and workflow automations using prebuilt conversational templates. Works with Power Virtual Agents and integrates smoothly with Microsoft Teams for basic support scenarios.
• Azure OpenAI: Powers fully customizable conversational agents, supporting multi-turn interactions, deep intent recognition, and integration with third-party platforms using APIs. Best fit for enterprises needing advanced customization.
• Security & Governance: AI agents often run with broad permissions, making governance and security a critical concern. IT should consult best practices for mitigating shadow IT risk and safeguarding AI agent operations to prevent silent failures or compliance breaches.

Enabling Advanced AI Applications in Microsoft Environments

• Knowledge Mining with Copilot: Copilot helps users surface relevant info, summarize lengthy documents, and find actionable insights directly in everyday productivity apps.
• Process Automation via AI Builder: Automate routine business workflows using AI Builder’s low-code models—think invoice extraction, sentiment analysis, or intelligent email processing.
• Conversational AI with Azure OpenAI: Build advanced chatbots or virtual assistants capable of nuanced, multi-turn conversations, custom integrations, and real-time voice/text support.
• Hybrid Scenarios: Combine these platforms—Copilot for everyday end-user tasks, AI Builder for workflow automations, and Azure OpenAI for high-complexity requirements—to create layered, enterprise-ready AI solutions.

copilot studio and azure ai: choosing the right tool for build ai and productivity

What are the core differences between Copilot, AI Builder, and Azure OpenAI?

Copilot (including Microsoft 365 Copilot and Copilot Studio) is an AI assistant and low-code experience focused on productivity, natural language interactions, and integrating with Microsoft 365; AI Builder is a low-code AI tool in the Power Platform for business users to build models like prediction and form processing; Azure OpenAI Service exposes large language models (LLMs) and advanced AI capabilities for developers and data scientists to build custom applications, retrieval-augmented generation (RAG) scenarios, and scalable AI solutions. Use Azure when you need deep customization and Azure AI services; choose Copilot and Copilot Studio for embedded productivity and business user scenarios.

How do I decide between Microsoft 365 Copilot, Copilot Studio, and Azure OpenAI for my project?

Choosing the right tool depends on goals: Microsoft 365 Copilot is ideal if you want out-of-the-box natural language assistance within Microsoft 365 apps for business users; Copilot Studio and Microsoft Copilot Studio let you use low-code templates and agent builder features to create tailored copilots that integrate with existing Microsoft data; Azure OpenAI Service or Azure machine learning is the right choice for developers and data scientists who need full control, custom language model fine-tuning, RAG, and scalable AI apps. Consider integration needs, whether you want ai services embedded in workflows, and the level of developer involvement required.

Can Copilot Studio integrate with Azure AI Foundry and other azure ai solutions?

Yes. Copilot Studio can integrate with Azure AI Foundry and other Azure AI services to leverage advanced ai capabilities, azure machine learning models, and azure ai search for retrieval-augmented generation. This integration lets you build copilots that use enterprise data, scale on the Azure cloud, and combine low-code copilot studio’s low-code authoring with developer-oriented AI services for robust production systems.

What is retrieval-augmented generation and how does it relate to Azure OpenAI and Copilot?

Retrieval-augmented generation (RAG) combines retrieval (e.g., azure ai search or vector databases) with language model generation to provide accurate, context-aware responses. Azure OpenAI Service is commonly used for RAG by pairing LLMs with Azure AI search and knowledge stores; Copilot and Microsoft 365 Copilot use similar concepts behind the scenes to surface company-specific information in natural language, and Copilot Studio can be used to configure RAG workflows without heavy coding.

Who should use AI Builder versus leveraging Azure OpenAI Service or Azure Machine Learning?

Business users and citizen developers should use AI Builder for quickly building common AI scenarios (form processing, object detection, prediction) with minimal coding. Developers and data scientists should use Azure OpenAI Service or Azure machine learning when they require advanced ai development, custom language model training, scalable ai, or integration with complex data pipelines. AI Builder is optimized for accessibility and quick wins; Azure services are for deep customization and enterprise-grade ai solutions.

How does Microsoft 365 Copilot fit into enterprise productivity and existing microsoft tools?

Microsoft 365 Copilot integrates directly into Word, Excel, Outlook, Teams, and other Microsoft 365 apps to provide an ai assistant that enhances productivity through natural language prompts, draft generation, summarization, and data analysis. It leverages existing microsoft data and security controls, making adoption simpler for organizations already using Microsoft 365, and it can be extended or complemented by Copilot Studio and Azure AI solutions for richer, domain-specific capabilities.

Is it possible to use Azure OpenAI and Azure Machine Learning to build a custom copilot for my company?

Yes. Developers and data scientists can leverage Azure OpenAI Service for LLMs, Azure machine learning for model lifecycle management, and azure ai search for knowledge retrieval to build a custom copilot. Using these azure ai solutions you can implement RAG, fine-tune models, deploy scalable ai apps in the azure cloud, and integrate with enterprise systems for a tailored ai assistant that meets compliance and performance requirements.

How do costs, scalability, and compliance compare across Copilot, AI Builder, and Azure OpenAI?

Costs and scalability vary: Microsoft 365 Copilot and Copilot Studio often come as subscription or tenant-level services optimized for business users, while AI Builder pricing targets Power Platform projects. Azure OpenAI and Azure machine learning are pay-as-you-go cloud services that scale with usage but require careful cost management for large-scale model inference and storage. For compliance and governance, Azure services provide granular controls and enterprise-grade security; Microsoft 365 Copilot benefits from Microsoft’s integrated compliance posture for M365 environments. Choose based on budget, expected scale, and regulatory needs.

What skills and roles are needed to adopt Copilot and Azure AI solutions effectively?

Adoption typically involves a mix of business users, IT administrators, developers, and data scientists. Business users and analysts can drive use cases with Microsoft 365 Copilot and AI Builder; developers and data scientists will handle azure ai foundry, Azure OpenAI Service, and azure machine learning for advanced ai development. Collaboration across roles helps ensure you leverage microsoft offers, integrate existing microsoft systems, and build scalable ai apps while maintaining governance and security.